esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 5 Metal Oxide Thin Films
### Copper-Doped Zinc Oxide Films  
Cu-doped ZnO films were deposited under various concentrations of copper solutions (0.05 and 0.1 mol%) [162]. The rod-like shape and hexagonal wurtzite structure could be seen based on XRD and SEM studies. The surface roughness, band gap, and green emission reduced, but the intensity of the UV emission increased with the increase of copper concentrations according to atomic force microscopy, ultraviolet spectroscopy, and photoluminescence studies.
# The Multiband over Spatial Division Multiplexing Sliceable Transceiver for Future Optical Networks
## 4 Numerical XT Evaluation
From the figure, it can be seen that the O-band is the spectral band less affected by the XT, presenting values below \(-50\) dB, whereas within the C-band, the XT values range between \(-30\) dB and \(-25\) dB. In particular, at 1550.12 nm the XT value is \(-26.1\) dB, which perfectly match the measured XT mean value of the MCF specifications of \(-25.5\) dB. Finally, the U-band presents a higher XT degradation, presenting values higher than \(-20\) dB. Figure 4 presents an evaluation of the XT considering different  
Figure 2: The trench–assisted core design of the 19–cores 25.4 km MCF considered for the experimental assessment in Section 6. MCF lengths up to 279.4 km, corresponding to 11 spans of 25.4 km. In this particular scenario, central wavelengths of the different analysed bands are considered to perform the XT analysis: specifically, 1310 nm (O-band), 1410 nm (E-band), 1495 nm (S-band), 1550 nm (C-band), 1600 nm (L-band), and 1650 nm (U-band). The achieved numerical results show an increase in the XT effect at the increase in the MCF length. As it is also seen in Figure 4, the U-band presents higher XT impact, showing values from \(-\)15 dB (after 25.4 km) up to \(-\)5 dB (after 279.4 km). After the maximum analysed MCF length of 279.4 km, \(-\)48 dB, \(-\)33.4 dB, \(-\)22.3 dB, \(-\)15.7 dB, and \(-\)10.1 dB are measured at O-, E-, S-, C-, and L-bands, respectively.
### Strain-Specific Predictions Reveal Novel Candidate Antibacterial Peptides (ABPs)
For the ABPs, we considered the Rank I, II, and III peptides more promising for further testing. They consist of \(20\) ABPs, \(19\) from Anthozoa and \(1\) from Medusozoa. The AA sequences from these \(20\) ABP candidates are available in Table 4. All peptides from Rank I demonstrate putative activity against _Bacillus subtilis_, with one from the stony coral _Fimbiraphylilia ancora_ also demonstrating activity against _Escherichia coli_ and _Pseudomonas aeruginosa_. Rank II AMPs demonstrate putative activity against lone-to-five strains, while Rank III AMPs demonstrate putative activity against four-to-five strains. The FASTA files corresponding to Rank I, Rank II, Rank III, and Rank IV ABPs are available in Dataset 12. Additionally, strain-specific predictions of AFPs and AVPs (Supplementary Figure S8), as well as specific targets of ACPs, are available at Dataset 12. Figure 8: Predictions of the specific activities of non-ha hemolytic and non-toxic AMPs were obtained from the union dataset (1935) of the most representative Cnidaria AMPs. Three predictive tools were applied, generating both intersection and union datasets for each predicted activity type: (**a**) Antibacterial peptides (ABP); (**b**) Antifungal peptides (AFP); (**c**) Antiviral peptides (AVP); and (**d**) Anticancer peptides (ACP). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Species**} & \multirow{2}{*}{**Subphylum**} & \multirow{2}{*}{**Class (Order)**} & \multirow{2}{*}{**Candidate ABP Sequence**} & \multirow{2}{*}{**Rank**} & \multicolumn{2}{c}{**Predicted**} \\  & & & & & **Antimicrobial** \\  & & & & & **Activity** \\ \hline _Clemactis_ & \multirow{2}{*}{Anth.} & Hexacorallia & CGVWQYRQGNSLYVQVISPRKKSGFRFR & I & _B. subtilis_ \\ _cchinata_ & & (Scleractinia) & DIFFFRVNYLGNQYNQLGVWWKKVRSSGSRG & I & _B. subtilis_ \\ _fuscicularis_ & & (Scleractinia) & DIFFFRVNYLGNQYNQLGVWWKKVRSSGSRG & I & _B. subtilis_ \\ _Favites colemani_ & \multirow{2}{*}{Anth.} & Hexacorallia & DREGKEKQWPFVPWQWPVRNNVLLRRQR & I & _B. subtilis_ \\ _Catalphylina_ & & (Scleractinia) & GAWSGAKRYCTQQRHESSNSSLERKWGND & I & _B. subtilis_ \\ _jarinti_ * & & (Scleractinia) & VFPFRSIFSGVTRGLRAVSSLSRD & I & _B. subtilis_ \\ _Finnliraphilia_ & \multirow{2}{*}{Anth.} & Hexacorallia & VFPFRSIFSGVTRGLRAVSSLSRD & I & _B. subtilis_ \\ _ancora_ & & (Scleractinia) & CRYRSVYKPLQFSLSSSSFLSYLVKRNTQQRGSFWR & II & _B. subtilis_ \\ _Alvepora_ & \multirow{2}{*}{Anth.} & Hexacorallia & CRKQVYKPLQFSLSSSSFLSYLVKRNTQQRGSFWR & II & _B. subtilis_ \\ _japonic_ & & (Scleractinia) & PREUM
## 3 Experiments and Discussions  
### Measurement of the Gravity Effect  
For the five-axis machine tool, the artefact may be mounted in different orientations so that the orientation of the local gravity of the artefact may alter relative to global gravity. As a result, three positions are measured. The position deviations are summarized using the differences between the two tilt positions (10\({}^{\circ}\), 90\({}^{\circ}\)) and the horizontal position (0\({}^{\circ}\)). A multi-sensor coordinate measuring machine (CMM) (Werth Videocheck from Giessen, Germany) was used (a Renishaw trigger probe TP200) to measure the positions of the fiducials. The measurement uncertainty of this machine is u = 0.25 + L/300 \(\upmu\)m.  
As shown in Figure 5, the position variations go up when the angle of the artefact increases from the horizontal orientation. Position deviations are mainly below 1 \(\upmu\)m in all directions but for fiducials whose heights were more than 25 mm; they could be many to a few micrometers when the artefact is rotated into the vertical position. It is noted that the position deviation in the z direction (global coordinate frame in Figure 2) seems to be sensitive to the artefact's rotation. This is because the component which is used to connect the fiducials to the fixture has a deformation. The longer the component is, the more obvious the gravitational deformation is. The largest deviation reaches \(\Delta d\xi_{5,VH}\) = 2.9 \(\upmu\)m and \(\Delta z_{5,VH}\) = 2.1 \(\upmu\)m in synthesis direction and only the z direction, respectively, when the gravitational orientation is changed from \(-z\) to \(-y\) for the local coordinate frame.  
In this study, the effect caused by gravitational deformation still needed to be considered although it is as small as a few micrometers. As shown in Figure 5, the results indicate that there are two facts which can be considered to reduce the effect. It is necessary to optimize the length of the connection component to be as short as possible with consideration of the working volume. In other words, it is helpful to add stiffness to the artefact. The other is that the artefact can be measured in a stable position in each measurement. For example, it is better to carry out the metrology in a horizontal position (0\({}^{\circ}\)) as shown in Figure 2 in every measurement instrument. In this case, the measurement repeatability should be guaranteed in a particular position. Taking these two points into consideration, the height of each fiducial (z value as shown in Figures 2 and 3) is controlled within 25 mm and the artefact was clamped in a vertical position in every measurement process and instrument; the raster milling machine tool and the coordinate measuring machine are used in this case. The deformations due to weight are finally controlled to an acceptable level, and the largest deformations are \(\Delta\overline{z}\leq 0.3\) um and \(\Delta\overline{d}_{(x,y,z)}\leq 0.45\) um, respectively.
# Electrospinning of Chitosan for Antibacterial Applications--Current Trends
## 6 Antibacterial Effect of Chitosan Nanofibers  
The flexibility of the chitosan application depends on its physical, chemical, and biological properties. These properties are a function of its structural characteristics and production method, such as the extraction source and procedure, molecular weight, pH dependency, concentration, and electrospinning technique. In acidic environments, the protonation of its functional amine groups gives it a polycationic nature, while at higher pH, intermolecular interactions via hydrogen bonds and van der Waals forces facilitate the formation of fibers, films, porous structures, and gels [131].  
Chemical modifications to its molecular structure, including crosslinking, may affect the material properties such as the tensile strength, biodegradability, and microbial cell affinity, which are crucial for several application fields prone to the attack of different bacteria strains.
## 3 Results and Discussion
### Acidogenic Fermentation
#### 3.2.1 pH and Organic Load Effects on BSG Acidogenic Batch Fermentation
[8], in a study on VFA production using raw BSG, achieved HProp and HAc as major organic acids followed by HBut in a fed-batch stirred tank reactor operated under different HRT and OLR. However, as raw BSG was used as feedstock and the reactor configuration and operation mode were different from our study, the synthesis of organic acids can follow different metabolic pathways. The produced organic acids can be used as building blocks for polyhydroxyalkanoates (PHA) production. The PHA monomeric composition is directly related to the FP used as substrate. In general, hydroxybutyrate (HB) monomers are synthetized from HLac, HAc and HBut, while hydroxyvalerate (HV) monomers are produced from HProp and HVal [15; 39]. However, the production of other organic acids can promote the synthesis of different homopolymers or copolymers with improved mechanical properties. The homopolymer P(3 HB) has a high crystallinity which results in a very low impact strength and brittle failure. On the other hand, the incorporation of HV chains enable to reach a broader processing window and an improved material flexibility, by decreasing crystallinity, melting temperature and glass transition temperature [40]. Having in mind the final application of the biopolymer, it is possible to manipulate the FP composition to achieve the desired properties of the polymer. Based on the FP profile obtained at each pH and OL, the HB and HV monomers were estimated (Figure 3c). The highest content of HB precursors was achieved at pH 5 (87.7 \(\pm\) 5.6%C-mol basis) while the HV precursors were higher at pH 6.0 (15.9 \(\pm\) 2.0%C-mol basis). The HV precursors generally increased (from ca. 15.9-17.0, 7.68-15.8, and 5.91-12.7%C-mol basis, at OL of 10, 30 and 40 gCOD L\({}^{-1}\), respectively) with pH increase from 5.0 to 6.0 (Figure 3c), except for the OL of 20 gCOD L\({}^{-1}\). Thus, operating conditions such as pH and OL can be manipulated to produce the HB/HV precursors accordingly to the target polymer composition considering VFA application in PHA production. On the other hand, in all the conditions tested butyrate was the dominant FP obtained. This acid is considered as the preferred substrate for PHA production since it is more energetically favorable at a metabolic level than other FP [41; 42; 43]. Thus, from this point of view the butyric acid is beneficial for the process efficiency. Nevertheless, introduction of HV precursors is envisaged if a more flexible/less rigid polymer is required. The main aim of this study was to produce organic acids, thus stopping the process before methane production. Therefore, biogas composition analysis was performed along the experiments. The main gases detected were nitrogen (N\({}_{2}\)) and carbon dioxide (CO\({}_{2}\)) (data not shown). The presence of hydrogen (H\({}_{2}\)), oxygen (O\({}_{2}\)) and methane (CH\({}_{4}\)) was negligible (data not shown) indicating that acidogenesis was favored over methanogenesis under the applied operating conditions.
# High Reliability Evaluation and Lifetime Prediction of 50 GHz Athermal AWG Module
## 5 Lifetime Estimation
### Accelerated Life Test
#### 5.1.1 Preparation  
Based on the accelerated life test conditions in Table 4, the samples were measured at 250-h intervals for 4000 h using three high-temperature chambers (ESPEC), an optical source (Amonics), and an Optical Spectrum Analyzer (Anritsu, MS9740A) at three different temperatures, 71 \({}^{\circ}\)C, 83 \({}^{\circ}\)C, and 95 \({}^{\circ}\)C. The failure criteria were based on accidental failure or a 50% change in the initial measurement. Data reliability was improved by measuring channels 1 and 48. A total of 28 samples (Gaussian type 21, Flat type 7) were distributed by temperature.  
Figure 10: All channel average result of module after low storage temperature test; (**a**) Center wavelength shift; (**b**) Insertion loss variation; (**c**) Spectrum after low storage temperature test (L.T 1).
## 2 Wireless Multimedia Sensor Networks
### Directional Sensor Coverage  
Unlike the omnidirectional scalar sensor siblings, camera sensors in WMSNs are directional. As shown in Figure 4, the coverage regions, namely FoVs, of camera sensors are determined by two factors: Intrinsic camera parameters such as lens focal length, pixel location of the optical center, and skew factor [79; 115]; and extrinsic camera parameters like location and orientation [90]. The directional FoVs of camera sensors brings many new challenges upon WMSNs. First, both intrinsic and extrinsic camera parameters need to be calibrated accurately. Because correct positioning of cameras and accurate interpretation of FoVs are key to the the success of all monitoring tasks. Second, even though WMSNs are usually deployed with a redundant number of camera sensors, means of maximizing the coverage region with only a minimum number of active camera sensors are necessary in order to conserve energy [116]. Last but not the least, as many applications will often require collaboration among multiple camera sensors, careful designing of collaboration schemes is required.  
Figure 4: Field of View of A Camera Sensor.  
Figure 3: In-network Processing in WMSNs.
# Experimental Research on Mechanical Performance of SSRC Columns under Eccentric Compression
## 3 Experimental Results
### Load-Lateral Displacement Curves  
The experimental load-lateral displacement curves of the specimens are exhibited in Figure 5. The failure process is divided into three stages:  
1. Linear growth stage: The lateral displacement is small, and the load increases linearly.
2. Nonlinear growth stage: With the increase in load, transverse cracks appear in the concrete on the tension side, and the section stiffness decreases gradually.
3. Falling stage: After the ultimate load, the curve exhibits a downward trend. The falling stage for the curves of the large eccentric compression specimen is gentler, showing good ductility.  
As shown in Figure 5a, under the same eccentricity, the reinforcement ratio is positively correlated with the bearing capacity of SSRC members; however, it has little effect on the lateral displacement corresponding to the ultimate load. As shown in Figure 5b, under the same reinforcement ratio, the eccentricity is negatively correlated with the bearing capacity of SSRC members.  
The lateral deflection curves of all specimens under various loads are displayed in Figure 6. The lateral displacement curves of the eccentric compression specimens are symmetrically distributed along the middle of the columns and are approximately sinusoidal. The maximum lateral displacement occurs in the middle of the test column. Under the same eccentricity, the reinforcement ratio had a negligible effect on the lateral displacement corresponding to the ultimate load. The specimens SSRC3, SSRC4, SSRC5, and SSRC6 have the same reinforcement and increasing eccentricity; as the eccentricity increases, the ultimate load decreases. As the eccentricity increases, the lateral deflection at maximum load increases for columns SSRC3 and SSRC4 (reaching approximately 10 and 17 mm, respectively) and begins to decrease for columns SSRC5 and SSRC6 (reaching approximately 12 and 10 mm, respectively).This effect may be due to the axle being applied to the corbel.  
Figure 5: Load–lateral displacement curves for various (**a**) reinforcement ratios and (**b**) eccentricities.  
Figure 6: _Cont._
## 4 Results
### Topside Effective Scale Height from Calibrated In Situ \(N_{e}\) Observations
For example, CSES--01 successfully captures the \(H_{0}\) increase in the South Atlantic region, which is especially visible in the NDJ and FMA months. This region corresponds to the South Atlantic anomaly, where the magnitude of the geomagnetic field decreases noticeably. Seasonal differences are visible near the geomagnetic equator and at mid latitudes. Indeed, at mid latitudes, \(H_{0}\) values are higher in the local summer season (MJ) in the Northern hemisphere; NDJ in the Southern hemisphere) than in the local winter. Around equinoxes, hemispheric differences are limited to the longitudinal sectors of the South Atlantic and South Indian oceans. Figure 5 shows the geographical maps of median \(H_{0}\) values for the nighttime sector in the same format as Figure 4. Compared to daytime, in the nighttime sector, \(H_{0}\) values obtained from both CSES--01 and Swarm B observations show large differences compared to COSMIC--1, as well as a greater spatial variability. In particular, CSES--01 and Swarm B predict higher \(H_{0}\) values in several latitudinal belts at both low and mid latitudes. The region of very high \(H_{0}\) values in the South African and South Indian ocean is also noteworthy, with values significantly higher compared to other longitudinal sectors at the same latitude. The highest values are obtained during the NDJ months, regardless of the local season. On the other hand, COSMIC--1 shows a much simpler spatial pattern, with higher values at high latitudes (within the auroral oval region) and lower values at mid and low latitudes.
## 3 Quality Assessment of the BIC/B2a Signals from BDS-3 Operational Satellites
### Differential ISB estimation between BDS-3/GPS/Galileo BIC-L1-E1 and B2a-L5-E5a signals
#### 4.3.2 Test Results
Compared with LCM solutions, it is obvious that the adoption of TCM can provide fewer float and wrongly-fixed solutions while more \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Freq.**} & \multirow{2}{*}{**Elev.**} & \multirow{2}{*}{**AVE.**} & \multirow{2}{*}{**AR**} & \multirow{2}{*}{**CPS/BDS-3**} & \multirow{2}{*}{**GPS/Gallieo/BDS-3**} \\ \cline{5-10}  & & & & & **LCM** & & **TCM** & **Imp. 2** & **LCM** & **TCM** & **Imp.** \\ \hline \multirow{5}{*}{SF} & \multirow{5}{*}{10\({}^{\circ}\)} & & Th 1 = 2.0 & 33.2 & 58.7 & 25.5 & 78.4 & 95.1 & 16.7 \\  & & (9.3, & Th = 3.0 & 19.7 & 40.8 & 21.1 & 60.6 & 89.4 & 28.8 \\  & & 13.1) & P\({}_{\rm f}\) = 1\% & 9.8 & 39.5 & 29.7 & 71.8 & 97.5 & 25.7 \\  & & & P\({}_{\rm f}\) = 0.1\% & 0.1 & 14.5 & 14.4 & 43.5 & 93.9 & 50.4 \\  & & & Th = 2.0 & 24.1 & 47.3 & 23.2 & 66.8 & 91.4 & 24.6 \\  & & (8.0, & Th = 3.0 & 14.7 & 34.5 & 19.8 & 49.8 & 84.5 & 34.7 \\  & & 11.6) & P\({}_{\rm f}\) = 1\% & 3.3 & 22.3 & 19.0 & 51.0 & 93.2 & 42.2 \\  & & & P\({}_{\rm f}\) = 0.1\% & 0.0 & 5.8 & 5.8 & 23.6 & 84.5 & 60.9 \\ \hline \multirow{5}{*}{DF} & \multirow{5}{*}{10\({}^{\circ}\)} & & Th = 2.0 & 68.7 & 85.9 & 17.2 & 97.6 & 98.7 & 1.1 \\  & & (9.3, & Th = 3.0 & 58.2 & 79.2 & 21.0 & 95.1 & 96.9 & 1.8 \\  & & 13.1) & P\({}_{\rm f}\) = 1\% & 63.2 & 84.8 & 21.6 & 99.2 & 100.0 & 0.8 \\  & & & P\({}_{\rm f}\) = 0.1\% & 53.6 & 79.0 & 25.4 & 97.2 & 100.0 & 2.8 \\  & & & Th = 2.0 & 59.1 & 80.1 & 21.0 & 95.1 & 96.9 & 1.8 \\  & & (8.0, & Th = 3.0 & 48.8 & 73.4 & 24.6 & 92.8 & 96.4 & 3.6 \\  & & 11.6) & P\({}_{\rm f}\) = 1\% & 50.6 & 78.9 & 28.3 & 96.3 & 97.5 & 1.2 \\  & & & P\({}_{\rm f}\) = 0.1\% & 35.5 & 70.1 & 34.6 & 93.2 & 97.5 & 4.3 \\ \hline \hline \end{tabular} \({}^{1,2}\) \"Th\" represents threshold of ratio for integer ambiguity validation; “Imp.” means empirical success rate improvement of TCM with respect to LCM.  
\end{table} 
Table 5: Empirical ambiguity resolution success rates (percent) for GPS/BDS-3 and GPS/Galileo/BDS-3 solutions with 10\({}^{\circ}\) and 20\({}^{\circ}\) elevation cut-off angles. correctly-fixed solutions. Such results undoubtedly confirm the benefits of tightly combined RTK with observations from GPS, Galileo and BDS-3 operational satellites.
# Applying Force Plate Technology to Inform Human Performance Programming in Tactical Populations
## 4 Testing Performances for Training Related Adapations
#### Case Study Scenario 2: Training Adaptations  
There are many theories on proper training protocols to elicit adaptations in tactical populations. Since many tactical training programs include aerobic/endurance-based tasks (i.e., combat training, prolonged rack marches, field/shoot house training), the addition of high volume and low-intensity steady state cardiovascular training may interfere with cardiorespiratory and neuromuscular adaptations [84]. On the contrary, adding strength training has improved strength, power, endurance, speed, and occupational obstacle course times, proving the use of strength training for occupational preparedness and performance [85, 86]. It should also be noted that while power training is beneficial for improving power-related metrics, stronger individuals may benefit more from this style of training [87]. Thus, it has been suggested by many that individualized training programs based on periodization and progressive overload theories be used to create optimal training programs for eliciting performance adaptations and subsequently reducing the risk of injuries [84, 85, 86]. Monitoring at the \"meso level\" with frequent performance assessments (i.e., every 8-12 weeks) illustrates whether the prescribed training programs are eliciting the intended adaptations through precise quantifications of performance alterations [88]. Over time, meso-level monitoring can be linked together with more robust, macro-level monitoring to help practitioners view development across the annual plan. This proactive approach identifies maladaptive responses to be corrected with necessary adjustments to ensure improved physical preparedness prior to occupational tasks, which can improve overall safety and health. It is beyond the scope of this paper, but the timeline for training adaptations is important to understand for estimating what training adaptions should be occurring at a given time frame. For example, strength and power adaptations may be noted at a faster rate in untrained versus trained populations, but in general, may be noticed following 8-12 weeks of training (Figure 6).  
Figure 5: Definitions of Variables of Emphasis in the force-time curve.
# Multi-Sensor Photoelectric Fire Alarm Device Implementation for Early Fire Detection in Campsites
###### Abstract  
With the growing popularity of leisure activities such as camping and glamping, the incidence of fires at camping sites has increased. This study focuses on improving the effectiveness of photoelectric fire alarm devices by incorporating temperature and humidity data for early fire detection in confined spaces, such as campsites. This study proposes a novel multi-sensor fire alarm system that dynamically adjusts fire detection threshold values based on temperature and humidity data collected by unmanned automatic weather observation systems. The prototype, which was implemented using Raspberry Pi and multiple sensors, demonstrated approximately 20% faster fire detection speed than existing photoelectric fire alarm systems, as verified through experiments in a simulated camping environment. The proposed approach is expected to advance fire alarm systems, enabling faster and more accurate fire detection in diverse environments, particularly at campsites.  
photoelectric type smoke detectors; fire detection considering humidity; fire detector for narrowclosed places; multi-sensor fire detector; correlation between fire occurrence and humidity/temperature