esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### PlanetQi-NWP Models Comparison
#### 4.3.1 PlanetQi-GFS Model Comparison
Specifically, both the Spire and COSMIC-2 datasets exhibit fractional differences within a few percentage points compared to the ERA5 model, suggesting comparable retrieval qualities. Moreover, the statistical analysis underscores the consistent performance of both datasets across different RO event modes and altitude ranges, affirming their reliability and accuracy in atmospheric research and modeling endeavors. Ref. [36] also compared COSMIC-2 radio occultation temperature data with three reanalysis model datasets (ERA5, MERRA-2, and JRA-55) in the context of Hurricane Dorian. They found that biases with respect to ERA5 and the estimated error statistics of the COSMIC-2 observations compared favorably with those of the other datasets. Random errors in COSMIC-2 temperature were estimated to be less than 0.75 K at all levels, including the hurricane environment. These results indicate that COSMIC-2 1D-Var retrieves high-quality temperature soundings, even in extreme weather conditions such as an intense hurricane. In Figure 17, the distribution of the diff.% between the PlanetIQ and GFS profiles at 3 km altitude for various variables is depicted over the study period. Pressure differences remain generally within \(\pm\)0.5%, except for certain land areas in the southern hemisphere where deviations are observed. Across the globe, the diff.% at 3 km altitude for pressure does not exceed \(\pm\)2%. Figure 17: Diff.% contour maps for all variables at altitude 3 km between PlanetIQ and GFS. Temperature differences, given the relatively small values of measurements at 3 km altitude, may exhibit slightly higher diff.% but still generally remain below \(\pm\)2.5%. Notably, temperature diff.% increase around the equator and pole regions, with differences elsewhere not surpassing 0.4%. WVP difference percentage typically is less than \(\pm\)0.5%, except for specific distributed regions where it may reach up to \(\pm\)3%, particularly near the equator. The refractivity difference percentage generally does not exceed \(\pm\)4%, is concentrated around the equator, and tends to decrease moving towards the poles. The analysis reveals that the differences in the aforementioned variables between PlanetIQ and GFS profiles at 3 km altitude are generally within acceptable limits, with occasional exceptions in specific places. The findings provide valuable insights into the performance and agreement of the two datasets, highlighting areas of interest such as the equator and pole regions, where variations are more pronounced. In agreement with the present research findings, Ref. [37] found that the atmospheric refractivity observations from the COSMIC-2 RO mission were evaluated by comparing them with radiosonde observations, the ECMWF ERA5 reanalysis, and the NCEP GFS analysis. The findings indicate that COSMIC-2 refractivity shows good overall agreement with RS observations, ERA5, and GFS analyses, with mean and standard deviations of the differences within acceptable limits.
## 2 Composition, Feature and Utilization of Slag
### Utilization of slag  
#### 2.3.1 Making Cement and Concrete  
Existences of C\({}_{3}\)S, C\({}_{2}\)S and C\({}_{4}\)AF confirm slag's cementitious property. It is generally considered that slag's cementitious property increases with the increase of alkalinity. Thus, the slag grained into fine powder can be used for admixture of cement and concrete [27]. Research showed that [28] cement added to 30% micro-powder slag met Turkey's standard requirements for Portland cement. [PERSON] et al. [29] found that cementitious materials could be produced by the main raw materials of phosphogypsum, steel slag, blast furnace slag and limestone. [PERSON] et al. [30] found that compression strength of concrete added to slag reached 100 MPa and showed excellent resistance to chloride ion penetration. [PERSON] et al. [31] observed that concrete added to grinded electric-arc-furnace slag showed an excellent reduction of water. [PERSON] et al. [32] found that the compression strength of concrete that used slag as a fine aggregate was 1.1-1.3 times higher than ordinary concrete. [PERSON] et al. [33] found that high-strength (>70 MPa) concrete could be produced by electric-arc-furnace slag as an aggregate. Slag can also be used as a raw material for producing Portland cement [5] and Belite cement [34].
## 3 Energy Efficiency Analysis  
**Definition 1**.: _In this paper, the energy efficiency is defined as the ratio of the average achievable rate to BS power consumption in the HCN. The EE indicates the total downlink rate that an HCN can achieve with certain power consumption._  
\[\eta_{EE}=\frac{R_{total}}{P_{total}} \tag{3}\]  
_where \(R_{total}\) is the total average achievable transmission rate and \(P_{total}\) is the total power consumption of BSs. Firstly, we characterize the average achievable transmission rate. Then, we obtain the total power consumption of BSs and formulate the network EE._
# Untargeted 1H-NMR Urine Metabolomic Analysis of Preterm Infants with Neonatal Sepsis
## 3 Results
### Metabolic Profile Alterations of Pretems with LOS  
#### 3.2.1 LOS Preterms versus Non NICU Healthy Preterms  
Spectroscopic data of urine samples collected on the third day of life from eleven (n = 11) preterm neonates that developed LOS were compared to the third day's \({}^{1}\)H NMR urine profile of twelve (n = 12) non-NICU healthy preterms. The unsupervised PCA, explaining the 75.1% of the total variability within the first three components (PCI = 43.3%, PC2 = 18.2% and PC3 = 13.6%) indicated a clear tendency for clustering the two groups. Loadings with the greatest impact caused this form of PCA plot, agreed with EOS results and reinforced the claim of the great impact of hospitalization in NICU on urine metabolome (Supplementary materials Figure S5). The supervised PLS-DA, after the cross-validation resampling method, present a reliable and predictable model with R\({}^{2}\) = 0.861 and Q\({}^{2}\) = 0.788 for the second component (Figure 5a). As expected, VIP scores highlighted gluconate and the pattern of 1.40, 7.40-7.50 ppm (Figure 5b), with significant alterations of normalized concentration among groups, clearly displayed on the box plots (Figure 5c). Multivariate analysis of LOS metabolic profile reveals similar metabolic alterations with EOS. This analysis did not add something different compared to the analysis about EOS and healthy non-NICU preterms, and it is primary evidence that the septic profile does not dramatically change in relation to time within the first three days of neonate's life.
## 3 Results and Discussion
### Fouling and Rejection Test
The separation performances of GO- and rGO-blended membranes were then investigated by using the synthetic raw water containing kaolin and alginate (Table 1). The feed water had a turbidity of 250 NTU, it contained TDS and TOC of 16 and 10.8 mg/L, respectively, and UVA\({}_{254}\) (UV absorbance at 254 nm wavelength) was 1.057. All experiments were performed at 20 \({}^{\circ}\)C and pH 6.7 \(\sim\) 7.0. Compared with the blank PSf membrane, the removal efficiency was increased when using the membrane that contained the optimum amount and type of graphene-based nanomaterials: the GO-blended PSf membrane with a GO content of 0.10 wt% showed the best performance with respect to its flux increase, antifouling effect, and rejection efficiency. For rGO, the removal rate was the best at 0.10 wt% rGO, but its efficiency was lower than that of the GO membrane. These results provide further evidence that the use of GO-blending membranes would facilitate the application of graphene-based materials in water treatment processes. Figure 7: Membrane resistance of the PSf/GO membrane.
## 3 Potential Applications in Biological Studies
### Bioactive Urethanes and Derivatives  
Carbamates or urethanes have attracted much attention due to their biological relevance as mimic fragments of pharmaceutical compounds, such as efavirenz [138] and retigabine (Figure 7) [139]. In other respects, urethanes are monomers that are largely used in the synthesis of polyurethane, one of the most used raw materials in global industry [10]. Polyurethanes are also versatile thermoplastic elastomers employed in the preparation of biocompatible components, including artificial hearts, feeding tubes, catheters, surgical drains, and others [140].  
In 2012, [PERSON] and co-workers [141] reported the synthesis of a branched cationic polyurethane **134** via the polycondensation of 4-chloromethyl-1,3-dioxolan-2-one **1o** with diethylenetriamine (DETA) **63d** in ionic liquid as the solvent (Scheme 65). The reaction of **1o** and DETA **63d** was achieved using a 2:1 molar ratio for the monomers. When carried out in molten imidazolium salts, the polymerization yields were significantly higher than those obtained using conventional solvents. The authors reported that due to its ammonium group, the polymer can serve as a catalyst and a capsule for DNA, whereby it can be used as a gene delivery carrier. The complexation with DNA was confirmed by dynamic light scattering (DLS) experiments.  
Figure 7: Structures of efavirenz (component of first-line antiretroviral therapy) [138] and retigabine (anticonvulsant medication) [139].  
By reacting primary amines **63e**, alkyl halides, and diverse carbonates **1** bearing cationic, hydrophobic, or amphiphilic groups, [PERSON] and co-workers [142] prepared poly(ethyleneimine)s (PEI) and amphiphilic monodisperse compounds (Scheme 66). These compounds were assayed for their ability to inhibit the proliferation of microorganisms (_E. coli_, _S. aureus_, and _B. subtilis_) and for their hemolytic activity, which was influenced by the microstructure and length of the PEI alkyl chain. The results led to the conclusion that the microstructure of the amphiphilic monodisperse compounds has a significant influence on their antibacterial properties and the hemolytic activity. Compounds with alkyl chains directly linked to cationic groups have high antibacterial efficiency for long (C-14 to C-18) alkyl chains.
###### Abstract  
Consumption of produce contaminated with pathogens of fecal origin is the most common source of food borne illnesses. Current practice is to visually survey fields for evidence of fecal contamination, and to exclude problematic areas from harvest. Bird excrement is known to contain human pathogens, and is often not detectable in produce fields using current survey methods. The goal of this project was to identify parameters for optical detection of bird excrements to support development of instruments to be used to supplement existing visual surveys. Under daylight ambient conditions, results suggested that reflectance imaging at around 500-530 nm or 610-640 nm could be used to detect excrements from the three bird species tested. Images were acquired using ad hoc camera parameters; however, normalizing intensities for individual images at 525 nm and using a fixed detection threshold allowed detection of 100% of bird excrements with no false positives against the background that consisted of local soil and fresh romaine and spinach leaves. Similar results were obtained using fluorescence imaging. Fluorescent imaging was accomplished in a darkened room using 405-nm illumination. The largest consistent differences in intensity responses between excrements and the brightest non-excrement object in the background matrix occurred at around 550 nm. Results suggested that using reflectance or fluorescence imaging for detection of bird excrements could be a valuable tool for reducing risks of consuming contaminated produce. One possibility would be to incorporate appropriate reflectance imaging capabilities in drones under the control of the individuals currently conducting field surveys.  
Keywords:birds; hyperspectral imaging; imaging; food safety; leafy greens; fecal matter +
Footnote †: journal: Applied Physics Letters
## 2 3D Microstructure Generation  
To generate microstructures for the composite material, our algorithm utilizes a technique whereby particles, or inclusions, are randomly placed within cubic cells that represent elementary volumes in a three-dimensional space. This iterative process continues until a predetermined volume fraction is reached. Initially, a single particle is generated. Subsequent particles are then randomly placed in the matrix while adhering to a minimum repulsion distance between particles and a minimum relative volume compared to existing particles to avoid overlap. Once the specified conditions are met, the algorithm calculates the new volume fraction. If the target volume fraction is not achieved, the process of adding new particles is repeated, and the algorithm continues until the desired volume fraction is obtained. In this study, the number of inclusions (\(N\)) is set to 200 spherical inclusions and considers four distinct volume fractions: 10%, 15%, 20%, and 25%. It is noted that exceeding a volume fraction of 26% results in particle overlap.  
The spherical inclusions within the composite material have diameters that are dependent on the set volume fraction and the total number of inclusions. The diameter of these inclusions is calculated using the following formula:  
\[D=\sqrt[3]{\frac{6V_{f}a^{3}}{\pi N}} \tag{1}\]  
where:  
\(V_{f}\) is the volume fraction and \(a\) is the side of the cube.  
This technique allows for the creation of a range of composite material structures with varying microstructures that can be used to estimate the effective elastic and thermal properties of the composite material.  
Figure 1 shows four microstructures generated by varying the volume fraction of inclusions.  
Continuing with our research, we have successfully generated a range of representative microstructures tailored to our composite material. We now transition to a crucial phase of our analysis: evaluating the linear elastic and thermal characteristics of these microstruc  
Figure 1: Example of generated microstructures using different volume fractions: (**a**) \(V_{f}\) = 10%, (**b**) \(V_{f}\) = 15%, (**c**) \(V_{f}\) = 20%, and (**d**) \(V_{f}\) = 25%.  
tures. To achieve this, we will employ finite element analysis (FEA), a method renowned for its effectiveness and that is widely used in the field of materials modeling. This approach will enable us to comprehensively assess the material's response under various conditions and will provide vital insights into its mechanical and thermal properties.
## 5 Performance Evaluation
we can confirm that our introduced scheme will provide a lower task completion time. This is because our proposed collaborative approach can distribute the arrival user requests among SBS-MEC servers, and it can improve the system performance to handle a large number of mobile devices. Finally, the last simulation result shows the impact of the VM number on the SBS-MEC server. In this simulation, we assigned eight VMs to the extremely powerful MEC server, four VMs to the least powerful one, and six VMs to the other. From Figure 10a,b, it can be observed that the average task completion time tends to reduce as the number of VM increases. Compared to the WOTO scheme, the proposed FCTO scheme outperforms among all the scenarios. For example, when the number of the mobile devices is 700 and the number of VMs is four per SBS-MEC server, the average task completion time is 3.07 s in the WOTO scheme. In the same scenario, the average task completion time is 1.17s in our proposed FCTO scheme. Thus, the proposed FCTO scheme reduces the task completion time by 1.9 s when compared with the WOTO scheme. Throughout the analysis, as observed from Figure 10a,b, the proposed FCTO scheme with four VMs can alleviate the average task duration at almost 60.52% more than the WOTO scheme. Furthermore, in Figure 10c,d, we performed another experiments to investigate the effect of SBS-MEC server capacity for the task failure rate and server utilization by varying the number of mobile devices. From Figure 10c, it can be observed that the average task failure rate tends to increases as the number of mobile device increases. The SBS-MEC server while considering four VMs, increases the task failure rate after 200 mobile devices, whereas, with six VMs, it increases the task failure rate after 500 mobile devices, and the SBS-MEC server with eight VMs can handle more than 700 mobile devices without task failure. From Figure 10d, it can be seen that the server utilization tends to increase if the number of mobile devices is also increased; however, if the number of VMs is increased, the server utilization will be decreased. When the number of mobile device is between 100 and 500, the SBS-MEC server with four VMs utilizes more servers than others. However, when the number of mobile device is more than 500, server utilization is a little lower than the six VMs because of increasing task failure rate. Therefore, it can be summarized that, when the number of VMs is higher, the average task completion time is reduced, and it can handle more mobile devices. This result is expected and the main motivation of this simulation is to explain the necessity of the collaborative task offloading scheme. Figure 9: Performance analysis: (**a**) average task failure rate versus number of mobile devices; (**b**) average task completion time with respect to the different numbers of mobile devices.
# Analysis of Wind Speed Characteristics Along a High-Speed Railway
## 4 Predictability Analysis
### Predictability of Wind Speed  
The characteristics of historical wind speed data provide an indication of the predictability of wind speed at any given moment. The reference criterion (1.63, at a significance level of 0.01) and [PERSON]'s test statistic were compared. If the test statistic exceeded 1.63, the wind speed was deemed predictable. Conversely, if the \(p\)-value surpassed the significance level of 0.01 (denoted in red), the wind speed was considered unpredictable, indicating a 1% chance of erroneously concluding a difference from white noise in the absence of white noise. This criterion allows for the assessment of historical wind speed. Wind speed becomes unpredictable when insufficient historical data are available. As the quantity of historical data increases, [PERSON]'s test statistic rises, indicating greater predictability of wind speed. As shown in Figure 9, the four RS wind speeds initially exhibited unpredictability (sampling frequency = 1 s, implying a 1 s interval between wind speed series), but as the number of historical data points increased, the statistical properties steadily improved. However, this trend does not continue indefinitely; an excess of historical data can degrade the statistical properties. For example, K1005 and K1245 lose their predictive capability when provided with 45 and 47 h of historical data, respectively. The maximum duration of historical data that can be predicted is 37 h (45 h-8 h).  
This study investigates whether the wind speed series, generated by adjusting the sample time, is predictable. To standardize the wind speeds at the RSs and WSs, we sampled the wind speed at the station every 5 min to produce a 5 min average wind speed sequence. The predictability of the RSs and WSs for the 5 min samples is shown in Figure 10. The four dotted lines represent the changes in the [PERSON]'s test statistic for each of the four WSs over time, as measured by historical wind speed. The data gradually increased from 12 h, peaked around 48 h, and then rapidly declined, tending toward zero after 60 h. In contrast, the predictability of the four RSs exhibited different patterns. Except for K1256, whose predicted historical wind speed extends beyond 36 h, the predictability of the other RSs was lower than 36 h. The [PERSON]'s test statistics for the four RSs were significantly  
Figure 8: NCPs of RS wind speed (sampling time = 1 s).  
smaller compared to those for the 1 s sample time results shown in Figure 10, and the duration of predictable historical wind speeds was reduced by more than half.
# 5G-MEC Testbeds for V2X Applications
## 3 Existing Testbeds and Implementations
### 5G-Mec V2X Testbeds
#### 3.3.3 5G Carmen  
The 5G Carmen testbed [54] involves the integration of different MEC systems and was developed in three countries: Italy, Germany, and Austria. The MEC platform in the 5G Carmen testbed consists of three interfaces: a connection to a Packet Gateway (PGW) via an SGi-interface, a public IP address that is exposed to other MEC platforms, and VPN interfaces for platform management functions and applications that are deployed on the platform by third parties. The setup in Germany includes the Deutsche Telekom integration and operation of the LTE/5G MEC system. The Nokia infrastructure provides the MEC platform, local edge processing capabilities, and the latency-optimized access path for LTE latency and reliability-sensitive applications. The MEC Edge cloud infrastructure is based upon the Nokia Airframe hardware, Ubuntu Linux and KVM virtualization, and the Application Life Cycle Management [55]. The setup in Germany deploys the BMW Cooperative Maneuvering Application. TIM proposed a setup in Italy, where Nokia provides the MEC system solution and the AMQP broker platform is provided by TIM. Some MEC platforms and application entities are deployed in virtual machines, whereas the remainder are realized in containers. A container-based service mechanism is used for life cycle management services. The application orchestration is provided via Helm charts. The VM orchestration in the MEC platform is performed using Nokia CBAM. The setup in Austria is managed and operated by Magenta Telekom Austria (MTA) with the MEC environment located within their network infrastructure. The setup is based on the OpenStack Red Hat Release 13 that can host multiple independent tenants. Currently, the setup is hosting the onboarding of MobiledgeX, which provides a low-latency computing platform.
## 2 Background
Furthermore, a hybrid approach can be adopted for specific application requirements by combining multiple scheduling techniques. In a dense network, heavy non-periodic traffic causes network congestion, leading to packet loss. In such scenarios, delivering packets to their destination within a deterministic time becomes more challenging. While retransmissions can address packet failures, they also increase end-to-end latency [13]. Consequently, an adaptive SF is becoming more appropriate for managing data packets based on their criticality level and efficiently allocating resources in multi-hop networks.
## 2 Upper-Level Controller
### Nonlinear Model-Predictive Control  
NMPC can handle constraints explicitly, which comes from its model-based prediction of the future dynamic behavior of the system, by adding constraints to future inputs, outputs, or state variables, which can be represented explicitly in a nonlinear programming problem solved online. The NMPC closed-loop procedure is shown in Figure 1. The NMPC controller obtains the vehicle states and positioning by fusing the vehicle's sensor information. At the same time, a path planning module gives a reference path based on the same information. The controller predicts the future state and calculates the error against the reference path. The controller obtains a group of control outputs by solving optimally under the constraints. The first group of control outputs is employed in the vehicle. After that, new states are obtained, and the subsequent control is performed.