esa-sceva/satcom-chunk-collection huggingface dataset:
# Inter-Data Center RDMA: Challenges, Status, and Future Directions
## 4 Existing Representative Works
### Bifrost  
Bifrost [23] introduces a downstream-driven predictive flow control to extend RoCEv2 for efficient long-distance inter-DC transmission, addressing the inefficiencies of PFC without additional hardware. Unlike SWING, which relies on external relay devices, Bifrost innovatively modifies switch chip logic to leverage virtual incoming packets, a novel concept where the downstream port uses historical pause frame records to predict the upper bound of in-flight packets for the next RTT. This allows proactive, fine-grained control over upstream sending rates, eliminating the need for deep buffers while maintaining Ethernet/IP compatibility.  
As illustrated in Figure 4, the design centers on the downstream port maintaining a history of pause frames to infer incoming packet limits, termed \"virtual incoming packets.\" By combining this predictive upper bound with the real-time queue length, Bifrost dynamically adjusts the flow rates in microsecond-scale time slots, ensuring the upstream matches the downstream draining capacity with minimal delay. This approach reduces buffer requirements to one-hop BDP, half of PFC's 2 \(\times\) BDP requirement, while achieving zero packet loss.  
Crucially, Bifrost reuses PFC frame formats and integrates with existing switch architectures, requiring only lightweight modifications to the hardware logic of egress switches (e.g., on-chip co-processor for virtual packet tracking). This avoids additional device deployment and ensures seamless compatibility with intra-DC RoCEv2 infrastructure. Real-world experiments demonstrate that Bifrost reduces average FCT by up to 22.5% and tail latency by 42% compared to PFC, using 37% less buffer space. By shifting congestion control intelligence to the downstream switch and enabling predictive signaling, Bifrost  
Figure 3: Design diagram of SWING (adapted from [19], Figures 8 and 10).  
achieves a balance of low latency, high throughput, and minimal buffer usage, making it a pragmatic solution for extending lossless RDMA to inter-DC links without compromising protocol compatibility.
# Evaluation of Pressure Effect on Accelerated Aging Tests of Polymer-Insulated Aircraft Wires
## 3 The Proposed Experimental Method to Measure the Surface Discharges Activity  
### Surface Discharge Detection and Quantification Methods  
This section describes the three methods used to measure the surface discharge activity for each sample at each degradation cycle and pressure level.  
The process begins by purging air from the low-pressure chamber and replacing it with fresh air to prevent ozone contamination. Then, an automatic control system adjusts and maintains the chamber pressure at the appropriate level (16 kPa, 44 kPa, 72 kPa, and 100 kPa in this work) to simulate typical low-pressure aircraft conditions. Next, the power source is configured to apply a 400 Hz AC voltage to the wires. This frequency is based on the European standard EN 3475-603 for aeronautical applications. The AC 400 Hz voltage applied to the samples is increased in increments of 20 VRMS. At each voltage step, an RGB image is captured with the CMOS sensor described in Section 4 and analyzed with image processing software (Matlab @ R2022b) to determine if surface discharge activity has been detected. This process is repeated until surface discharge activity is detected so that the surface discharges inception voltage (SDIV) is obtained for each sample at each degradation cycle. The SDIV is the minimum voltage at which surface discharge activity is detected with the CMOS sensor. Once the SDIV is registered, the voltage of the source is set to the reference voltage, corresponding to the SDIV value measured during the first degradation cycle for each pressure level and sample analyzed. The response of the solar blind UV sensor is then measured, and a 25 s long exposure photograph is taken with the CMOS imaging sensor. The image is processed by special software (Matlab @ R2022b) that detects the surface discharge activity and measures the intensity of the image. Figure 2 summarizes the various steps needed to detect the SDIV and quantify the intensity of the discharges with the CMOS and the solar blind UV sensors.
## 3 Numerical Result of Bandgap Characteristics  
### Effect of Initial Stress on the Bandgap Characteristics  
The finite element method has been employed to the bandgap calculation of CCPM.  
When the configuration changes, the lattice constant of CCPM changes, resulting in complex boundary conditions. Although, under complex boundary conditions, the eigenvalue of CCPM can be solved directly using COMSOL.  
The periodicity of CCPM enables calculations to be implemented in a unit cell. The stress of the CCPM under different axial displacement is obtained first. Then, the initial stress is adopted as a precondition into the numerical bandgap calculations. Floquet periodicity boundaries are applied to the unit cell. The meshes of the computational domain are constructed using free trihedral meshing, and the Floquet boundaries are marked in red (Figure 4). The effect of the initial stress on the bandgap characteristics of the CCPM is discussed and compared with the experimental results cited in paper [11].  
Figure 7 depicts the comparison between the bandgap diagram and transmittance spectra when the axial compression deformation is 10 mm. Figure 7a,b are the bandgap diagrams in the range of 0 Hz to 450 Hz with and without considering initial stress. Figure 7c is the experimental transmission spectra cited from paper [11]. It can be seen that two bandgaps appear between 0 Hz and 450 Hz. Without considering the initial stress, the frequency range of the first bandgap is 262 Hz-310 Hz and that of the second bandgap is 394 Hz-405 Hz. Considering the initial stress, the frequency range of the first and second bandgaps is converted into 254 Hz-291 Hz and 391 Hz-400 Hz. Their beginning and ending frequencies decrease, and the width of the two bandgaps is reduced when the initial stress is considered. Compared with Figure 7a, considering the initial stress, the two bandgap frequency ranges are more in accordance with the experimental transmission spectra. From the comparison in Figure 7, we may reach the conclusion that  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & **5 mm** & **10 mm** & **15 mm** & **20 mm** & **25 mm** & **30 mm** \\ \hline \multirow{3}{*}{Point 1} & FEM (Pa) & 8961 & 17,574 & 25,752 & 33,582 & 40,803 & 47,589 \\  & EXP (Pa) & 8526 & 18,531 & 24,621 & 35,844 & 44,370 & 52,287 \\  & error & 4.85\% & 5.45\% & 4.39\% & 6.74\% & 8.74\% & 9.87\% \\ \hline \multirow{3}{*}{Point 2} & FEM (Pa) & 9222 & 18,009 & 26,274 & 34,191 & 41,412 & 48,198 \\  & EXP (Pa) & 8874 & 17,052 & 27,579 & 32,016 & 38,367 & 43,848 \\ \cline{1-1}  & error & 3.77\% & 5.31\% & 4.97\% & 6.36\% & 7.35\% & 9.03\% \\ \hline \end{tabular}
\end{table}
Table 2: Experimental values and finite element results of stress at observation points.  
Figure 6: The relationship between compression displacement and axial stress.  
initial stress occurs as the configuration of CCPM changes and has a significant influence on its bandgap characteristics.
## 4 Problem Definition and Research Methodology
### Selection of Features for Improved Algorithm Accuracy  
The goal of the study was to build a model that predicts CQI in growing network load circumstances. The main idea was to segment cells according to important factors and drivers for performance. The features examined that could improve modeling accuracy are:  
- frequency band,  
- Physical Resource Block (PRB) utilization in surrounding cells,  
- number of surrounding cells within a given radius,  
- heavy data factor,  
- higher order modulation usage.  
A natural segmentation is by the frequency band, since radio propagation is different at different frequencies, and as discussed earlier, the inter cell interference is the main performance factor. Hence, performance in cells operating at lower frequencies will be more affected by interference in growing network load scenarios. This is due to better radio propagation and a higher probability that the signal from a neighboring cell will interfere with the signal from the serving cell.  
Another relevant indicator is the number of neighboring cells within a certain radius. This indicator reflects the network density. The idea, examined previously in [16; 17], is that the denser a network the more sensitive to interference it will be. The average PRB utilization (determined for all surrounding cells and averaged) and total PRB utilization (aggregated for all surrounding cells, encapsulating the number of neighbors in a total score) were both investigated. Surrounding cells were defined as cells within a chosen radius. A radius of 2.5 km, 5 km, and 10 km were compared. Performance was best using a 5 km radius definition of surrounding cells, although the performance using 2.5 km was similar. A 10 km radius led to a substantially worse performance than 5 km or 2.5 km.  
Finally, the load in neighboring cells, measured as average PRB utilization, is defined to segment parts of the network with higher traffic. Higher order modulation usage was selected as an indicator that should capture the distribution of users in space and segment cells by the average radio condition. It is defined as the percentage usage of 64 QAM and 256 QAM. For cells with users distributed closer to the antenna, and in good radio conditions it will have higher values. The last indicator considered is the heavy data factor, with the idea of segmenting cells by the type of traffic. Some use cases, such as video streaming services or heavy FTP download, utilize the LTE/5G-NR network resources to a much larger extent than other use cases, with a small amount of bursty data such as reactions on social media, or messaging applications. Capturing traffic burstiness is not an easy task with available counters in PM systems. The authors have tried to capture this with the ratio of data payload in the cell and PRB utilization. However, a high level of correlation between both the heavy data factor and higher order modulation features with spectral efficiency may be expected.
## 2 Related Method of XNAV for Single Spacecraft  
### Measurement Model  
The measurement of pulsar-based navigation is the pulse phase. The pulse phase at \(t_{i}\) can be expressed as [16] follows:  
\[\phi(t_{i})=F_{0}[g(t_{i})-T_{0}]+\frac{F_{1}}{2}[g(t_{i})-T_{0}]^{2}, \tag{1}\]  
where \(F_{0}\) and \(F_{1}\) are the frequency of the pulsar and the time derivative of the frequency at \(T_{0}\), respectively, and \(F_{0}\) and \(F_{1}\) can be found in the public pulsar ephemeris [21]. In addition, \(g(t_{i})\) can be described by [20; 22] the following:  
\[g(t_{i})=t_{i}+\frac{\mathbf{n}\cdot(\mathbf{r}_{i}+\mathbf{r}_{E,i})}{c}+\sum_{k=1}^{K} \frac{2\mu_{k}}{c^{3}}ln|\mathbf{n}\cdot\mathbf{r}_{k,i}+\big{\|}\mathbf{r}_{k,i}\big{\|}, \tag{2}\]  
where \(c\) is the speed of light, \(\mathbf{r}_{i}\) is position vector of the spacecraft relative to the Earth at \(t_{i}\), \(\mathbf{n}\) is the direction vector of the pulsar, \(\mathbf{r}_{E,i}\) is the position vector of Earth relative to the SSB at \(t_{i}\), \(\mathbf{r}_{k,i}\) is the position of the spacecraft relative to the \(k\)th celestial body(unit m), \(\mu_{k}\) is the gravitational coefficient of the \(k\)th celestial body, and \(\|\ \|\) is the magnitude of the vector. The second and third terms on the right side of Equation (2) are the Romer delay and the Shapiro delay, respectively. The Romer delay represents a vacuum delay between the spacecraft and the SSB, while the Shapiro delay represents a time delay caused by the pulse passing through curved space-time.  
Assuming that \(\mathbf{x}_{i}=\mathbf{x}(t_{i})=[\mathbf{r}_{i},\mathbf{v}_{i}]\), \(y_{i}=\phi(t_{i})=\phi_{i}\), and \(h(\mathbf{x}^{i})=F_{0}[g(t_{i})-T_{0}]+\frac{F_{1}}{2}[g(t_{i})-T_{0}]^{2},\mathbf{v} _{i}\) is the velocity of the spacecraft relative to the Earth at \(t_{i}\). Thus, Equation (1) can be rewritten as [16] follows:  
\[y_{i}=h(\mathbf{x}_{i}). \tag{3}\]
## 4 Discussion
### Coastal Lakes as a Buffer Zone between the Continental and Marine Environment  
Coastal lakes, commonly connected directly with the ocean and also having water discharged from inland rivers, can serve as a repository and reworking zone for plastics sourced from the continental environment. Thus, plastic debris from the drainage area of rivers can be transported to coastal lakes and reprocessed there. The Dishui Lake is a typifying example of this type of lake. The lake connects with the East China Sea via a canal on the southeast. The Dazhi River, a tributary of the Huangpu River which runs through the center of Shanghai, enters the lake on the north. We call such lakes as buffer zones for plastics (PBZ), which can act as a sink for plastics from the continental environment and, at the same time, as a source contributing plastics to the marine environment. Specifically, coastal lakes like the Dishui can serve as a reworking zone for plastics entering them, which can be further fragmented by physical, chemical and biological processes [55,58]. Plastics entering such lakes can be from direct sources, i.e., human activities around the lake, or from indirect sources, e.g., by riverine transport from continental environment.  
Recent studies have shown that size distribution of plastic debris can be used to indicate the source and transport pathways [59]. The average abundance at ALS and TCS sites were, respectively, 47.1 and 230 items kg\({}^{-1}\) d.w.s. The concentrations of microplastics in the canal sites DSL-13 and NHZ are orders of magnitude higher than that at the lake sites (DSL-6, DSL-10 and D-2). At the ALS sites, microplastics accounted for 86.8% of all size plastics. However, the ratio at the TCS sites was 93.0%. This indicated that plastics were more severely fragmented in the canal than in the lake. Combining the fact that the Nankuizui gate is periodically opened to release water to the ESC, we infer that during the closing of the Nankuizui gate, the plastic debris entering the lake are broken into smaller pieces by physical, chemical, and biological processes. Our observations suggest that Dishui Lake served as a PBZ, contributing plastic particles to the Eastern China Sea.
## 5 Experimental Proof of Two Cache Timing Attacks
### Cache Timing Side-Channel Attacks  
This section describes the implementation of two cache timing attacks, the _Flush+Reload_ attack and the _Evict+Time_ attack. This attack targets the symmetrical encryption algorithm _AES-128_ (Advanced Encryption Standard) running in the processing system, it is a standard encryption use for many applications and is currently implanted in crypto-processors [19]. We use the specific implementation call _AES-128 T-table_ presented in the following section.
## 2 Numerical Model and Verification
### Model Verification  
The validity of the finite element method was demonstrated by comparing the numerical results with both analytical and experimental results available in the literature. First, a modal analysis was performed on a FE model of a liquid container that has been experimentally studied in the literature. The first three convective frequencies of the filled water were compared with an analytical solution proposed by [64].  
\[{\omega_{n}}^{2}=\left((2n-1)\frac{\pi g}{L}\text{tanh}\bigg{(}(2n-1)\frac{\pi H _{L}}{L}\bigg{)}\bigg{)}n=1,2,3,\ldots \tag{12}\]  
where \(L\) and \(H_{L}\) are the tank's length and the height of the water, respectively.  
A liquid container modeled to verify the numerical method had a length, width, and height of 1, 0.4, and 0.96 m, respectively. The water depth inside the tank (\(H_{L}\)) was 0.624 m. This tank was numerically and experimentally studied by [53]. Figure 2 shows the schematic of the tank.  
This water-tank system was numerically modeled and meshed using Fluid80 elements with different dimensional properties to determine the optimal size for the elements. A modal analysis was conducted for each element size, and the first five frequencies of the water were compared with those of the analytical solution (Equation (12)). The frequency responses obtained from the FE analysis of the tank, discretized with various element sizes, were compared with those obtained from the analytical solution, and are shown in Table 1.  
According to Table 1, the element size for M4 is fine enough to calculate the first five frequencies of the water with 0, 1, 2, 2, and 3.5 percent compared to the analytical solution. Furthermore, the larger element sizes, i.e., larger than M1, have not been able to calculate higher sloshing modes.  
Figure 2: Schematic view of the verification tank model [53].  
This work then compared the time-history sloshing response of the same tank with the experimental results published by [53]. The tank was subjected to a harmonic load, as shown in Equation (13).  
\[f(t)=D.\text{sin}(\omega t) \tag{13}\]  
where \(D\) and \(\omega\) are the displacement and frequency, set to 5 mm and 1.12 times the water's first natural frequency (0.87 Hz), respectively. Figure 3 shows the time history of the sloshing response of the tank at the left wall of the tank.  
The comparison between the numerical and the experimental results, as shown in Figure 3, demonstrates that the numerical model can accurately capture the time-history sloshing response. The difference between the maximum sloshing response of the numerical model, which is equal to 60.3 mm, with that of the experimental model, 62.3 mm, is 3.2 percent. It is important to acknowledge that while the maximum sloshing response is accurately captured using this numerical method, increasing the nonlinearity of this response increased the difference between the time-history sloshing response of these two models.  
Based on Figure 3 and Table 1, the numerical method can accurately model the water-tank system's maximum sloshing response.
# Energy and Exergy Analyses of an Existing Solar-Assisted Combined Cycle Power Plant
## 3 Thermodynamic Analysis
### Exergetic Efficiencies  
Exergy-based performance analysis is the performance study of a system based on the second law of thermodynamics, which overcomes the limitations of studying the system based on the first law of thermodynamics. Exergy is a measure of the maximum useful work of a system as it proceeds to a specified final state in equilibrium with its surroundings (dead state). Exergy is destroyed in the system, not conserved as energy is.  
Two different approaches are generally used to calculate the exergy efficiency of a system, one is called \"brute force\", while the other is called \"functional\" [16].  
The brute force form of exergy efficiency is used in this paper. The brute force form requires accuracy and an explicit definition of each input and output exergy term before calculating the exergy efficiency as shown in Table 2. The input exergy terms of the ISCC represent the chemical exergy of the fuel and the exergy associated with the solar thermal energy input.  
So, the second law efficiency (\(\eta_{II}\)) (exergetic efficiency) is given by  
\[\eta_{II}=\frac{Exergy\ output}{Exergy\ input} \tag{8}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Exergy Destruction** & **Second Law Efficiency (\(\eta_{II}\))** \\ \hline Pumps & \(\dot{I}_{pump}=\dot{X}_{in}-\dot{X}_{out}+\dot{W}_{pump}\) & \(\eta_{II,pump}=1-\frac{j_{pump}}{W_{pump}}\) \\ Heaters & \(\dot{I}_{natter}=\dot{X}_{in}-\dot{X}_{out}\) & \(\eta_{II,hotter}=1-\frac{j_{hotter}}{\dot{X}_{in}}\) \\ Turbine & \(\dot{I}_{turbine}=\dot{X}_{in}-\dot{X}_{out}-\dot{W}_{turbine}\) & \(\eta_{II,turbine}=1-\frac{j_{hotter}}{\dot{X}_{in}-\dot{X}_{out}}\) \\ Condenser & \(\dot{I}_{condenser}=\dot{X}_{in}-\dot{X}_{out}\) & \(\eta_{II,condenser}=\frac{\dot{X}_{out}}{\dot{X}_{in}}\) \\ Cycle & \(\dot{I}_{cycle}=\sum\limits_{all\ components}\dot{I}\) & \(\eta_{II,cycle}=\frac{\dot{W}_{out,out}}{\dot{X}_{cycle,in}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Definitions of the exergy destruction and second law efficiency.  
The net exergy transfer by heat (\(\dot{X}_{heat}\)) at the source temperature (\(T_{s}\)) and dead state temperature (\(T_{0}\)) is given by  
\[\dot{X}_{heat}=\sum(1-\frac{T_{0}}{T_{s}})\ \dot{Q}_{s} \tag{9}\]  
and the specific exergy (\(\Psi\)) is given by  
\[\Psi=(h-h_{0})-T_{0}(s-s_{0}) \tag{10}\]  
where \(h\), \(h_{0}\), \(s\), and \(s_{0}\) are the specific enthalpy, the specific enthalpy under the dead state condition, the specific entropy, and the specific entropy under the dead state condition, respectively.  
Then, the total exergy rate associated with a fluid stream (\(\dot{X}\)) at the mass flow rate (\(\dot{m}\)) becomes  
\[\dot{X}=\dot{m}*\Psi=\dot{m}[(h-h_{0})-T_{0}(s-s_{0})]. \tag{11}\]  
The exergy destruction rate in the ISCC as a whole (\(\dot{I}_{ISCC}\)) was obtained from  
\[\dot{I}_{ISCC}=\dot{I}_{compressor}+\dot{I}_{CC}+\dot{I}_{GT}+\dot{I}_{SF}+ \dot{I}_{SFHex}+\dot{I}_{condenser}+\dot{I}_{CP}+\dot{I}_{FWP}+\dot{I}_{ST}+ \dot{I}_{HRSG}. \tag{12}\]
###### Abstract  
NASA's Cyclone Global Navigation Satellite System (CYGNSS) mission, launched in 2016, is a small satellite constellation designed to measure the ocean surface wind speed in hurricanes and tropical cyclones. To explore its additional capabilities for applications on the land surface, this study investigated the advantages and limitations of using CYGNSS data to monitor flood inundation during typhoon and extreme precipitation events in southeast China in 2017. The results showed that despite the lack of quantitative evaluation, the CYGNSS-derived surface reflectivity (SR) and flood inundation area was qualitatively consistent with the Global Precipitation Measurement (GPM)-derived precipitation and Soil Moisture Active Passive (SMAP)/Soil Moisture and Ocean Salinity (SMOS)-derived total brightness temperature at circular polarization (T\({}_{\text{bc}}\)). The results provide supporting evidence for further designation of Global Navigation Satellite System (GNSS) reflectometry (GNSS-R) constellations to monitor land surface hydrology.  
CYGNSS; GNSS-R; flood inundation; typhoon; extreme precipitation 2019; 11, 854; doi:10.3390/rs11070854
## 4 Results and Discussion
### Doppler Amplitude Threshold Setting  
Figure 13 represents the probability density function (PDF) of Doppler amplitude reflected in two conditions; nylon particles 80 \(\upmu\)m and microbubbles <1 mm at \(U_{L}\): 200 mm/s and 300 mm/s, respectively. An evaluation was carried out by observing the maximum of Doppler amplitude data of 30,000 Doppler signals in each condition. Whenever a nylon particle was dispersed into the water, the distribution of PDF of Doppler amplitude voltage occurred during 0-0.1 V at \(U_{L}\): 200 mm/s and 300 mm/s. The peak was observed at about 0.04 V. Furthermore, a large number of bubbles at the microscale (diameter <1 mm) were injected into the water without any nylon particles. Clearly, the PDF of Doppler amplitude voltage was distributed between 0.25 V and 0.4 V at both velocity levels of liquid. The peak was observed at about 0.32 V. Hence, it can be concluded that the Doppler amplitude reflected by a particle and bubble is obviously different.  
Figure 11: Measurement results for the single-phase liquid velocity profile: (**a**) \(U_{L}\) = 200 mm/s and (**b**) \(U_{L}\) = 300 mm/s.  
Figure 12: Discrepancy for the single-phase liquid velocity profile: (**a**) \(U_{L}\) = 200 mm/s and (**b**) \(U_{L}\) = 300 mm/s.  
The setting of the threshold value, to precisely separate Doppler amplitude reflected by both reflectors, was done on PDF of Doppler amplitude. Therefore, the threshold was set at about 0.18 V which was the center point between the peak of PDF of particle and bubble. At both velocity levels; \(U_{L}\): 200 mm/s and 300 mm/s, the probability (\(\beta\)) of occurring of bubble data in particle zone (lower than the threshold value) was less than 0.01. Also, the value of particle data that occur within the region of bubble data (Higher than the threshold value) was smaller than 0.01.