esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Overall Workflow  
Figures 1 and 2 show the overall workflows of the developments of the DLS model and automated prediction of CTRs, respectively. The dense V-Net was trained with the training dataset, and the trained model was evaluated with internal test dataset A by using the DSC. Whole tumor regions were predicted for internal test dataset B and the external test dataset. Then, a thresholding technique with a clinical window level and width was applied to the CT images within the predicted whole tumor regions to obtain solid components. Finally, the maximum diameters for both the whole tumor and the part-solid regions were measured by using the Feret method. The CTR for each part-solid tumor was predicted as a ratio of the maximum diameter of the largest solid component to that of the whole tumor. The automated prediction approach of CTRs was assessed with the Pearson correlation coefficient (PCC) [25] and the intraclass correlation coefficient (ICC) [26]. The prognostic power of predicted CTRs was evaluated with \(p\)-values in a KM analysis.  
Figure 1: The overall workflow for a deep learning (dense V-Net) segmentation (DLS) model of lung cancers.
## 5 Empirical Validation: A Case Study
### Resilience Assessment Results and Analysis  
The assessment yielded a holistic, quantitative view of the system's resilience posture, with an overall Resilience Capacity (RC) score of 82.75. This score is derived from individual scores in four dimensions, as shown in Figure 2 and Table 1, revealing highly unbalanced resilience characteristics:  
* Preventive Capacity (P): 90/100. The system demonstrated strong capabilities in identifying its 10 critical business services and 32 key assets. It had automated threat warning mechanisms in place. However, the assessment also uncovered risks, including security blind spots in 10% of its infrastructure, incomplete API logging, and the use of outdated third-party components with known vulnerabilities.
* Destruction Resistance (R): 91/100. This was the system's strongest dimension. The attack surface was well-managed, with only 10 out of 113 ports (9%) exposed to the internet. The system also had capabilities for threat intelligence collection and analysis. The primary weakness identified was that 5% of the cloud application's security and access logs were not completely recorded, potentially impeding real-time attack analysis.
* Adaptive Recovery (A): 66/100. The framework identified a critical deficiency in this dimension. The assessment found that the system lacked defined processes for analyzing and responding to security incidents, had no effective isolation between its modular components to contain damage, and possessed no redundancy, backup, or self-recovery capabilities. This low score highlighted a significant risk of prolonged downtime and data loss in the event of a successful attack.
* Learning Evolution (L): 85/100. The system showed a good capacity for evolution, with some methods for upgrading functional components. However, it lacked comprehensive threat simulation, assessment, and attack traceability mechanisms. A keysystemic risk was its reliance on an end-of-life operating system (CentOS), for which the cloud provider did not offer an automated upgrade path, severely limiting the system's long-term adaptability and exposing it to unpatched vulnerabilities.
## 2 Mathematical Model
### Computational Geometry, Domain, and Grid  
The airflow within about a 450-m length, 200-m width, and 50-m height range outside the tunnel portals was adopted to simulate the pollutant diffusion. The tunnel section resembled the shape of a horseshoe. The two-lane and three-lane tunnels were considered separately, and the hydraulic diameters \(D\) were 6.1 and 8.6 m, respectively. In reference to a certain tunnel project, the front slope gradient of the tunnel portal was \(15^{\circ}\). The lateral distance of inlet and outlet \(d\) was set within the range of 5-20 m at 2.5-m intervals. The staggered distance \(\Delta l\) was from \(-10\) to 320 m at 5 m intervals. If \(\Delta l<0\), the inlet would be positioned behind the outlet as shown in Figure 1a; if \(\Delta l=0\), the outlet and inlet would be aligned as shown in Figure 1b; if \(\Delta l>0\), the inlet would be positioned in front of the outlet as shown in Figure 1c.  
The whole domain was meshed by nonuniform hexahedral grids using the professional mesh generation software ICEM-CFD17.2 (ANSYS Inc. Pittsburgh, PA, USA, 2016), and the areas near the  
Figure 1: Sketch map of closely spaced twin tunnels with staggered inlet and outlet.  
inlet and outlet were locally refined to ensure calculation efficiency, accuracy, and reliability. Figure 2 shows the grid map of a three-lane tunnel with a lateral distance \(d=10\) m.
# Hardware-in-Loop Modules for Testing Automated Ventilator Controllers
## 1 Introduction
They have been able to predict individual patient changes in response to ventilator settings as well as gas/aerosol substance transport [21; 22; 23]. However, these models still require extensive, highly specialized datasets from clinical trials for training [22]. Some models enable communication with external sensors and devices, but the outputs of the physical devices do not directly impact the sensor readings [24]. The ventilator settings or measurements of the output are commonly used to estimate a change in the physiological variables, which are then simulated and streamed to the sensors. Previous benchtop models for simulating physiological data have been developed for testing patient care during a coma [25], the testing of cerebrospinal fluid shunt systems [26], and the testing of fluid resuscitation strategies for patients experiencing hemorrhagic shock [27]. There have been several HIL systems previously developed for mechanical ventilation. Simulated lungs that mimic specific respiratory dynamics have been used for controllers that manage positive end-expiratory pressure (PEEP) [28], and in silico simulations have been designed for modeling the interactions between mechanical ventilators and human lungs [29]. There have also been complex lung simulators developed, like the xPULM simulator developedby [PERSON] et al. Their system combines in silico, ex vivo, and mechanical components into one physical platform to capture flow and pressure changes at differing respiratory rates (RRs) and tidal volumes (V\({}_{\rm T}\)) [30]. Another group developed a physical lung model with adjustable mechanical properties that was able to mimic a normal lung, an obstructive lung, and restrictive lung diseases [31]. However, these models primarily focus on physically reproducing the pressure and volume mechanics of ventilation and--except for [PERSON] et al.--still use in silico simulations for CO\({}_{2}\) generation and/or end-tidal CO\({}_{2}\) (EtCO\({}_{2}\)) waveforms. Additionally, none of these models account for O\({}_{2}\) delivery, which is one of the primary purposes of mechanical ventilation. More recent HIL testbeds that have sought to incorporate a measurement of O\({}_{2}\) delivery have done so by introducing an SpO\({}_{2}\) simulator that interacts with the testbed [24]. The work described here presents novel oxygenation and CO\({}_{2}\) ventilation HIL testing setups for the development and testing of automated ventilation controllers. In summary, this work provided the following contributions:  
* The development and characterization of a HIL ventilation system for oxygenation
* The development and characterization of a HIL system for CO\({}_{2}\) ventilation using commercial ventilator technology
* Proof-of-concept closed-loop ventilator controller evaluation using HIL systems and physiological scenarios to highlight the utility of the ventilator test platform
Energies_**2014**, \(7\), 2377-2420; doi:10.3390/en7042377  
_reviews_  
**ISSN 1996-1073**  
www.mdpi.com/journal/energies  
_Review_  
**Strategies for Power Line Communications Smart Metering Network Deployment**  
**[PERSON] 1,*, [PERSON] 2 and [PERSON] 2**  
1 Division of Control Systems and Telecommunications, Iberdrola, Av. San Adrian 48, 48003 Bilbao, Spain  
2 Department of Communication Engineering, Bilbao Faculty of Engineering,  
University of the Basque Country (UPV/EHU), Alda. Urkijo S/N, 48013 Bilbao, Spain;  
E-Mails: [EMAIL_ADDRESS] (I.P.); [EMAIL_ADDRESS] (P.A.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +34-94-4664787; Fax: +34-94-4664485.  
_Received: 7 February 2014; in revised form: 21 March 2014 / Accepted: 24 March 2014 / Published: 15 April 2014_  
**Abstract:** Smart Grids are becoming a reality all over the world. Nowadays, the research efforts for the introduction and deployment of these grids are mainly focused on the development of the field of Smart Metering. This emerging application requires the use of technologies to access the significant number of points of supply (PoS) existing in the grid, covering the Low Voltage (LV) segment with the lowest possible costs. Power Line Communications (PLC) have been extensively used in electricity grids for a variety of purposes and, of late, have been the focus of renewed interest. PLC are really well suited for quick and inexpensive pervasive deployments. However, no LV grid is the same in any electricity company (utility), and the particularities of each grid evolution, architecture, circumstances and materials, makes it a challenge to deploy Smart Metering networks with PLC technologies, with the Smart Grid as an ultimate goal. This paper covers the evolution of Smart Metering networks, together with the evolution of PLC technologies until both worlds have converged to project PLC-enabled Smart Metering networks towards Smart Grid. This paper develops guidelines over a set of strategic aspects of PLC Smart Metering network deployment based on the knowledge gathered on real field; and introduces the future challenges of these networks in their evolution towards the Smart Grid.  
keywords: smart metering; smart grid; PLC; broadband (BB); narrowband (NB); ultra-narrowband (UNB); strategy; deployment; distribution grid +
Footnote †: journal: Journal of the American Statistical Association
# An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
## 5 Performance Evaluation  
Our scheme is deployed on a Samsung Note2 (Seoul, Korea) with an Android system. Several ordinary digital loudspeakers are chosen to be the acoustic sources. We set the speakers in various indoor environments as anchor points. The acoustic signals' frequencies in the experiments are selected to be 19,500 Hz to 21,500 Hz. In practice, we can set the acoustic frequency to be higher than 20,000 Hz. This kind of signal cannot be heard by human beings, which has the minimal impact to users. High frequency acoustic signals are relatively seldom in the daily life unlike WiFi signals or light signals. The high frequency acoustic signals are less likely to be affected by other signals in the environment.
## 3 Model Development
### Control Model
The ANN model structure is shown in Figure 4c. The ANN model is trained with 70% of the parameter set as the training set, 15% as the test set, and 15% as the validation set. The ANN training process uses forward transmission and backpropagation to minimize the mean square error between the actual output value and the expected output value. The trained ANN is then applied to control the thermal management system. The results show that the model can predict the optimal PID parameters according to the current operating condition through the trained ANN, realizing the accurate control of the coolant inlet temperature under different load currents and preset stack temperatures.
# Multiscale Modeling for Application-Oriented Optimization of Resistive Random-Access Memory
## Materials and Methods  
As highlighted in Section 1, the design of memristive devices requires complete knowledge of all the involved resistance switching phenomena, including the effects of the device geometry and materials.  
In RRAMs, the fundamental mechanisms include (i) the interaction between the electronic and ionic transport, (ii) the effects induced by the applied electric field, and (iii) the influence of the microscopic material properties [41].  
Due to the complexity of those mechanisms, exacerbated by their interplay, the physical modeling of RRAMs presented in this work is extremely advantageous, as it easily allows the following: (i) prediction of the device performance (i.e., switching time, endurance, and retention) from the material properties (which is one of the key novel aspects of this work, which allows the materials and the process conditions to be directly screened when targeting specific applications); (ii) investigation of the trade-off between the device scaling and variability; (iii) evaluation of the process effects on the device and its materials through the interpretation of electrical characterization data; and (iv) co-design of the device materials and geometries for satisfying the specific application requirements (see Table 1).  
In this section, we first review the physical mechanism underlying the RRAM operation, and then present a multiscale modeling platform that includes all the presented effects and mechanisms.
## 2 Materials and Methods
### Methods  
The preparation of the zinc-containing nanoparticle (i.e., **NP-Zn**) for diaryl carbonate hydrolysis is shown in Scheme 1. Surfactant **3**[37], diazide surface-cross-linker **4**[37], monoazide surface ligand **5**[37], template **T**[38], and the zinc-containing functional monomer **FM**[39] were synthesized following previously reported procedures.  
Compound **1a** has been used as a model substrate for BPA-PC in the de novo design of polycarbonate hydrolases [16], partly because its hydrolysis can be monitored readily by UV-vis spectroscopy. We decided to examine a number of diaryl carbonates (**1b-f**) in addition to **1a** (Scheme 1), some of which were used to obtain the Hammett plot for the mechanistic investigation (vide infra). To create a molecularly imprinted site for **1a**, we prepared 1,3-bis(4-nitrophenyl)thiourea as the template (**T**). It is dimensionally similar to **1a**, while its sulfur atom serves as a good ligand for the zinc atom in the tridentate zinc complex **FM**. In this way, the zinc atom would be close to the carbonyl of the bound substrate in the final catalyst. We employed zinc as the catalytic cofactor because it is used to catalyze ester hydrolysis by both natural hydrolases [40; 41] and artificial enzymes [42; 43; 44; 45; 46; 47].  
For the preparation, a 1:1 mixture of **T** and **FM** is first solubilized in water by the micelles of **3**. The micelles also contain divinylbenzene (DVB) as a radical cross-linker and 2,2-dimethoxyoxy-2-phenylacetophenone (DMPA) as a photoinitiator. As in other molecularly  
Figure 1: Preparation of nanozyme **NP-Zn** via molecular imprinting of **T–FM** complex within the cross-linkable micelle of **3**. The clicked surface ligands **5** are omitted from the drawing for clarity.  
imprinted nanoparticle catalysts [20; 21; 22], we first cross-link the surface of the micelles by the Cu-catalyzed cycloaddition between the terminal alkynes on the surfactant headgroups and water-soluble diazide **4** (Scheme 1, step a). We then use the same click reaction to decorate the surface-cross-linked micelles (SCMs) with monoazide **5** (step b). Photoinduced radical polymerization/cross-linking subsequently solidifies the micelle core around the **T-FM** complex, covalently attaching the zinc-containing **FM** to the micelle core at the same time (step c). The polyol ligand **5** on the surface of the resulting **NP-Zn** makes it poorly soluble in most organic solvents. The molecularly imprinted nanoparticles can thus be purified by simply precipitation into acetone and solvent washing, with the template removed at the same time (step d). Nonimprinted nanoparticles (NINPs) are also prepared in the absence of the template as the control catalyst.  
Our nanoparticles are estimated to contain ~50 surfactants per (cross-linked) micelle. Since a 50:1 surfactant/template ratio is used in the preparation, each nanoparticle, on average, contains a single binding site, a feature previously confirmed by isothermal titration calorimetry (ITC) [37].
# A Review of Passive Constant Flow Regulators for Microfluidic Applications
## 4 Non-Mechanical Passive Constant Flow Regulators  
An original microfluidic passive flow regulator that can function at low Re without any moving parts was developed by [PERSON]'s group at Caltech in 2003 [13; 70]. The nonlinearity required, as discussed in Section 2, for a passive constant flow regulator, derived from the non-Newtonian elastic properties of the working fluid, a 250 ppm by weight aqueous solution of a high molecular weight polyacrylamide. Two different designs of nonlinear channels were fabricated in PDMS using soft lithography [17; 18]. The first channel is a chain of 46 identical halves of broad ellipsoidal rings (with inner half axes of 185 and 65 \(\upmu\)m and outer half axes of 410 and 290 \(\upmu\)m) interconnected by a narrow bottleneck (\(\approx\) 30 \(\upmu\)m), while the second channel is a chain of 43 identical triangular segments (about 203 by 330 \(\upmu\)m) interconnected by a bottleneck contraction of about 37 \(\upmu\)m. The viscoelastic solution is therefore forced to flow through a sequence of expansions and contractions. The polymer molecules become extended along the flow direction when the working fluid flows through a contraction. If the extension rate is large enough compared to 1/\(\lambda\) (where \(\lambda\) is the relaxation time of the polymer solution), the molecules can unravel and generate nonlinearity with an increase of the apparent viscosity of the solution [71]. Both devices behave as a three-stage valve with two inflection points in the \(Q(\Delta p)\) characteristics. The first device showed a linear increase in the flow rate with the pressure until a transition when the flow rate reaches 15 nL/s. Constant flow rates of 18.7 \(\pm\) 0.6 nL/s for a \(\Delta p\) per segment in the range \([0.5;1]\) mbar and 20 \(\pm\) 5 nL/s for a \(\Delta p\) per segment in the range \([0.22;2.2]\) mbar are reported [13]. At higher pressure, the differential flow resistance d\(P\)/d\(Q\) decreases. The second device exhibited a flow transition at 17 nL/s but the nonlinear increase of the resistance did not yield a flow stabilization when the pressure gradient per segment became larger than 0.07 mbar [70]. At low \(Re\) (\(\ll 1\)), the Navier-Stokes equations become linear and flow is expected to be completely reversible [72]. Therefore, the channel  
Figure 13: Schematic top view of a planar PDMS microfluidic flow regulator. The flow direction is indicated by red arrows. The flow restriction between the flap narrows with the applied pressure to maintain the flow rate constant.  
shall exhibit the same hydraulic resistance for both flow directions. [PERSON] demonstrated with the second device a diodicity of about 2, which was a signature, in absence of significant inertial effects, of a flow rectification due to nonlinear elastic properties of the working fluid [70]. Such devices may be integrated into a drug delivery system if a biocompatible polymer solution is used.
## 4 Experimental Results
### Evaluation of ROI Extraction Module
#### 4.2.2 Testing Phase  
In this phase, we applied the remaining images to our network as the test set. The results of the testing phase, illustrated in Figure 7, show that REM performed very well on the test set.  
Figure 5: Examples of predicted ROIs for different epochs.  
Figure 6: Palmprint localization cost versus epochs.  
Figure 7: Examples of extracted palmprint ROIs in the testing phase.  
To investigate the performance of the proposed REM, we used the IoU metric [65], which measures the overlap between the predicted ROI and ground truth ROI or the target region. Considering ground-truth ROIs as \(R_{g}\) and the corresponding predicted ROIs by our model as \(R_{p}\), the IoU can be calculated by the Formula (4):  
\[\text{IoU}=\frac{\text{area}(R_{g}\cap R_{p})}{\text{area}(R_{g}\cup R_{p})} \tag{4}\]  
Figure 8 presents a visual sample of the ground-truth ROI versus the predicted ones by our model. It should be noted that ground truth ROIs are the ones firstly produced by bounding boxes in our own works. The final IoU score is obtained by the mean taken over all classes, and was 93% in our experiment.  
In order to validate the effectiveness of our proposed ROI extraction model, we compared it to the Han model [16], which performs well even under unconstrained scenes.  
Figure 9 shows the learned features for the palmprint localization task in our model and the Han method. The Han method selects Haar features in order to represent the shape features of the hand, while our model extracts discriminative features automatically that are easier to learn than the complicated Haar features. In our ROI extraction module, the first convolutional layer extracts basic features like edges and lines, and other convolutional layers represent high-level features automatically.  
Figure 8: A visual sample of the intersection over union (IoU) metric in our proposed model.  
Figure 9: Comparison of learned features in our ROI extraction model with the Han method [16].  
As depicted in Figure 10, our proposed model could effectively extract the palmprint ROI using deep features in comparison to the Han method, especially in segmenting the background from the palmprint and keeping the important regions of the palm.
# Increasing Photovoltaic Systems Efficiency Through the Implementation of Statistical Methods
## 2 Literature Review
### Assessment of PV Power Plants' Operating Conditions  
The final product of a power plant is the electric power supplied to the grid at 50 Hz and various voltage levels, depending on the power plant's structure. For photovoltaic plants, the voltage level varies depending on the injection points in the system, which include 11 kV, 33 kV, 110 kV, and 400 kV. Electricity is a product; like any product, it must meet its quality requirements. They are specified in the IEC 61000-4-30:2015+A1:2021 and EN 50160 standards [31; 32]. A PV system that does not comply with these standards will generate problems for the transmission and distribution grids, potentially risking disconnection from the power system. These interruptions can affect the efficiency of photovoltaic plants, decreasing the installed power/electricity produced ratio.