esa-sceva/satcom-chunk-collection huggingface dataset:
# Partial Pre-Emphasis for Pluggable 400 G Short-Reach Coherent Systems
## 4 Experimental Setup and DSP  
Figure 6 shows the experimental setup used to evaluate the impact of partial pre-emphasis. First, 16 QAM symbols are generated in the Tx DSP and then resampled to the AWG sampling rate at 64 GSa/s for pulse shaping via the root-raised-cosine (RRC) matched filter. Next, a pre-emphasis filter is applied to the signal to pre-compensate for the frequency response of the transmitter. The filter taps are obtained from a T/2-spaced feedforward equalizer that compensates for the whole transmitter frequency response including modulator. Note that in the experiment, we reduce the peaking of the pre-emphasis filter to different levels to achieve partial pre-emphasis of the Tx. Then, the signal is quantized and fed to the AWG memory for digital-to-analog conversion. The output RF signals from the two-channel AWG are amplified by a pair of RF amplifiers with a 3 dB bandwidth of 42 GHz and then modulated onto the optical carrier at 1550.12 nm through an In-phase/Quadrature modulator (IQM). In the setup, a LiNbO\({}_{3}\) IQM with a 3 dB E-O bandwidth of 15 GHz is used to emulate a low bandwidth SiPh modulator. The power of the optical source was 15.5 dBm and LO power was set to 12 dBm. The launch power into the fiber was around -9 dBm for 50 Gbaud signal. After transmission over 80 km of standard single-mode fiber (SSMF), the optical signal is pre-amplified by erbium-doped fiber amplifier (EDFA) with a noise figure of 5 dB and received by the coherent receiver (CRX). The CRX consists of an optical hybrid followed by two balanced photodetectors (BPD) with no inline transimpedance amplifier (TIA). The balanced photodetectors have a 3 dB bandwidth  
Figure 4: PAPR vs. Tx loss at 20 GHz.  
Figure 5: Signal signal-to-noise ratio (SNR) vs. Tx loss at 20 GHz.  
of 45 GHz. An 8-bit real-time oscilloscope (RTO) operating at 160 GSps and 62 GHz bandwidth is used to capture the data for subsequent offline signal processing. An ASE source and variable optical attenuator (VOA) is used at the receiver to control the received signal optical signal-to-noise ratio (OSNR). The sampled signal from RTO is stored and processed following common coherent DSP algorithms, which includes resampling, frequency offset compensation, dispersion compensation, RRC matched filtering, and phase locked-loop (PLL)-interleaved equalization to compensate for both the ISI and the phase noise. Finally, the symbols are determined and decoded for bit-error rate (BER) calculation.
## 4 Experimental Results
### Dataset and Experimental Setup
During the training phase, images are randomly cropped to \(224\times 224\times 3\) to meet the input requirements of ResNet-34, with data augmentation applied. In the model evaluation phase, the center of the \(256\times 256\times 3\) image is cropped to \(224\times 224\times 3\) as the model input, ensuring a fair comparison. The MORPH-2 dataset is randomly split into 80% for training and 20% for testing, with all models trained on the MORPH-2 dataset; the dataset division is shown in Table 2. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Age Group** & **16–20** & **21–25** & **26–30** & **31–35** & **36–40** & **41–45** & **46–50** & **51–55** & **56–60** & **61–65** & **66–70** \\ \hline
**Training Set** & 7376 & 7189 & 5748 & 6477 & 6965 & 6914 & 4147 & 2903 & 2000 & 890 & 288 \\
**Testing Set** & 820 & 800 & 650 & 780 & 750 & 790 & 380 & 180 & 70 & 30 & 20 \\ \hline
**Total** & 8196 & 7989 & 6398 & 7257 & 7715 & 7704 & 4527 & 3083 & 2070 & 920 & 308 \\ \hline \hline \end{tabular} Note: The table shows the distribution of age groups across the training and testing sets.  
\end{table} 
Table 2: In the MORPH-2 dataset used for training, 77% are African, 19% are European, and the remaining 4% include Hispanic, Asian, Indian, and other ethnicities. The dataset includes 44,692 male images and 10,916 female images. The dataset is divided into 80% for the training set and 20% for the validation set. Figure 4: Examples of partially occluded images are shown. In the actual experiments, 10 different types of sunglasses and 10 types of face masks were used.
###### Abstract  
The gas-phase reaction between _trans_-2-methyl-2-butenal and chlorine (Cl) atoms has been studied in a simulation chamber at 298 \(\pm\) 2 K and 760 \(\pm\) 5 Torr of air under free-NO\({}_{x}\) conditions. The rate coefficient of this reaction was determined as \(k\) = (2.45 \(\pm\) 0.32) \(\times\) 10\({}^{-10}\) cm\({}^{3}\) molecule\({}^{-1}\) s\({}^{-1}\) by using a relative method and Fourier transform infrared spectroscopy. In addition to this technique, gas chromatography coupled to mass spectrometry and proton transfer time-of-flight mass spectrometry were used to detect and monitor the time evolution of the gas-phase reaction products. The major primary reaction product from the addition of Cl to the C-3 of _trans_-2-methyl-2-butenal was 3-chloro-2-butanone, with a molar yield (Y\({}_{\text{Prod}}\)) of (52.5 \(\pm\) 7.3)%. Acetaldehyde (Y = (40.8 \(\pm\) 0.6)%) and HCl were also identified, indicating that the H-abstraction by Cl from the aldehyde group is a reaction pathway as well. Secondary organic aerosol (SOA) formation was investigated by using a fast mobility particle size spectrometer. The SOA yield in the Cl + _trans_-2-methyl-2-butenal reaction is reported to be lower than 2.4%, thus its impact can be considered negligible. The atmospheric importance of the titled reaction is similar to the corresponding OH reaction in areas with high Cl concentration.  
Cl atom; branched unsaturated aldehyde; kinetics; gas-phase products; secondary organic aerosol (SOA) Article
## 3 Results and Discussion  
### Heavy Metal Concentrations in Water and Reservoir Sediments  
Metals concentrations in the Crisul Negru water and sediments are shown in the Table 3. Average levels of Zn, Cr, Pb, Cu, and Cd in the sediments were 66.46 mg/kg, 35.95 mg/kg, 30.40 mg/kg, 19.51 mg/kg, and 2.5 mg/kg, respectively. The abundance of metals in the sediments varied in the following sequence: Zn > Cr > Pb > Cu > Cd.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Heavy Metal** & **Water (mg/dm\({}^{3}\))** & **Sediment (mg/kg Dry Weight)** \\ \hline Cd & \textless{}0.001 & 2.5; 2.5–2.5 \\ Cr & \textless{}0.01 & 35.95; 27.75–42.37 \\ Cu & \textless{}0.05 & 19.51; 17.91–21.53 \\ Pb & \textless{}0.003 & 30.40; 24.48–33.73 \\ Zn & \textless{}0.05 & 66.46; 61.10–69.50 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Concentration of trace elements (mean; range) in sediments (mg/kg dry weight).
## 3 Experimental Result and Accuracy Evaluation
### Validation with Model Simulation  
The CAMS EGG4 dataset provides globally continuous spatiotemporal XCO2 data obtained by data assimilation and model simulations. It serves as a valuable supplementary dataset for understanding atmospheric CO\({}_{2}\) concentrations in regions where precise ground-based measurements and satellite XCO2 observations are not available. According to the official validation report [41] (p. 6), CAMS XCO2 showed very good agreement across all sites (\(\pm 1\%\)) compared to TCCON, with a monthly mean difference of \(\pm 4\) ppm. There is a seasonal pattern of deviations, with maximum deviations up to 10 ppm in summer.  
Comparing the 2015-2020 XCO2 values reconstructed in this paper with the CAMS XCO2 data, it can be seen that there is a consistent temporal trend between the CNN-STK XCO2 and CAMS XCO2 (Figure 5). From 2015 to 2019, the two datasets show close agreement, with an average deviation of approximately 0.18 ppm. In contrast to the CAMS XCO2, the CNN-STK model underestimates in all summers but generally overestimates in the other seasons. After June 2019, the CNN-STK XCO2 estimates are consistently lower than CAMS XCO2, although the trend aligns with previous years. Furthermore, the results of Spearman's rank correlation coefficient (\(\rho\)) between the two datasets reveal positive correlations in summer and negative correlations in autumn, though these correlations are both weak. The significant differences emerge from nonparametric tests simultaneously (Section S3 in the Supplementary Materials). We specifically discussed the uncertainty of the CNN-STK XCO2 dataset relative to CAMS-XCO2 in Section 4.2.1.  
Figure 4: Accuracy validation results of CNN-STK model and comparison models. (**a**) CNN-STK model; (**b**) CNN model; (**c**) RF-STK model; (**d**) RF model.  
Figure 5: Comparisons between CNN-STK XCO2 and CAMS XCO2.
### Hydration of Blended Recycled Glass Powder in CEM II/A-LL Slag Cements  
It is proposed that a new blended cement formulation may be produced by incorporating recycled glass powder, blended with ground granulated blast furnace slag to produce a novel industrial pozzolan. This in turn would be blended with 'common cements'--in this case, Portland-limestone cement to produce a modified CEM II/A-LL type binder. CEM II/A-LL was chosen as the most tightly constrained limestone cement composition in order to limit chemical variations in this study and in ongoing experiments.  
In terms of cement classification, this is a quaternary cement. The objective is to conserve cementitious slag by its partial replacement with recycled glass powder. This study examines the likely consequence of doing so by the thermodynamic modelling of both phase evolution and solution chemistry. This mixed RCGP-GGBS powder (known here as the 'Development Blend') comprises 85% GGBS and 15% recycled glass powder, RCGP.  
Thermodynamic modelling was used to compare the hydration chemistry of four formulations:  
1. A control sample comprising 50% GGBS and 50% CEM II/A-LL (all quantities by mass) but without RCGP.
2. A cement paste containing 60% CEM II/A-LL combined with 40% of the RCGP-GGBS development blend.
3. A cement paste containing 40% CEM II/A-LL combined with 60% of the RCGP-GGBS development blend.
4. 'Standard' Portland-Limestone cement (CEM II/A-LL) containing 10.48% CaCO\({}_{3}\) (measured and supplied by Ecoem France, Dunkirk works).  
The control mix is typical of that used in one of the national markets served by the cement manufacturer Ecoceem.
# Wound Dressing Materials: Bridging Material Science and Clinical Practice
## 4 Antimicrobial Dressings
### Iodine-Based Wound Dressings  
Iodine is well known for its ability to reduce microbial load in chronic wounds [81]. In clinical settings, it is primarily used in two forms: poyidone-iodine and cadexomer iodine [81]. Povidone-iodine is highly effective against a broad range of pathogens, including bacteria, viruses, fungi, spores, protozoa, and amoebic cysts [82]. Iodine potent antimicrobial effects arise from oxidative reactions with amino (-NH2) and thiol (-SH) groups in amino acids, as well as with carbon-carbon double bonds in fatty acids. These reactions lead to quick damage to the structure and function of bacterial and fungal cells, preventing their growth and survival [83]. Beyond its antimicrobial action, poyidone-iodine also possesses anti-inflammatory properties, as it neutralizes reactive oxygen species [83; 84].  
Cadexomer iodine dressings work through a dual mechanism: absorbing exudates and releasing iodine in a controlled manner. The absorbent cadexomer matrix soaks up wound exudates, forming a gel that aids in wound debridement. Simultaneously, as the dressing absorbs fluid, iodine is gradually released, providing sustained antimicrobial activity against bacteria, fungi, viruses, and yeasts while minimizing iodine toxicity. The gel also helps clean the wound bed by removing debris and bacteria during dressing changes. Additionally, iodine's anti-inflammatory effects reduce inflammation, promoting the healing process. By absorbing exudates and debris, these dressings help decrease wound size and depth, further facilitating recovery [77].  
In comparison to silver, iodine is less harmful to cells and more affordable, making it a practical preventative choice for wounds with a high risk of infection [75]. Iodine compounds, such as cadexomer iodine and poyidone-iodine, can be incorporated into occlusive dressings like hydrocolloids (e.g., lodosorb(tm)) and hydrogels (e.g., lodoflex(tm)) [79].  
Despite these advantages, iodine-based dressings may lead to systemic iodine absorption, potentially resulting in thyroid dysfunction. As a result, they are not recommended for use in children, pregnant or breastfeeding women, or individuals with a history of thyroid issues or iodine sensitivity [75].
## 4 Results
### Drag Sail  
Considering the drag sail, it is possible to plot the predicted decay time as a function of the altitude for different ratios of the sail device mass to the spacecraft total mass (Figure 12). The behavior is exactly the same of the natural decay with a simple boost of effectiveness. As already said, due to the exponential behavior of atmospheric density, the decay time becomes too long at higher altitudes. A relatively light system can shift the 25-year limit to over 1000 km. In order to further decrease the decay time, it is necessary to increase the drag sail area, and consequently mass. However, technical feasibility and operational reliability become more and more doubtful. In this case, a chemical propulsion system or a combination of a drag sail with the satellite propulsion system (often already present for other mobility needs) seems to represent a better choice.  
Sometimes, it is argued that the reduction in the decay time of a drag sail is proportional to its area, and that consequently the probability of impact remains unchanged. This is formally correct, but some aspects should be considered:  
1. The impact of small debris with the sail has probably fewer effects and consequences than an impact with the satellite;
2. In case of cooperative systems, a collision avoidance process can occur [49]. This maneuver is based on an uncertainty area of impact that is much larger than the satellite itself and is thus not a linear (but rather sublinear) function of the satellite  
Figure 11: Altitude vs. time for an elliptical orbit (200 \(\times\) 700 km) and a circular orbit (316 km).  
Figure 12: Decay time as a function of altitude for three different drag sail mass fractions: (**a**) lower altitudes; (**b**) higher altitudes, with black line being the 25-year limit.  
area. So, while the probability of passive impact remains constant, the probability of a collision avoidance maneuver by a third party is definitely reduced as the falling time is shortened;
3. It is much easier and cheaper to track a large falling satellite for a shorter time (with the sail) than its original, way smaller, counterpart for a much longer time. Thus, the drag sail remains an interesting option for deorbiting purposes. For an elliptical orbit with a low perigee (perhaps as a consequence of an active partial deorbit with a chemical system, as shown in Section 4.7 about combined solutions), we think it is possible that the drag sail could be opened near the perigee to exploit the region of peak drag, and closed in the rest of the orbit where this is little effective. In this way, the probability of impact is widely reduced. However, this solution is probably not worth the added complexity and risks of having an active solar sail be deployed and closed repeatedly once every orbit.
# Rock Emissivity Measurement for Infrared Thermography Engineering Geological Applications
###### Abstract  
Infrared thermography is a growing technology in the engineering geological field both for the remote survey of rock masses and as a laboratory tool for the non-destructive characterization of intact rock. In this latter case, its utility can be found either from a qualitative point of view, highlighting thermal contrasts on the rock surface, or from a quantitative point of view, involving the study of the surface temperature variations. Since the surface temperature of an object is proportional to its emissivity, the knowledge of this last value is crucial for the correct calibration of the instrument and for the achievement of reliable thermal outcomes. Although rock emissivity can be measured according to specific procedures, there is not always the time or possibility to carry out such measurements. Therefore, referring to reliable literature values is useful. In this frame, this paper aims at providing reference emissivity values belonging to 15 rock types among sedimentary, igneous and metamorphic categories, which underwent laboratory emissivity estimation by employing a high-sensitivity thermal camera. The results show that rocks can be defined as \"emitters\", with emissivity generally ranging from 0.89 to 0.99. Such variability arises from both their intrinsic properties, such as the presence of pores and the different thermal behavior of minerals, and the surface conditions, such as polishing treatments for ornamental stones. The resulting emissivity values are reported and commented on herein for each different studied lithology, thus providing not only a reference dataset for practical use, but also laying the foundation for further scientific studies, also aimed at widening the rock aspects to investigate through IRT.  
infrared thermography; rock; emissivity; laboratory test; surface temperature Article
## 3 Results and Discussion
### Effect of Lap Rate on Forming Stability  
#### 3.2.1 Effect of Overlap Rate on Forming Stability  
As shown in Figure 8, the overlap rate (\(\delta\)) decreases as \(D\) increases. When \(D\) is 0.6 mm and 0.8 mm, the overlap joint of each coating layer is extremely obvious with the stacking phenomenon and forms the inclined surface. This phenomenon is the opposite direction of the sequence of cladding. There is an overlap gap between the channels instead of the inclined surface when the center distance is 1.2 mm and 1.4 mm. Overlap is an important reference parameter for the center distance; when \(\delta\) is greater than 20% and close to 20%, it is easy to achieve a flat cladding surface [33; 35]. When \(D\) is 1.0 and 1.2 mm, the surface flatness of the deposition layer is better. When \(D\) is 1.0 mm, the overlap rate \(\delta_{1.0}\) is 32.43%. When \(D\) is 1.2 mm, the overlap rate \(\delta_{1.2}\) is 15.49. Both \(\delta_{1.0}\) and \(\delta_{1
## 4 Effect of Depth of Semi-Ellipsoidal Notch on Wire Life  
Semi-ellipsoidal notches with different depths are used to investigate the influence of depth of machine-cut notches on the corrosion fatigue performance of steel wire. The depths are taken as 0.6 mm, 0.8 mm, 1.0 mm, 1.1 mm and 1.2 mm, respectively. The widths of semi-ellipsoidal notches are all 4 mm. The schematic diagram of steel wire and corresponding wire specimen with different semi-ellipsoidal notches are shown in Figure 15a,b.  
Table 5 shows the life of steel wires with semi-ellipsoidal notches of different depths. It can be seen that the life of steel wire decreases obviously with the increase of the depth of the semi-ellipsoidal notch. When the notch depth increases from 0.6 mm to 0.8 mm, the life of the steel wire decreases sharply. When the notch depth continues to increase from 0.8 mm, the influence of depth on the life is still significant, but not as significant as when the depth increases from 0.6 mm to 0.8 mm.  
Figure 14: Logarithmic S-N curves of steel wire with machine-cut notch subjected to the fatigue load and sodium chloride solution.  
Figure 15: Wire specimens and schematic diagram of steel wire with different semi-ellipsoidal notches of different widths.  
Figure 16 shows the Logarithmic S-N curve of steel wire with different semi-ellipsoidal notches, which are the power exponential curves obtained by MATLAB fitting toolbox. When the stress range is 720 MPa and the depth of the semi-ellipsoidal notch increases from 0.6 mm to 0.8 mm, the service life is reduced by 25%. When the depth of the semi-ellipsoidal notch increases from 0.6 mm to 1.2 mm, the service life is reduced by 49%. When the stress range is 600 MPa and the depth of the semi-ellipsoidal notch increases from 0.6 mm to 0.8 mm, the service life is reduced by 67%. When the depth of the semi-ellipsoidal notch increases from 0.6 mm to 1.2 mm, the service life is reduced by 78.8%. When the stress range is 480 MPa and the depth of the semi-ellipsoidal notch increases from 0.6 mm to 0.8 mm, the service life is reduced by 53%. When the depth of the semi-ellipsoidal notch increases from 0.6 mm to 1.2 mm, the service life is reduced by 77.3%. When the stress range is 360 MPa and the depth of the semi-ellipsoidal notch increases from 0.6 mm to 0.8 mm, the service life is reduced by 50.7%. When the depth of the semi-ellipsoidal notch increases from 0.6 mm to 1.2 mm, the service life is reduced by 63.7%. It can be seen that under each stress range when the depth of machine-cut notch increases from 0.6 mm to 0.8 mm, the decline of wire life is the highest and the life falls by more than 25%; when the depth of semi-ellipsoidal notch increases from 0.6 mm to 1.2 mm, the life falls by more than half.
Customized Millimeter Wave Channel Model for Enhancement of Next-Generation UAV-Aided Internet of Things Networks  
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vehicles (UAVs) that can be easily controlled and relocated to achieve LoS connectivity and enhance coverage and throughput [9]. Such an approach coincides with the vision of 6G, which is expected to integrate aerial elements, such as UAVs, with terrestrial networks to provide high-quality and cost-efficient mobile services [10].  
UAVs coincide with the cellular concept since low altitude combines coverage superiority and confined cell radius [11; 12]. The coverage area can be controlled by altitude adjustment; however, an enlarged coverage area will result in high path loss. Therefore, the optimum placement of UAVs depends on the design objective. UAVs can either support mobility or hover above the coverage area. The mobility of UAVs creates a Doppler shift, which causes severe inter-carrier interference at higher transmission frequencies [13]. Trajectory planning and control must be investigated to avoid interference in such a scenario. Regulations on the operation of UAVs in urban areas can impose restrictions in terms of the allowable areas and altitudes [14].  
Despite the conceptual simplicity of using UAVs to enhance IoT networks, the actual implementation requires advanced measures to control system parameters and enable the functionality of such networks. Artificial intelligence (AI) techniques have been shown to play a major role in this regard [15]. Advanced optimization techniques, such as genetic algorithms, have been suggested to enhance energy efficiency by controlling the UAV trajectory [16]. The role of UAVs in IoTs exceeds the communication part to include a computational enhancement by enabling mobile edge computing to offload tasks from IoT devices and optimize the management of energy and time resources [17; 18]. Non-orthogonal multiple access (NOMA) techniques, along with UAV networks, have been proposed for ultra-reliable low-latency communication (URLLC) applications [9]. UAVs have also been suggested to play new roles in IoT networks, such as wireless powering of IoT devices [19].  
The success of adopting UAV-enabled networks operating in mmWave bands depends on the accurate characterization of channel behavior. For example, the LoS blockage probability of UAV links in urban deployment is presented in [20]. Effective data acquisition for wireless sensors with obstacles is presented in [21]. A detailed overview of channel model literature is shown next, followed by an illustration of the objectives and contributions of this paper.