esa-sceva/satcom-chunk-collection huggingface dataset:
# Compression-Assisted Adaptive ECC and RAID Scattering for NAND Flash Storage Devices
## 4 Evaluation
### Analysis of Metadata and Parity Overhead
From the results, it can be seen that when the number of StripeMapLog blocks is increased by four or more, the overall metadata log overhead can be reduced to 5% even if the capacity of flash device is hundreds of GB. Next, to analyze the parity overhead, we have measured the number of parity writes for random write requests while varying average request size from 4 KB to 128 KB. The experiments are done with two RAID5 configurations, that is, full stripe parity scheme and conventional RAID5 scheme. The full stripe parity scheme implemented in this paper was compared with the conventional RAID5 scheme, in which parities of RAID5 are updated whenever a write request occurs for the corresponding stripe. Those configurations are also set to four, eight, 16 stripe size, respectively. The result of the parity overhead according to the average random request size is depicted in Figure 13, for each stripe size four, eight, 16, respectively. In the figure, the \(x\)-axis represents the average request size and the \(y\)-axis represents the number of parity writes per request. The number of parity writes per requests means the number of parities written for each request, and therefore, the lower the value, the smaller the parity overhead. Two RAID configurations, STLog and RAID5, are depicted, where STLog stands for the full stripe parity scheme and RAID5 for the conventional RAID5 scheme. As shown in the Figure 13a, if the stripe size is four, there is little difference between STLog and RAID5. There is lower parity overhead for small requests, and parity overhead becomes similar as the request size increases. However, if the stripe size is bigger, i.e., eight or 16, the parity overhead is largely reduced with the STLog parity management scheme as shown in Figure 13b,c. It is due to the partial parity buffering of the full stripe parity scheme. Particularly, when the request size is smaller than the stripe size, the partial parity buffering applied in STLog is more effective since parity updates are frequently performed in the conventional RAID5 scheme. If the request size  
Figure 12: The results of metadata log overhead for the metadata log system for RAID5 architecture with stripe size four, eight, and 16, respectively. exceeds the stripe size, parity for full stripe write is created as it is, so STLog is no different from conventional RAID5.
# Phase Therapy: Consider the Past, Embrace the Future
## 3 Strategies for Phage Therapy
### Phage and Phage Enzymes Combined with Antibiotics  
Phages are considered to be a promising alternative to antibiotics, but multiple studies have shown that co-treatments with phages and antibiotics could achieve a better therapeutic effect than individual treatment. Phage-antibiotic synergy (PAS) was first proposed in 2007, referring to the phenomenon that the sub-lethal concentrations of certain antibiotics can significantly stimulate the propagation of lytic bacteriophages in the host bacteria, thus leading to accelerated cleavage of host cells and rapid diffusion of progeny phages [66]. Biofilm formation helps bacteria to prevent antibiotic action, but the bacterium number was more significantly decreased when lytic phage-producing depolymerase and ciprofloxacin unite to treat the biofilm of _Klebsiella pneumoniae_ than when ciprofloxacin was used alone [67]. The synergy between phage and conventional antibiotics can substantially improve the eradication of bacterial biofilms [33]. As mentioned in the previous section of this paper, phages produce depolymerases to specifically degrade exopolysaccharides in biofilm, so that antibiotics can attack the bacteria protected by biofilms. [PERSON] et al. found that the combined treatment of Vancomycin-phage EFLK1 (anti-_Enterococcus faecalis_ phage) was more effective than treatment with phage or antibiotic alone both in planktonic and biofilm cultures of vancomycin-resistant _E. faecalis_ (VRE) [47]. In addition, phage was used in combination with nisin to reduce planktonic and biofilm cells of _S. aureus_[68]. The combination of phages and antibiotics can not only enhance the antibacterial effect, but also reduce the emergence of bacterial resistance [69]. In a _P. Aeruginosa_ infection rat model, phage-ciprofloxacin combinations were highly synergistic [34]. Recently, the combination of sulfamethoxazole-trimethoprim and the phage cocktail effectively inhibit the occurrence of bacterium resistance to phage and cure the urinary tract infection with extensively drug-resistant _K. pneumoniae_[50], [PERSON] et al. successfully cured a patient infected with extensively drug-resistant _A. baumannii_ and multidrug-resistant _K. pneumoniae_ by a combination of phage cocktail and intravenous meropenem and colistin [48]. Likewise, the aforementioned phage-derived enzymes can also be used in combination with other drugs. In animal experiments, the combination therapy of a lysine LysGH15 and apigenin also shows a better treatment effect than LysGH15 or apigenin monotherapy [70]. At the same time, it should be noted that some antibiotics may have potential antagonistic effects on phage infection, and that the concentration of antibiotics and the order of addition will also have a great impact on the synergistic effect [71; 72; 73].
# Underwater Acoustic Target Tracking: A Review
## 5 Classification Underwater Acoustic Target Tracking Algorithms
### Classification Based on Instrument-Assisted Method
#### 5.1.1 Acoustic Imaging Sensor
Mean shift tracking, which is focused on calculating the color histogram in image sequences, locates the position of the interested object according to the matching results of the histogram of the initial image with the later image. The authors in [53] analyze feasibilities of two typical vision-based tracking algorithms in underwater tracking, while researchers in [49; 50] focus on overcoming the drawbacks of visual tracking. The problem of low visibility in underwater images is investigated in [49], while the disadvantage of limited FOV is overcome in [50]. Different from previous articles, in [52], another imaging technique is provided to replace acoustic cameras for nocturnal underwater tracking. In conclusion, tracking algorithms based on image or video sequences are limited to the target in close range. For marine targets which are far from tracking platforms, we should utilize the acoustic waves which can propagate over a long distance in the ocean environment.
## 3 Localization of the Parabolic Problem  
A few methods have been developed for numerical solutions to PDEs defined on unbounded domains, such as artificial boundary conditions [15], transparent boundary conditions [16; 17; 18] and independent and/or dependent variable transformations [19]. Such approaches are required since the numerical treatment of the problem requires the computations to be performed on a finite domain. In the following, we adopt the methodology that was developed in [13]. In this section, instead of problem (4), (5), we solve an initial-boundary value problem for Equation (4) on a bounded domain \(\Omega\subset\mathbb{R}^{2}\) with \((x_{i},y_{i})\in\Omega\), \(i=0,1\). For this, it is necessary to pose boundary conditions for function \(u(x,y,t)\) on the boundary \(\partial\Omega\). We follow the methodology of the fundamental solutions developed in the papers [5; 6; 13]. Let \(G(x,y,t;\alpha,\gamma,\tau)\) be the fundamental solution of Equation (4). Then, the solution of the Cauchy problem (4), (5) can be presented in the form [20]  
\[u(x,y,t)=\iint\limits_{R^{2}}G(x,y,t;\alpha,\gamma,0)u_{0}( \alpha,\gamma)\mathrm{d}\alpha\mathrm{d}\gamma\\ +\int_{0}^{t}\!\!\!\iint\limits_{R^{2}}G(x,y,t;\alpha,\gamma,\tau )F(\alpha,\gamma,\tau)\mathrm{d}\alpha\mathrm{d}\gamma\mathrm{d}\tau \tag{8}\]  
Assume that, outside the bounded domain \(\Omega\subset\mathbb{R}^{2},\ u_{0}(x,y)=0\), and \(F(x,y,t)=0\). Then, the integral representation (8) takes the form  
\[u(x,y,t)=\iint\limits_{D}G(x,y,t;\alpha,\gamma,0)u_{0}(\alpha, \gamma)\mathrm{d}\alpha\mathrm{d}\gamma\\ +\int_{0}^{t}\!\!\!\iint\limits_{D}G(x,y,t;\alpha,\gamma,\tau)F( \alpha,\gamma,\tau)\mathrm{d}\alpha\mathrm{d}\gamma\mathrm{d}\tau, \tag{9}\]  
i.e., the integration is performed on a bounded domain for the localized initial condition and right-hand side. Therefore, a transition of the Cauchy problem (4), (5) to an equivalent one on a bounded domain is performed. Thus, if we numerically solve problem (4), (5) on a finite domain, we can use formula (9) to take appropriate boundary conditions. However, this procedure is expensive, and we construct a more economical one below. We start with the introduction of the auxiliary problems,  
\[\mathcal{L}v=F(x,y,t),\ \ x\in\Omega,\ \ 0<t<T, \tag{10}\]  
\[v(x,y,0)=u_{0}(x,y), \tag{11}\]  
\[v(x,y,t)=0,\ \ (x,y)\in\partial\Omega,\ \ 0<t\leq T. \tag{12}\]  
Let \(\mathcal{L}^{*}\) be the adjoint to the \(\mathcal{L}\) operator [21], i.e.,  
\[\mathcal{L}^{*}u(x,y,t)=-\frac{\partial u}{\partial t}-a\frac{\partial^{2}u}{ \partial x^{2}}-c\frac{\partial^{2}u}{\partial y^{2}}. \tag{13}\]  
Then, with respect to arguments \(\alpha,\gamma,\tau\), the fundamental solution \(G(x,y,t;\alpha,\gamma,\tau)\) satisfies the equation  
\[\mathcal{L}^{*}_{\alpha,\gamma,\tau}G=0,\tau<t, \tag{14}\]which means that \(G\) is the fundamental solution of the adjoint equation with respect to the variables \(\alpha,\gamma,\tau\).
## 3 Collision-Free Hybrid MAC Protocol Based on Pipeline Parallel Transmission
### Optimization of the Handshaking and Data Transmission Schedules
#### 3.4.1 Time Slot Allocation for Handshaking  
In a TDMA period, each node is allocated a fixed time slot, which means that each node can send out a packet as needed. Assume that a TDMA period starts at time \(T_{0}\). For a cluster consisted of \(N\) nodes, the start of the time slot for each node can be determined using the following steps according to the condition of collision-free transmission provided by Equation (1):  
1. [label=0., ref=1]
2. Initiate an empty queue \(\mathbf{Q}_{0}\) for the nodes whose time slots are determined. Create a set \(\overline{\mathbf{Q}}_{0}=\{Q_{i}|1\leq i\leq N\}\) for the nodes whose time slots are not determined, where \(Q_{i}\) denotes node \(i\) in the cluster. Set \(r=1\).
3. Pick a node from \(\overline{\mathbf{Q}}_{r-1}\) and try to add it to the tail of \(\mathbf{Q}_{r-1}\). Assume that the selected node is \(Q_{i}\). The optimal transmission time for \(Q_{i}\) can be calculated using the following equation: \[T_{r,Q_{i}}^{slot}=\left\{\begin{array}{cc}T_{0},&r=1\\ \max_{l}\Bigl{(}T_{r-1}^{slot}+D_{Q_{tail},Q_{l}}-D_{Q_{l},Q_{l}}\Bigr{)}+L^{ slot}+C,&r>1,&1\leq l\leq N\end{array}\right.\] (2) where \(T_{r,Q_{i}}^{slot}\) denotes the start of the time slot for \(Q_{i}\). \(Q_{tail}\) denotes the tail node of \(\mathbf{Q}_{r-1}\). \(T_{r-1}^{slot}\) is the start of the time slot for \(Q_{tail}\). \(L^{slot}=\max\Bigl{(}L^{RTS},L^{CTS},L^{ACK}\Bigr{)}\), where \(L^{RTS}\), \(L^{CTS}\) and \(L^{ACK}\) denote the transmission durations of the RTS, CTS and ACK packets. \(C\) is the guard time required to accommodate the variations of the propagation delay.
4. Select the node from \(\overline{\mathbf{Q}}_{r-1}\) that minimizes the ending time of \(\mathbf{Q}_{r-1}\) when this node is added to its tail, i.e., \[Q_{r}^{opt}=\underset{Q_{i}}{\operatorname{argmin}}\Bigl{(}\max_{l}\Bigl{(}T_ {r,Q_{i}}^{slot}+D_{Q_{i},Q_{l}}\Bigr{)}\Bigr{)},\ 1\leq l\leq N,\ Q_{i}\in \overline{\mathbf{Q}}_{r-1}\] (3)
5. Calculate the start of the time slot for \(Q_{r}^{opt}\) using the following equation: \[T_{r}^{slot}=\left\{\begin{array}{cc}T_{0},&r=1\\ \max_{l}\Bigl{(}T_{r-1}^{slot}+D_{Q_{tail},Q_{l}}-D_{Q_{l}^{opt},Q_{l}}\Bigr{)} +L^{slot}+C,&r>1,&1\leq l\leq N\end{array}\right.\] (4) Add \(Q_{r}^{opt}\) to the tail of \(\mathbf{Q}_{r-1}\) and delete it from \(\overline{\mathbf{Q}}_{r-1}\).  
If \(r<N\), then \(r=r+1\) and repeat steps 2 to step 5 for another iteration. Otherwise, the node order in \(\mathbf{Q}_{N}\) is the optimized time slot sequence. The time slot for the \(r\)-th node in \(\mathbf{Q}_{N}\) is \(\left[T_{r}^{slot},T_{r}^{slot}+L^{slot}\right]\). The total length of the TDMA period is given by  
\[T^{TDMA}=\underset{l}{\text{max}}\left(T_{N}^{slot}+D_{Q_{N}^{opt},Q_{l}} \right)+L^{slot}+C,\ 1\leq l\leq N \tag{5}\]
## 2 Estimation of Sideslip Angle  
During the vehicle stability control, the sideslip angle is a key control parameter, and its estimation accuracy can directly affect the controlling quality. So far, the sideslip angle is mostly determined by estimation. For accurate estimation of this angle, we established a 7-DOF nonlinear vehicle dynamic model involving tire force, which was computed using the unitire model.
## 4 Results Analysis
### BIM Integration with GPS and RFID  
GPS (global positioning system) and RFID (radio-frequency identification) are both used to find the actual location of the construction workers [109]. The interviewers asked, 'Often a safety sign is missing in construction of high-rise buildings such as no on-site monitoring system of workers, no safety sign location plan, according to your experiences, what do you think BIM technology helps to minimize this issue?' All the interviewees strongly agreed that with BIM integration-GPS-RFID, the location of the construction workers can easily be tracked. Responses included 'Yes sure with the integration of BIM with digital technologies, for example RFID, GPS can monitor the movements of construction workers in high-rise buildings' and 'I think yes, it can be done with the incorporation of BIM with RFID'.  
In RFID, electromagnetic fields are the most useful and productive techniques for the role of construction workers. It is also possible that the BIM-RFID platform can support the visibility and traceability of construction workers in real time. The statements of the interviewees are also well backed by the literature studies. [PERSON] et al. [110] suggested an integrated RFID and GPS system for construction sites and revealed that the accuracy indicator helps to estimate the accuracy of RFID-based position and tracking systems in construction. Moreover, [PERSON] et al. [111] concluded that RFID technology has shown its ability to locate and track construction resources. Using BIM integration with GPS-RFID and other techniques, such as cloud computing, internet of things (IoT) etc., can be used in the tracking of construction objects, and these objects must be converted into smart objects that can be tracked in real time over the internet or in the cloud. Real-time internet or cloud-based monitoring enables better coordination between remote project stakeholders.
# Optimized Multiport DC/DC Converter for Vehicle Drivetrains: Topology and Design Optimization
## 2 Analysis of Non-Isolated Interleaved DC-DC Converter
### Losses of Converter
#### 2.3.1 IGBT Losses and Diode Losses  
The losses of IGBTs (\(P_{loss\_IGBT}\)) and diodes (\(P_{loss\_D}\)) are due to the conduction losses and switching losses, which are evaluated based on [20], but neglecting the effect of the temperature variation.  
\[P_{loss\_IGBT}=\ I_{Srms}^{2}r_{CE}+V_{CE}I_{s}+\left(\frac{V_{o}}{V_{cc}} \right)^{1.2}\left[E_{off}\left(\frac{I_{s,rms}}{I_{c}}\right)+E_{on}\left( \frac{I_{s,rms}}{I_{c}}\right)\right]f_{sw} \tag{14}\]  
\[P_{loss\_D}=I_{Drms}^{2}r_{f}+V_{\text{F0}}I_{D}+\left(\frac{V_{o}}{V_{cc}} \right)^{0.6}E_{rr}\left(\frac{I_{d,rms}}{I_{c}}\right)^{0.6}f_{sw} \tag{15}\]  
where the IGBT and diode characteristics (\(r_{CE}\), \(V_{CE}\), \(V_{CC}\), \(I_{c}\), \(E_{off}\), \(E_{on}\), \(r_{f}\), \(V_{\text{F0}}\), \(\text{I}_{\text{F}}\) and \(E_{rr}\)) are given by the IGBT and diode datasheets; in addition, the effect of temperature variation is neglected. \(I_{S}\), \(I_{S,rms}\), \(I_{D}\) and \(I_{D,rms}\) are the switch and diode current.
# Least-Squares Reverse Time Migration in Imaging Domain Based on Global Space-Varying Deconvolution
## 4 Numerical Experiment  
The method is tested on the 3D Overthrust model. The model is 16 km, 5 km, and 3 km in size, and the migration velocity is obtained by Gaussian smoothing with a window of 40 m. The Ricker wavelet with the main frequency of 30 Hz is used in this experiment. As shown in Figure 3, the RTM result has an insufficient spatial resolution, and the amplitude of the reflectors attenuates rapidly as the depth increases.  
Then we compute the PSF using the same wavelet. Based on the migration velocity, the sampling interval (400 m, 400 m, 400 m) of the global PSF is set for the scattering model. To give intuitive insight into PSF, we present the X-Z tangent plane for point spread functions in Figure 4. As the geological structure and the illumination condition change, we can easily find that the difference of global PSFs in morphology is not negligible.  
Figure 5 shows the results from the proposed imaging-domain LSM. Compared with the RTM results, the LSM results show higher resolution, fewer migration noise, more balanced amplitude in deep and better focusing on diffracted structures (as shown in Figure 6). To compare the spatial resolution between these results, we compute the vertical wavenumber spectrum of Figures 3 and 5 for the whole model and present them in Figure 7. From the vertical wavenumber spectrum, we can find that the result from the LSM method has a broader frequency-band, especially for mid-high wave numbers. It means that the  
Figure 3: The 3D data volume of RTM in the 3D Overthrust model.  
Figure 2: Results from LSM in imaging domain by using single point spread function. (**a**) the point spread function at grid (26, 1), the shallow details are similar as reflectivity model; (**b**) the point spread function at grid (26, 11), the image is much closer to RTM result.  
Figure 5: The 3D data volume of LSM in the 3D Overthrust model.  
Figure 6: The 2D display of the X-Z tangent planes results from (**a**) RTM; (**b**) LSM.  
Figure 7: The comparison of vertical-wave-number spectrum between RTM (red) and LSM (blue) from Figures 3 and 5. The unit of y-axis is decibel, and x-axis indicates the wavenumber component.
# Dynamic Behavior of a Simple Rolling Seismic Isolator with a Position Restoring Device
## 2 Equation of Motion
### Equation of Motion  
In this paper, a rolling-type isolator system with PRD is established in Figure 2, and the assumptions to derive the equation of motion are as follows. The superstructure, with a total mass of the \(M\), is considered as a rigid structure having only horizontal motion; the rollers and the upper and base plates make rolling contact without slippage; since the mass of the rollers is smaller than the total mass, it is ignored; and that the radius of the concave surface is very large.  
Figure 1: Model of rolling-type isolator with the position restoring device (PRD).  
Figure 2: Free body diagram of the isolator.  
The force acting on the system is applied to the gravitational load \(Mg\) of the superstructure, the normal force \(N\), the rolling resistance \(F_{r}\) on the concave surface, and the vertical reaction \(N_{s}\) and the friction force \(N_{s}\) by the spring ball device, as shown in Figure 2. Here, \(F\) and \(g\) are the spring-damper unit force and gravitational acceleration, respectively. \(\mu_{r}\) is the ratio of the rolling resistant coefficient to the roller radius [29]. In Figure 2, we can see the dimensions of the wedge-shaped hole, which is a symmetric structure with a width of 2a and a height of b. In this study, it is assumed that the PRD plays a role only when the relative displacement \(x\) of the superstructure satisfies the condition of \(-a\leq x\leq a\). Then, if the compression displacement for the initial preload of this device is \(y_{0}\) the total compression amount \(\delta\) for the displacement \(x\) is as follows,  
\[\delta=y_{0}+\frac{b}{a}|x|-L(1-\cos\theta). \tag{4}\]  
when \(-a\leq x\leq a\), the normal force \(F\) is the sum of the spring force \(F_{s}\) and damping force \(F_{c}\), are expressed as follows,  
\[F_{s}=k\bigg{\{}y_{0}+\frac{b}{a}|x|-L(1-\cos\theta)\bigg{\}}\, \tag{5}\]  
\[F_{c}=c_{r}\frac{b}{a}\dot{x}\ sgn(x)\, \tag{6}\]  
where \(k\) and \(c_{r}\) are the spring constant and damping coefficient of the PRD, respectively.  
From the free body diagram in Figure 2, we can write the equilibrium equations of force for the horizontal and vertical directions when \(-a\leq x\leq a\);  
\[\begin{array}{rl}\sum F_{x}=&-N\sin\theta-sgn\big{(}\dot{x}\big{)}\mu_{r}N \cos\theta-sgn(x)N_{s}\sin\alpha\\ &\ \ \ \ \ \ \ \ \ \ \ \ \ \ \  
As can be seen from Equations (9) and (14), the natural frequency is mainly related to the radius of the curvature of the concave base \(R\) and the radius of the roller \(r\) without depending on the mass of the superstructure \(M\). The natural frequency is expressed as follows  
\[f_{n}=\frac{1}{2\pi}\sqrt{\frac{g}{L}}. \tag{15}\]  
In the model shown in Figure 1, the natural frequency \(f_{n}\) is 0.66 Hz for \(R=600\) mm and \(r=25\) mm.
# Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate
## 4 Applications Related to the Water Cycle
### Nowcasting  
The application to nowcasting and short-term regional weather forecasting is an important further application area. NASA's Short-term Prediction Research and Transition (SPoRT) Center [259] strives to provide products that enter the operational practice of the National Weather Service (NWS) in remote areas of the US. Flood, and especially flash flood monitoring and short-term forecasting is a key application which requires precipitation products at very short time intervals. The Hydro-Estimator (H-E, [260]) from NOAA has been in use since 2002, and makes available routine estimates every 15 min thanks to the fast repetition rate of GEO IR data. Rainfall totals at 1, 6, and 24 h are produced by NOAA's Self-Calibrating Multivariate Precipitation Retrieval (SCAMPR, [261; 262]) algorithm, combining GEO IR and LEO MW data for hydrological applications over the continental US. The algorithms of the Satellite Application Facility in Support to Operational Hydrology and Water Management (H SAF) of EUMETSAT are used in hydrological applications [263]. A separate topic is the improvement of rainfall potential and tracking of hurricanes. The ensemble Tropical Rainfall Potential (eTRaP) was developed to improve short-range forecasts of heavy rainfall in tropical cyclones [264] ([[https://www.ssd.noaa.gov/PS/TROP/etrap.html](https://www.ssd.noaa.gov/PS/TROP/etrap.html)]([https://www.ssd.noaa.gov/PS/TROP/etrap.html](https://www.ssd.noaa.gov/PS/TROP/etrap.html)), last accessed 20 August 2019; see Figure 9). The assimilation of MW observations in NWP models is also improving weather prediction and the forecasting of tropical cyclone tracks and intensities [251].
## 4 Performance Analysis of the BOC Signal and BPSK Signal
### The Design of the Signal Channel  
The parameters of the simulation scenario are given in Table 1. We simulate the state of the nodes under very high frequency (VHF) and S-band conditions.