esa-sceva/satcom-chunk-collection huggingface dataset:
# ReZNS: Energy and Performance-Optimal Mapping Mechanism for ZNS SSD
## 4 Evaluation
### Energy Efficiency  
Now, let us walk through the energy efficiency using ReZNS. To observe the energy consumption, we quantified individual requests performed inside ZNS SSDs: read, write, and zone-reset commands. Then, we calculated the expected energy costs by multiplying the total requests with the corresponding energy consumption listed in Table 1.  
Figure 11 compares the total estimated energy consumption for eight workloads, where lower is better. As shown in Figure 11, ReZNS delivers similar or better energy costs than the baseline except for the results of Filebench. The reason for the high energy consumption from Filebench is that the benchmark ran for 1 min based on time. This means that ReZNS handles more operations than the baseline at any given time because of its high performance, as shown in Figure 5. As a result, the graph confirms the positive effect of saving energy, which comes from ReZNS. Meanwhile, ReZNS saves on total power consumption by up to 3% compared with the baseline on the YCSB-F workload. This is quite encouraging in that the YCSB benchmark approximately ran for 20 min. When considering that datacenters always turn on and work, we would expect that the energy advantage of ReZNS continuously increases over time.  
To understand why the energy gap in Figure 11 is very close, unlike that of performance, we investigated the details of where the energy is mainly consumed. Figures 12 and 13 show the breakdown of energy consumption in percentage on all workloads on ReZNS and baseline,  
Figure 10: The number of zone-reset commands on YCSB workloads. In this evaluation, each zone is configured with a size of 1 GiB.  
Figure 9: Overall performance of YCSB workloads. In this evaluation, each zone is configured with a size of 1 GiB.  
respectively. In the figures, the portion colored in gray represents the energy consumed by zone-reset commands. As expected, most of the energy is consumed by the read and write operations, whereas zone-reset  drains only a few joules. Meanwhile, as shown in Figure 13, the portion of zone-reset commands is larger than that of ReZNS. To clearly understand the difference, we look into the amount of energy that comes from zone-reset commands (see Figure 14). Unlike the results in Figure 11, ReZNS  shows the best energy efficiency in all workloads. Interestingly, Figure 12 shows that ReZNS  can save on power consumption by up to 64% while running YCSB-B for 20 min. The reason behind this drop is that ReZNS  lets multiple applications recycle the remaining space in each zone instead of performing zone-reset  commands. In summary, the more often the zone-reset  command is triggered after frequent write operations, the more ReZNS  can save energy consumption. This is because ReZNS  absorbs many of the write operations using renewable zones and avoids triggering the zone-reset  commands that require high energy consumption. We believe it becomes more meaningful as the attention to energy efficiency inside the storage devices increases.
# On the Potential of the RST-FLARE Algorithm for Gas Flaring Characterization from Space
## 2 Data and Methods
### Rst-Flare  
RST-FLARE is a specific configuration of the Robust Satellite Techniques approach (RST, [32; 33; 34]), a general automatic change detection scheme widely and successfully applied to monitor the major environmental, natural and industrial hazards (e.g., [35; 36; 37; 38; 39]). In its original version, RST-FLARE was developed for computing the amount of gas annually flared by the COVA plant located in Basilicata region (southern Italy) [20]. A detailed description of this method can be found in [20]; here, the algorithm is briefly presented, providing more information about the new configuration proposed. To study and characterize the gas flaring phenomenon, RST-FLARE requires the implementation of three methodological steps, described in the following.
# GAE and OBE Enhanced Interference Mitigation Techniques in LDACS
## 4 Nonlinear Estimators
### Obe  
Bayesian estimators are useful in any Gaussian source affected by any Gaussian-mixture noise [33]. The time domain OFDM signal \(x\) can be approximated by Gaussian pdf, \(f_{X}(x)=G(x;\sigma_{x}^{2})=\frac{x^{2}/2\sigma_{X}^{2}}{\sqrt{2\pi}\sigma X}\). The complex valued received signal \(y_{t}[n]\) at the receiver side has real and imaginary part \(y_{t,R}[n]\) and \(y_{t,I}[n]\), respectively. Consider that \(y\) represents distinctly either the real or the imaginary part of \(y_{t}[n]\). When the received signal of interest is modeled or approximated as a Gaussian pdf or K-component pdf, the minimum mean square error Bayesian estimators can be effectively utilized along with the knowledge of the signal \(x_{t}\). By exploiting the statistical dependency between \(X\) and \(i\), it is possible to write \(f_{(Y|X)}(y)=f_{i}(y-x)\) and \(G(y;\sigma_{yk}^{2})=G(y;\sigma_{x}^{2})*G(y;\sigma_{k}^{2})\). Here, * stands for convolution operation. Thus the received noise power \(\sigma_{yk}^{2}\) is the sum of the signal power \(\sigma_{x}^{2}\) and \(k^{th}\) Gaussian component noise power \(\sigma_{k}^{2}\)  
\[\sigma_{yk}^{2}=\sigma_{x}^{2}+\sigma_{k}^{2} \tag{21}\]  
Considering all this information, the Bayesian estimated value can be expressed as in (22), which also expresses the dependency of OBE with signal power \(\sigma_{x}^{2}\) and each noise component \(\sigma_{k}^{2}\) along with probability of occurrence of each component (\(p_{k}\)).  
\[\hat{x}_{k}o(y) =E_{X|Y}\{x\}=\frac{E_{X,Y|X\{x\}}}{f_{Y}(y)}=\frac{\Sigma P_{k} E_{x}Y_{k}|X\{x\}}{f_{Y}(y)} \tag{22}\] \[=\frac{\Sigma_{k-0}^{K-1}p_{k}p_{k}G(y;\sigma_{yk}^{2})}{\Sigma_{ k-0}^{K-1}p_{k}G(y;\sigma_{yk}^{2})}\] \[=\beta_{o}(y).y\]where  
\[\beta_{o}(y)=\frac{\Sigma_{k=0}^{K-1}\rho_{k}p_{k}G(y;\sigma_{yk}^{2})}{\Sigma_{k =0}^{K-1}p_{k}G(y;\sigma_{yk}^{2})} \tag{23}\]  
and \(f_{Y}(y)=f_{X}(y)*f_{i}(y)\). From (22), it is clear that the attenuation factor of the Bayesian estimator can be viewed as the weighted sum of \(K\) number of GAE linear attenuators, where the nonlinear weights are \(\frac{p_{k}G(y\sigma_{yk}^{2})}{\Sigma_{k=0}^{K-1}p_{k}G(y\sigma_{yk}^{2})}\). The output expression of the Bayesian estimator (22) can be easily generalized as shown in (24) for complex valued signal operations.  
\[\hat{x}_{k}^{*}o(y)=\frac{\Sigma_{k=0}^{K-1}p_{k}p_{k}G(|y|;\sigma_{yk}^{2})}{ \Sigma_{k=0}^{K-1}p_{k}G(|y|;\sigma_{yk}^{2})}|y|e^{iarg(y)} \tag{24}\]  
where \(y\) is the complex valued signal expressed by \(y=y_{r}+iy_{i}\) and \(|y|\) is the signal envelope.
# On the Feasibility of Using Industrial Urea to Mitigate Thermal and Shrinkage Cracking in Concrete
## 3 Experimental Design  
### Materials  
The industrial urea used in this study comprises white particles with a mean size of 0.5-5 mm (Figure 1). Due to its strong water-soluble property, urea is produced in a globular form encapsulated using polymethyl methacrylate. Its solubility in water can be up to 525 g at 25 \({}^{\circ}\)C, and it reacts with and dissolves immediately in water. In particular, when urea is mixed in concrete, its dissolution performance improves due to the forceful mixing of the mixer. The reaction characteristics that can appear when urea is mixed with cement composites, described above, can be explained by Equations (1) and (2). Table 1 lists the physical and chemical properties of the urea used in the present experiment.  
\[\text{CO(NH}_{2})_{2}+2\text{H}_{2}\text{O}\rightarrow\text{H}_{2}\text{CO}_{ 3}+2\text{NH}_{3}, \tag{1}\]  
\[\text{H}_{2}\text{CO}_{3}+\text{Ca(OH)}_{2}\rightarrow\text{CaCO}_{3}+2\text{H }_{2}\text{O}, \tag{2}\]  
Ordinary Portland cement (OPC, density: 3140 kg/m\({}^{3}\), fineness: 343 m\({}^{2}\)/kg), ground granulated blast-furnace slag (GGBS, density: 2910 kg/m\({}^{3}\), fineness: 438 m\({}^{2}\)/kg; ASTM C989 [26]), and fly ash (FA, density: 2230 kg/m\({}^{3}\), fineness: 372 m\({}^{2}\)/kg; ASTM C618 Class F [27]) were used as binders in this study. In addition, low-heat Portland cement (LPC, density: 3220 kg/m\({}^{3}\), fineness: 366 m\({}^{2}\)/kg; ASTM C150 type IV [28]) was mainly used for lowering the hydration heat when exposing concrete to an extreme temperature or for massive members for performing relative comparisons of different urea mixtures. The chemical compositions of the cementitious materials used in this study are listed in Table 2. The fine and coarse aggregates had a density of 2610 and 2670 kg/m\({}^{3}\), a maximum dimension of 5 and 25 mm, and a water absorption rate of 1.05% and 1.28%, respectively. A superplasticizer from the polycarboxylate series and a viscosity agent from the methylcellulose series were used as agents.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & \begin{tabular}{l} **Chemical** \\ **Formula** \\ \end{tabular} & **Appearance** & \
\begin{tabular}{l} **Density** \\ **(kg/m\({}^{3}\))** \\ \end{tabular} & **pH Value 1** & **Solubility (g)** &
\begin{tabular}{l} **Molecular** \\ **Weight** \\ \end{tabular} \\ \hline \hline Urea & \(\text{CO(NH}_{2})_{2}\) & White sphere & 1320 & 12.2 & 545 & 60.06 \\ \hline \hline \end{tabular}  
* 10% solution at 25 \({}^{\circ}\)C.  
\end{table}
Table 1: Properties of industrial urea [2].  
Figure 1: Industrial urea used in the current experiment.
###### Abstract  
Discriminating a real underwater target echo from a synthetic echo is a key challenge to identifying an underwater target. The structure of an echo envelope contains information which closely relates to the physical parameters of the underwater target, and the characterization and extraction of echo features are problematic issues for active sonar target classification. In this study, firstly, the high-frequency envelope fluctuation of a complex underwater target echo was analyzed, the envelope fluctuation was characterized by the envelope fluctuation intensity, and a characterization model was established. The features of a benchmark model echo were extracted and analyzed by theoretical simulation and sea testing of a scaled model, and the result shows that the envelope fluctuation intensity varies with carrier frequency and azimuth of incident signal, but the echo envelope fluctuation of the synthetic target echo does not present these features. Then, based on the characteristics of echo envelope fluctuation, a novel method was developed for active sonar discrimination of a real underwater target echo from the synthetic echo. Through a sea experiment, the real target echo and synthetic echo were classified by their different echo envelope fluctuations, and the feasibility of the method was verified.  
active sonar; underwater; envelope; envelope fluctuation intensity; synthetic echo; echo discrimination +
Footnote †: journal: Applied Sciences  
1
Footnote 1: School of Naval Architecture, Dalian University of Technology, Dalian 116024, China; [EMAIL_ADDRESS]  
2
Footnote 2: Science and Technology on Underwater Test and Control Laboratory, Dalian 116013, China; [EMAIL_ADDRESS] (B.J.); [EMAIL_ADDRESS] (G.L.); [EMAIL_ADDRESS] (Z.W.)  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-0411-8267-7308 (ext. 803)
## 3 Secretory Profile of Exosomes in Metabolic Tissues
### Adipose Tissue  
Adipose tissue was for many years considered solely a triglyceride-storing tissue; however, in recent decades, it has emerged as the largest endocrine organ in the human body, secreting a wide range of hormones, with leptin and adiponectin being the most prominent [44].  
In patients with obesity, adipose tissue is capable of secreting exosomes containing microRNA cargo, which can induce insulin resistance in the liver and muscle [41]. This mechanism was reported by Wei Ying, who conducted both in vivo and in vitro studies in which exosomes were extracted from adipose tissue macrophages containing miR-223 and were subsequently internalized by adipocytes. Mice treated with these exosomes exhibited glucose intolerance and insulin resistance compared to mice treated with exosomes from lean mice [45].  
Moreover, exosomes produced in adipose tissue are known to modulate insulin sensitivity. In obesity, exosomes contain a higher amount of fatty acids, which could affect cellular signaling, in addition to containing proteins that promote beta-oxidation [46]. For instance, it has been reported that in studies using the 3T3-L1 cell model of hypertrophic adipocytes stimulated with palmitic acid, there is an increased secretion of miR-802-5p, which subsequently leads to a decreased expression of HSP60 in cardiac cells, causing insulin resistance [46; 47].  
Likewise, Liu Y et al. report that exosomes containing microRNAs such as miR-155 and miR-29a, derived from adipose tissue, can be endocytosed by hepatocytes and myocytes. Within these cells, they inhibit the expression of PPAR and PPAR\(\delta\) receptors, thereby contributing to the development of insulin resistance [38; 48].  
It has been observed that adipose tissue releases many exosomes, which are directly involved in the inflammatory response of metabolic syndrome, such as miR-802-5p and miR-34a [41; 43]. In this study, an increase in miR-802-5p in exosomes derived from adipose tissue was demonstrated, promoting insulin resistance in peripheral tissues such as the liver and heart by downregulating Hsp60 protein expression [45; 49]. Finally, in a murine study, miR-132/212 in exosomes derived from adipose tissue exacerbated atherosclerosis by stimulating endothelial cell apoptosis mitigated by melatonin treatment [47; 50].
# Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms
## 2 Materials and Methods  
### Primitive Equation Model Data  
The Mediterranean Forecasting System (MFS) is a hydrodynamic model for the Mediterranean Basin and the Atlantic Ocean off the Strait of Gibraltar [45]. Monthly to 15-minute instantaneous outputs of 3D horizontal currents and sea surface height (SSH), as well as monthly to hourly estimates of 3D temperature and salinity fields are available via the Copernicus Marine Service web portal (Product ID: MEDSEA-ANALYSIS-FORECAST-PHY-006-013). For the present study, we relied on daily outputs of SSH and SST, extracting information within the boundaries of the Mediterranean Basin (30 to 46\({}^{\circ}\) N and \(-\)6 to 37\({}^{\circ}\) E). These fields are provided on a \(1/24^{\circ}\) regular grid and 125 unequally spaced vertical levels. The simulations are based on the NEMO model (Nucleus for European Modelling of the Ocean) used in combination with Wave Watch-III for the wave component. The MFS simulations also account for data-assimilation of 2D satellite-derived SST, salinity vertical profiles, as well as along-track sea-level anomaly observations.
## 3 Results  
We analyze the six anomaly detection methods regarding their overall performance in Section 3.1 and their differences in detecting certain types of anomalies in Section 3.2. Beyond that, we analyze the influence of varying subsequence lengths on MDI and MERLIN, and thus, their ability to utilize additional information about the anomalies in Section 3.3 and compare the point-wise application of RRCF to the raw time series to that on subsequence-wise statistical vectors in Section 3.4.
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{6}{c}{**Daytime Comfort (Hours)**} \\ \cline{2-6}  & **2024** & **2050—4,5** & **2080–4,5** & **2050—8,5** & **2080–8,5** \\ \hline Actual building & 98.39 & 96.54 & 93.09 & 94.12 & 88.13 \\ Case 1: windows & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ Case 2: windows + facades & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ Case 3: windows + facades + roof & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Percentage of daytime thermal comfort hours during the summer period. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{6}{c}{**Nighttime Comfort (\%)**} \\ \cline{2-6}  & **2024** & **2050—4,5** & **2080–4,5** & **2050–8,5** & **2080–8,5** \\ \hline Actual building & 70.00 & 17.10 & 7.58 & 15.81 & 0.97 \\ Case 1: windows & 63.71 & 12.90 & 5.16 & 12.42 & 0.32 \\ Case 2: windows + facades & 66.61 & 15.48 & 5.97 & 14.19 & 0.65 \\ Case 3: windows+ facades + roof & 70.16 & 18.71 & 8.23 & 15.81 & 1.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Percentage of nighttime thermal comfort hours during the summer period. and even minor, thermal upgrades that significantly improve the thermal performance of the building envelope [57]. Even so, a combination of passive and active solutions may be essential to tackle this issue effectively. A detailed analysis of the energy consumption curve would also be necessary to prevent any excessive costs associated with these measures.
# Technological Advancement in Tower-Based Canopy Reflectance Monitoring: The AMSPEC-III System
## 2 Materials and Methods  
### The AMSPEC-III System  
The upgrades implemented in AMSPEC-III were based on previous experience over the past 5 years (_cf._[7; 8]). In order to reduce communication issues in the previous design, the AMSPEC-III consists of a single module mounted atop of the tower and controlled by a relay switch to allow remote power cycling of the system.  
The tower module enables the direct comparison of two different portable spectro-radiometers: a Unispec-DC (UDC; PP-Systems, Amesbury, MA, USA) and a JAZ-COMBO (JC), formed by two JAZ-S and one JAZ-DPU (Ocean Optics, Dunedin, FL, USA), which technical specifications are described in Table 1. The spectro-radiometers were coupled through optical fibers, with an upward-looking sensor featuring a cosine diffuser (PP-Systems) to correct sky irradiance for varying solar altitudes. A webcam (NetCam SC SMP, StarDot, Buena Park, CA, USA) image is automatically acquired simultaneously to spectra being co-registered in order to allow phenological assessment [32] and canopy shading from digital photography. The system features a pan-tilt unit (PTU; PTU-D46-17.5 W, Directed Perception, Burlingame, CA, USA), which allows the sensor head to record data at any view zenith angle (\(\theta_{v}\)) between 43\({}^{\circ}\) and 68\({}^{\circ}\) and at a view azimuth angle (\(\phi_{v}\)) between \(\pm\) 170\({}^{\circ}\) from the initial position for scaling of the observations through modeling of the bidirectional reflectance distribution function (BRDF; [27]) and investigation of foliage clumping effects [33]. In addition, the ability to programmatically define \(\theta_{v}\) and \(\phi_{v}\) permits users to automatically compare tower-based measurements to satellite observations without further modeling of sensor geometries difference, with footprint size on the ground depending on \(\theta_{v}\) and height of installation.  
Communication between computer and spectro-radiometers and between computer and PTU is ensured via serial connections and USB standard for UDC and JC, respectively; the webcam is connected via local area network (LAN). The system can be linked to an external network or mobile communication device to allow remote access to the data.  
Table 2 contains the AMSPEC-III components and their approximate costs at the time of the design of the research. The costs have not significantly changed at the time of writing this manuscript. The total sensor cost is the result of the individual components and labor costs, not included in Table 2. The majority of the costs is associated with the spectro-radiometers, with JC resulting in approximately a third of the UDC cost although requiring a temperature-controlled housing due to higher sensitivity to differences in temperature.
# Corrosion Behavior of Pre-Rusted Rebars in Cement Mortar Exposed to Harsh Environments
## 4 Conclusions  
In this study, the effect of pre-rusting of rebar on the durability of rebar in cement composites was analyzed. Based on the obtained results, the following conclusions on the corrosion behavior of pre-rusted rebar in a cement composite were drawn:  
The pre-rusted rebar underwent 2-3 times faster internal corrosion than the non-pre-rusted rebar when embedded in the reinforced cement composite exposed to a chloride-containing solution.  
Although the HCl solutions had 50% less chloride content than CaCl\({}_{2}\), HCl caused initial corrosion through the mill scale more effectively than CaCl\({}_{2}\), and resulted in the faster corrosion of the rebar in the cement composite. At 120 days, the corrosion rate of HCl was 2.3 times faster than that of CaCl\({}_{2}\) in cement composite.For the specimens considered in this study, the half-cell potential and the corrosion current were proportional when the corrosion current was 1.5 uA or higher. The result of XRD analysis showed that the corrosion products in the pre-rusted layer were similar to those of the rebar corroded in the marine atmosphere.The pre-rusting treatment considered in this study may provide an efficient method for researchers to reduce the time needed to prepare corroded concrete specimens by 50%.  
Conceptualization, C.Y. and H.L.; Data curation, G.B.; Funding acquisition, C.Y. and H.L.; Investigation, G.B.; Project administration, I.J.; Resources, I.J.; Supervision, C.Y.; Validation, I.J.; Visualization, D.S.; Writing--original draft, G.B. and D.S.; Writing--review and editing, C.Y. and H.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant (20 SCIP-B146946-03) from the Smart Civil Infrastructure Research Program funded by the Ministry of Land, Infrastructure, and Transport of the Korean government.  
This work was supported by a Korea University grant.  
The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.