esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This study investigated the effect of the hydrophilicity in activated carbon electrodes on the desalination performance in MCDI. Our experiments employed P60 and YS-2 activated carbons, which have similar specific surface areas, but different hydrophilicities due to the C-N or C=N bonding. The results demonstrated that hydrophilic YS-2 electrode have larger SAC, indicating a positive effect of hydrophilicity on the desalination performance. After PDA modification to enhance the hydrophilicity of the electrode and/or IEM, the SAC of the P60 electrode effectively increased, resulting from a dramatically improved hydrophilicity. Conversely, PDA-modified IEM reduced the desalination performance in the MCDI by inhibiting ion transportation by acting as an insulator without changing the hydrophilicity. These results provide the effects of hydrophilicity of activated carbon electrodes  
Figure 5: Capacitive deionization (CDI) ragone plot using pristine and PDA-modified P60 and YS-2 electrodes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Activated Carbon Electrode**} & \multicolumn{3}{c}{**Salt Adsorption Capacity (mg g\({}^{-1}\))**} \\ \cline{2-4}  & **EPDA + IEMp 1** & **E\({}_{\text{P}}\) + IEMpDA 2** & **EPDA + IEMpDA 3** \\ \hline P60 & 12.9 \(\pm\) 0.4 & 10.2 \(\pm\) 0.6 & 13.8 \(\pm\) 0.2 \\ YS-2 & 13.2 \(\pm\) 0.8 & 12.0 \(\pm\) 0.4 & 13.0 \(\pm\) 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of salt adsorption capacity (SAC) for three MCDI configurations.  
on the desalination performance in MCDI and require further comparable study on the effects of hydrophilic electrode when treating high-salinity water or using high electric field and so on.  
The following are available online at [[http://www.mdpi.com/2076-3417/9/23/5055/s1](http://www.mdpi.com/2076-3417/9/23/5055/s1)]([http://www.mdpi.com/2076-3417/9/23/5055/s1](http://www.mdpi.com/2076-3417/9/23/5055/s1)), Figure S1: Salt adsorption capacity (SAC) of P60 and YS-2 electrodes in CDI, Figure S2: Contact angle image of ion-exchange membrane before/after polydopamine (PDA) modification, Figure S3: Salt adsorption capacity of PDA-modified P60 electrode and YS-2 electrode.  
Conceptualization, data acquisition and interpretation, and writing--original draft preparation and editing, [PERSON]; conceptualization, data interpretation, and writing--review and editing, [PERSON]; writing--review, [PERSON]; supervision, writing--review, [PERSON].  
This work was supported by the Technology Innovation Program (10082572, Development of Low Energy Desalination Water Treatment Engineering Package System for Industrial Recycle Water Production) funded by the Ministry of Trade, Industry, and Energy (MOTIE, Korea), by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science, ICT and Future Planning (NRF-2018R1C1B5086300) and by the Sungshin Women's University Research Grant of 2018-2-85-003.  
The authors declare no conflict of interest.
## 2 Background
### Using a CMOS Sensor in VLC
#### 2.1.2 Camera Requirement  
The camera has many performance indicators that affect the images they capture. When a camera is used as a receiver in the VLC system, the two most important characteristics are exposure time and International Organization for Standardization (ISO).  
* Exposure Time  
The exposure time is a time horizon that the camera shutter is open for to allow light into the photodiode matrix. The exposure duration determines how long a pixel collects photons. Pixels in the light irradiation accumulate charge until saturation.  
LED is not only used as a transmitter, but also in many cases as a light source in the indoor environment, so the intensity of the LED is very high and the charge of the pixels is saturated in a short time. When the pixel charge is saturated, the charge will spill over to the nearest pixel if it continues  
Figure 1: Sketch maps of the (a) global shutter of the CCD sensor, and (b) rolling shutter of the CMOS sensor.  
to irradiate, resulting in the accumulation and saturation of the charge in the near pixel. Therefore, if the exposure time is too long, the width of the bright stripe will increase, and the reception will be affected. Especially when we use RGB-LED, this effect is more obvious. Incorrect doping of color will greatly increase the error of detection and recognition.  
* ISO  
In digital cameras, ISO indicates the speed of photosensitivity of the CCD or CMOS photosensitive elements. The higher the ISO value, the stronger the photosensitive ability of the photosensitive component. The higher the ISO, the fewer photons that a pixel needs to reach saturation. This means that the probability of the saturation pixel in the same exposure time increases, resulting in the width of the bright stripe in the captured image not normally increasing such that recognition rate will be reduced.
## 4 Conclusions
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Maximum Permissible**} & & \\
**Parameter** & **Unit** & **Concentration According to** & **SP-26** & **Min** & **Max** & **Average** \\ \hline Oxidizability & mg/L & 2 (5) & 0.4 & 2.5 & 1.0 \\ \hline NH\({}_{3}\) & mg/L & 2.0 & 0 & 0.63 & 0.05 \\ \hline NO\({}_{2}\) & mg/L & 3.0 & 0 & 0.09 & 0.009 \\ \hline NO\({}_{3}\) & mg/L & 45 & 0 & 10.4 & 1.6 \\ \hline Total hardness & mg-eq/L & 7.0 (10) & 0.8 & 8.25 & 3.7 \\ \hline TDS & mg/L & 1000 (1500) & 105 & 863 & 456.8 \\ \hline Cl\({}^{-}\) & mg/L & 350 & 4.6 & 270 & 71.8 \\ \hline SO\({}_{4}\)\({}^{2-}\) & mg/L & 500 & 10 & 241 & 102.5 \\ \hline Fe & mg/L & 0.3 (1.0) & 0 & 0.9 & 0.15 \\ \hline Cu & mg/L & 1.0 & 0 & 0.5 & 0.03 \\ \hline Cd & mg/L & 0.001 & 0 & 0.0004 & 0.0001 \\ \hline Zn & mg/L & 5.0 & 0 & 0.12 & 0.04 \\ \hline Pb & mg/L & 0.03 & 0 & 0.021 & 0.0045 \\ \hline Mo & mg/L & 0.25 & 0 & 0 & 0 \\ \hline Be & mg/L & 0.0002 & 0 & 0 & 0 \\ \hline Mn & mg/L & 0.1 (0.5) & 0 & 0.09 & 0.02 \\ \hline B & mg/L & 0.5 & 0 & 0.303 & 0.1 \\ \hline Se & mg/L & 0.05 & 0 & 0.46 & 0.09 \\ \hline Cr & mg/L & 0.05 & 0 & 0 & 0 \\ \hline F & mg/L & 1.5 & 0.02 & 1.45 & 0.5 \\ \hline Ni & mg/L & 0.1 & 0 & 0.04 & 0.008 \\ \hline Co & mg/L & 0.1 & 0 & 0.15 & 0.025 \\ \hline Sb & mg/L & 0.05 & 0 & 0 & 0 \\ \hline As & mg/L & 0.05 & 0 & 0.01 & 0.0003 \\ \hline Polyphosphates & mg/L & 3.5 & 0 & 0 & 0 \\ \hline Ba & mg/L & 0.1 & 0 & 0 & 0 \\ \hline Petroleum products & mg/L & 0.1 & 0 & 0.08 & 0.04 \\ \hline Al & mg/L & 0.5 & 0 & 0 & 0 \\ \hline Hg & mg/L & - & 0 & 0 & 0 \\ \hline DDT & mg/L & 0.002 & 0 & 0.002 & 0 \\ \hline HCH-\(\gamma\) (indane) & mg/L & 0.002 & 0 & 0.002 & 0 \\ \hline
2,4 D-salt & mg/L & 0.03 & 0 & 0.001 & 0 \\ \hline Total alpha (\(\alpha\)) radioactivity & K \(-\) 1 & 0.2 & 0 & 0.186 & 0.033 \\ \hline Total beta (\(\beta\)) radioactivity & K \(-\) 1 & 1.0 & 0.01 & 0.647 & 0.099 \\ \hline Total viable count (TVC) & cfu/cm\({}^{3}\) & Less than 50 & 0 & 20 & 5.6 \\ \hline Total coliform (TCB) & 100 per 100 ml & Absence & 0 & 0 & 0 \\ \hline Thermotolerant coliform bacteria (TCB) & 100 per 100 mL & Absence & 0 & 0 & 0 \\ \hline \hline \end{tabular}
## 4 Routing Layer
### Security at the Routing Layer  
There are several attacks that can be detrimental to routing protocols like sinkhole attacks, Sybil attacks, selective forwarding attacks or wormhole attacks. In [125] a taxonomy of different vulnerabilities, attacks and countermeasures for UWSN is provided.  
In a sinkhole attack a malicious node tries to deviate packets towards it. The selective forwarding attack consist on malicious nodes dropping selected messages in order to sabotage routing. Sybil attacks are based on a device taking multiple identities. All this attacks can be mitigated by using authentication. In [126] authors purpose a security suite at the communication middleware level based on the group communication paradigm. The nodes that form a group share a key and use it to encrypt and authenticate messages. To ensure confidentiality messages are encrypted using AES and authenticity is achieved by truncating the real hash function value (SHA-256) to a 4 bytes digest hence, reducing the total overhead to the expenses of reducing security. When a node is believed to be compromised it is forced to leave the group and a new key is then distributed using the S2 RP protocol [127]. Furthermore, in a subsequent work [95], the authors apply these same ideas to the routing layer.  
In a wormhole attack, a malicious node transmits packets received in one end of the network over a low latency link to another end causing false neighbor relationships, thus affecting routing. Countermeasures against the wormhole attack for UWSN are presented in [128] where every sensor collects its distance to its neighbors and broadcasts it. By doing so, every node is able to construct the local topology and wormholes detected.
# Evaluating the Effect of Intensity Standardisation
## 3 Results
### Scan-Rescan Repeatability
First, the application of histogram matching resulted in more similar estimates for both Alzheimer's disease patients and normal control subjects, even more similar than those detected without standardisation. This situation may be reflective of a potential under-detection of the actual atrophy due to information loss (e.g., contrast loss) after the application of histogram matching. Second, standardising intensities with z-score and WhiteStripe before registering baseline and follow-up scans resulted in lower effect sizes compared  
Figure 4: Scan–rescan deviation with and without intensity standardisation. We equipped FSL-SIENA with intensity standardisation before or after registration. We expressed the error as the percentage of brain volume change between scans acquired during the same visit and using the same scanner and acquisition protocol. We used the Wilcoxon signed-rank test to examine whether the median error obtained after standardisation differed from that obtained without standardisation (\(p\)-values on top). FCM: Fuzzy c-means. GMM: Gaussian mixture model. KDE: Kernel density estimation. HM: Histogram matching. WS: WhiteStripe. to those obtained when applying them afterwards (z-score: 0.39 vs. 0.45 in OASIS and 0.27 vs. 0.45 in ADNI; WhiteStripe: 0.37 vs. 0.44 in OASIS and 0.02 vs. 0.46 in ADNI). After inspecting the registration outputs, we noticed that standardising with these two techniques prior to registration led to unsatisfactory outcomes in numerous cases (ADNI: 63 with WhiteStripe and 26 with z-score; OASIS: 24 with WhiteStripe and 44 with z-score). Some examples of this issue are shown in Figure 5. On the other hand, the application of these two techniques after registration led to some of the highest Cohen's \(d\) values in both datasets. Figure 5: Examples in which registration failed due to the application of WhiteStripe and z-score before registration. The cases correspond to “ADNI_005\(S\)0814” and “ADNI_098\(S\)0149” for which brain volume change was estimated around 2.89% and \(-\)5.02%, respectively.
## 2 Related Work
The idea of entropy-based spectrum sensing was proposed by [PERSON] [16], in which the information entropy is estimated from the output of a matched filter. The entropy of the received signal is reduced if it contains a modulated PU signal. However, it requires perfect knowledge of the PU waveform and synchronization. The received signal is transformed in the frequency domain, and the spectrum magnitude is regarded as a random variable [17]. The probability space is partitioned into fixed dimensions, and the Shannon entropy is calculated as an information measure of the received PU signal for test statistics. It is demonstrated that under a fixed-dimension probability space, the entropy is independent of noise power. The authors concluded that their proposed scheme has better performance and is more robust against noise uncertainty in comparison with energy detectors and cyclostationary detectors [17]. [PERSON] _et al._ proposed a spectrum sensing scheme based on the entropy estimation of the cyclostationary features of the received PU [18]. To improve the spectrum sensing performance, the proposed scheme was extended to centralized spectrum sensing using soft and hard decision fusion rules. The results revealed that the proposed scheme outperforms the cyclostationary detection based on the spectral coherence function (SCF) and energy detection. A spectral entropy-based PU detection scheme performs better than energy detection without any prior knowledge of the PU waveform [19]. It is highlighted that the optimal threshold and the optimal number of samples should be studied for entropy-based spectrum sensing. [PERSON] _et al._[20] proposed a frequency-domain entropy-based spectrum sensing scheme for CR, which proved to be independently robust against noise uncertainty, as the entropy of noise is constant. It is concluded that the sensing time is reduced to about 75% in comparison with the energy detector under the same detection performance. Moreover, the results proved that the frequency-domain entropy-based detector outperforms the time-domain entropy-based detector. All the existing entropy-based detectors do well under low SNR conditions, but none of them has considered sea state distribution. MCRNs should be intelligent enough to switch their operating parameters to suit the sea state, geographic location/region and communication range in order to achieve better throughput and quality of service (QoS). UHF is used in the USA for maritime navigation services, and therefore, it is important to protect the primary band. To achieve this, a high probability of detection and a low probability of false alarms are required for precise detection of PU and for efficient utilization of bandwidth, respectively.
# NetAP: Adaptive Polling Technique for Network Packet Processing in Virtualized Environments
## 2 Background and Motivation  
### VirtIO  
VirtIO modules are para-virtualized drivers for virtualized systems based on KVM; such modules are widely used for network and block devices in KVM. Figure 1 shows the structure of VirtIO with request and response flows. When a user application running in a guest OS requests a packet transmission via a system call, the network stack of the guest OS prepares a packet for transmission and delivers the packet to the VirtIO front-end device driver, which is a virtual network device. The front-end driver uses shared memory for data communication between the VM and the host; the network packets are buffered into the queue in the shared memory region. A vIRQ is then generated in order to notify the host OS that packets are ready for transmission. The vIRQ is generated when the number of packets in the queue exceeds a certain threshold, rather than generating a vIRQ for each packet.  
Upon receiving the vIRQ, the host OS schedules the virtual host (vHost) user thread to deliver the packet fetched from the shared queue to the network stack of the host in kernel space. The vHost thread adopts a combined interrupt-polling method, which combines the interrupt and polling mechanisms. After the first vIRQ interrupt, the vHost thread fetches consecutive packets from the shared queue with a polling mechanism and then delivers them to the network stack. The fetched packets are sent to the MacVTaP bridge, which is a virtual interface that transmits packets through the physical device. Packet reception is similarly performed in the opposite direction.  
The main overhead occurs in the process of requesting packets from the VM to the host. The VM notifies the host system, using a vIRQ, that the packet to be processed is ready in the shared memory. The host system then receives the interrupt and wakes up the sleeping vHost thread to perform packet processing. This leads to frequent VM-Exit and VM-Entry operations to transfer the control between the VM and KVM, which leads to a substantial virtualization overhead [5; 6].
# A New Maximum Likelihood Estimator Formulated in Pole-Residue Modal Model
## Appendix B Derivation of the Reduced Normal Equations  
Since the Jacobian matrix as in Equation (14) normally contains a huge amount of elements, the computation of the inverse of the normal equations as in (17) slows down the estimation process with the MLE-PMM. Furthermore, the numerical stability of the Gauss-Newton algorithm can be compromised depending on the conditioning of this matrix. In order to avoid these issues, the reduced normal equations can be computed instead. The derivation of the reduced normal equations for the MLE-PMM described in Section 2.2 starts by rewriting Equation (17) in a more compact form, as  
\[\begin{bmatrix}R&S\\ S^{T}&T\end{bmatrix}\begin{Bmatrix}\Delta\theta\\ \Delta\theta_{\lambda}\end{Bmatrix}=-\begin{Bmatrix}U\\ V\end{Bmatrix} \tag{13}\]  
with  
\[\begin{split} R&=\begin{bmatrix}R_{1}&\cdots&0\\ \vdots&\ddots&\vdots\\ 0&\cdots&R_{N_{\text{o}}N_{i}}\end{bmatrix},\,S=\begin{bmatrix}S_{1}\\ \vdots\\ S_{N_{\text{o}}N_{i}}\end{bmatrix},\,T=\sum_{k=1}^{N_{\text{o}}N_{i}}T_{k}\\ \Delta\theta&=\begin{Bmatrix}\Delta\theta_{1}\\ \vdots\\ \Delta\theta_{N_{\text{o}}N_{i}}\end{Bmatrix},\,U=\begin{Bmatrix}\text{Re} \left(Y_{1}^{H}E_{1}\right)\\ \vdots\\ \text{Re}\left(Y_{N_{\text{o}}N_{i}}^{H}E_{N_{\text{o}}N_{i}}\right)\end{Bmatrix}, \,V=\sum_{k=1}^{N_{\text{o}}N_{i}}\text{Re}\left(X_{k}^{H}E_{k}\right)\end{split} \tag{14}\]  
From Equation (13), one can write  
\[\begin{cases}R\Delta\theta+R\Delta\theta_{\lambda}=-U\\ S^{T}\Delta\theta+T\Delta\theta_{\lambda}=-V\end{cases} \tag{15}\]  
Isolating \(\Delta\theta\) in the first set of Equations (15) and inserting the resulting expression into the second set, yields  
\[(T-S^{T}R^{-1}S)\Delta\theta_{\lambda}=S^{T}R^{-1}U-V \tag{16}\]  
Now replacing Equations (14) into (16), gives  
\[\Big{(}\sum_{k=1}^{N_{k}}T_{k}-S_{k}^{T}R_{k}^{-1}S_{k}\Big{)}\Delta\theta_{ \lambda}=\sum_{k=1}^{N_{\text{o}}N_{i}}S_{k}^{T}R_{k}^{-1}\text{Re}\left(Y_{k }^{H}E_{k}\right)-\text{Re}\left(X_{k}^{H}E_{k}\right)\]  
or simply  
\[M_{1}\Delta\theta_{\lambda}=M_{2}\]
## Data Acquisition and Model Construction
### Quest for Magnetic Component Size Reduction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Manufacturer** & **Volume (mm3)** & **Weight (g)** & **Inductor Value Range (\(\mu\)H)** & **DC Resistor Range (mf)** & **Frequency Range (MHz)** \\ \hline MSS1210 & 1353 & 5.1–6.2 & 10–10,000 & 14–7390 & 4–0.2 \\ \hline MSS1260 & 777 & 2.8–3.3 & 1–1000 & 5.8–1295 & \textgreater{}10–0.6 \\ \hline XGL6060 & 274 & 1.41–1.56 & 0.22–47 & 1.1–97 & 50–2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The main characteristics of the three inductor series considered in this work [15; 16; 17]. Figure 2: Picture of the 3 inductor series considered in this work [15; 16; 17]. Another important parameter made available by the manufacturer is the \(R_{DC}\), the winding resistance in DC. Figure 4 below is providing a logarithm plot to present the evolution of the \(R_{DC}\) with respect to the inductor value for the three inductor series. In Figure 4, it is interesting to see that, no matter the component series, a relation may be found between the series resistance \(R_{DC}\) and the inductor value. A simple relationship can be defined to link the DC series resistance of each inductor with its value as (3). \[R_{DC}=K_{l}\times L^{P_{l}} \tag{3}\]  
Looking at the evolution of the DC series resistance with respect to inductor value, it is highly desirable to reduce the value of \(L\), in a tentative to reduce the losses of the inductor  
Figure 4: Evolution of the \(R_{DC}\) series resistance of the inductors with respect to inductor value for the three studied inductor series [15; 16; 17]. Also linearized \(R_{DC}\) curves for each inductor series with respect to inductor value. Figure 3: Saturation current (at 90% nominal inductor value) versus inductor value for three Coilcraft moulded inductor series. All data are extracted from [15; 16; 17]. under operation. However, reducing the value of \(L\) without changing the switching frequency induces an increase in the current ripple magnitude that may produce extra AC losses in the windings and the core as it will be underlined later. To account for this, important knowledge is missing, the evolution of equivalent series resistance of the inductors with respect to the frequency and the magnitude of the ripple current. It is, therefore, necessary to build this complimentary knowledge in order to identify for which component series and for which inductor value, the power electronics designer will get the best compromise in a converter design optimization.
## 2 Results and Discussion
### Experimental and Theoretical UV-VIS Spectra
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experimental** & & & **Excited State Optimization** \\
**[nm]** & **CPCM [nm]** & **CPCM + 1 Molecule Water (nm)** & **(nm)** \\ \hline
314 & 288 & 291 & 330 \\  & (HOMO\(\rightarrow\)LUMO, 96\%, f = 0.3948) & (HOMO\(\rightarrow\)LUMO, 96\%, f = 0.3793) & (HOMO\(\rightarrow\)LUMO) \\
222 & 225 & 226 & 226 \\  & (HOMO\(\rightarrow\)LUMO+1, & (HOMO\(\rightarrow\)LUMO+2, & \\  & 32\%, f = 0.2307) & 37\%, f = 0.1816) & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental and theoretical (at M05-2X/6-311++G(d,p) level of theory) electronic transitions of LSD. would lead to the bathochromic shift of theoretically calculated values when a compound is surrounded by solvent molecules (315 vs. 288 nm). The possible improvement of the previously discussed strategy can be the addition of explicit solvent molecules [30]. Solvent molecules form stabilization interactions (dipole-dipole and hydrogen bonds) with the polar groups in the molecule and lead to bathochromic/hypochromic shifts. It is suggested that solvent molecules are always placed around polar groups, as these are usually responsible for the transitions in visible and vacuum UV regions. Figure S3 shows the positions of the four water molecules added around LSD. These solvent molecules were introduced in the vicinity of polar groups, nitrogen atoms of an amino group, pyrrole and piperidine rings, and oxygen of the carbonyl group. Another water molecule was added above the aromatic ring, as O\(-\)H\(\cdot\cdot\cdot\pi\) interactions are also responsible for the stabilization of the spectrum. A new electronic spectrum was also computed for ten transitions. The value of the longest wavelength transition is 291 nm, which is only 2 nm higher than the previously discussed value. The obtained values do not differ significantly, proving that the polarity of excited and ground states are similar and that the interactions with solvent molecules stabilize both states equally, as previously postulated through the electron density distribution analysis. The third strategy is optimizing the first excited state (singlet or triplet, depending on the compound) and calculating the energy difference. The excited state is optimized by adding the optimization keyword in the input file and the TD-DFT. The electronic energy difference in Hartree is then recalculated to nm. The structure of the optimized first electronic state is shown in Figure S2. This figure shows no significant differences in the ground and excited state geometries. The obtained energy of 330 nm fairly well reproduces the experimental value. The difference of 10 to 15 nm falls within the experimental width of UV-VIS transitions of this type of molecule. Therefore, the authors suggest applying all these methods to predict the expected range of electronic transitions.