esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Methodological Approach Dealing with Weather Data at Regional Level  
The large extent of the region covered by the solar cadaster of the Greater Geneva area accounts for significant variation of solar irradiance and temperature throughout the region. Conditions range from typical Swiss lowland conditions (372 m to sea level) to mountain climate (1500 m to sea level) with significant differences in cloud coverage and temperature.  
Two typical situations qualify the region:  
During summer (Figure 1 left), cumulus formations occur on mountainous landforms, typically beginning during the early afternoon and evolving occasionally into thunderstorms by the end of the day.  
During winter (Figure 1 right), when high pressure prevails over central Europe, stratus coverage and temperature inversion may occur for longer periods, thus impacting solar access below 800 m altitude.  
Since weather observations do not cover the whole area (the number of meteorological stations is limited), methods using satellite data to calculate solar radiation have been considered. Methods have been described in a number of scientific papers (e.g., [21; 22]), but an implementation from scratch would have been too time consuming and useless since these methods have been implemented into readily available tools.  
To choose the appropriate tool, inter-comparisons of two distinct databases providing calculation algorithms have been made. The graphs in Figure 2 show relative irradiation as calculated by PVGIS ([[https://ec.europa.eu/jrc/en/pvgis](https://ec.europa.eu/jrc/en/pvgis)]([https://ec.europa.eu/jrc/en/pvgis](https://ec.europa.eu/jrc/en/pvgis))) and Meteorom(r). Two calculated stations with significantly different geographic situations (St. Cergue, a settlement in the Jura mountains and Bellegarde, a small town located on the opposite side of the Jura) were compared to a reference station (Airport--Cointrin reference station close to Geneva in the lowlands). While PVGIS shows little effect due to topography, Meteorom(r) shows more solar radiation during winter and less during summer for St. Cergue. The same effect but attenuated can be observed for Bellegarde. This is consistent with cloud coverage schemes related to typical meteorological situations described earlier.
# Review of Structural Modal Tracking in Operational Modal Analysis: Methods and Applications
## 3 Method of Automatic Modal Tracking
### Development of Modal Tracking Methods
The maximal \(s_{ij}\) in the similarity matrix is found, and the \(i\)th current mode is linked with the \(j\)th reference mode. This step is repeated until all values are matched. The MMOC is defined as  
\[\text{MMOC}=\frac{\left|\phi_{i}^{*}\phi_{j}\right|\left(1-\left(\lambda_{i} \right)^{n}\left(\lambda_{j}\right)^{n}\right)^{2}\left(1-\left(\lambda_{i} \right)^{2}\right)\left(1-\left(\lambda_{j}\right)^{2}\right)}{\left|\phi_{i} ^{*}\phi_{i}\right|\left|\phi_{j}^{*}\phi_{j}\right|\left(1-\lambda_{i}\lambda _{j}\right)\left(1-\left(\lambda_{i}\right)^{2n}\right)\left(1-\left(\lambda_{ j}\right)^{2n}\right)} \tag{5}\]  
where \(\phi_{i}\) and \(\phi_{j}\) denote the \(i\)th and \(j\)th eigenvectors of the system matrix, respectively, and \(\lambda_{i}\) and \(\lambda_{j}\) are normalized eigenvalues. In summary, despite variations in the methodological details of modal tracking, it collectively provides crucial data support for subsequent structural assessments based on modal evolution. A standard automatic operational modal analysis process is illustrated in Figure 1.
## 2 Results  
### MICs vs. Cystic Fibrosis Isolates  
Peptide 1018 has previously been shown to have activity both as an immune modulator [28, 29, 30] and as an anti-biofilm agent [22, 27] against individual strains from several species of bacteria including _P. aeruginosa_ and _Burkholderia cencepacia_. The latter observation was particularly surprising since planktonic _B. cepacia_ complex strains are known to be completely resistant to the effects of cationic agents like polymyxin B and cationic antimicrobial peptides by virtue of their lack of a self-promoted uptake system across the outer membrane [32]. Here we extended our observations on the anti-biofilm activity _vs. Burkholderia_ using a broad variety of _B. cepacia_ complex species from CF patients and also examined activity _vs._ a broad range of _P. aeruginosa_ chronic pulmonary infection CF isolates, since these are known to form biofilms in the CF lung. MIC assays were performed to determine the direct antimicrobial activity of peptide 1018 against planktonic cells. Peptide 1018 exhibited somewhat better planktonic antimicrobial activity _vs. Pseudomonas_ compared to human host defense peptide LL-37 [18] and synthetic cationic peptide 1037 [21] (Table 1), but was still quite weak with MICs ranging from 16 to 32 \(\upmu\)g/mL. Interestingly, all _P. aeruginosa_ clinical isolates showed higher MICs for human cathelicidin LL-37 compared to reference strains PAO1 and PA14, confirming the idea that this peptide has little useful activity _vs._ planktonic strains (Table 1). In contrast cationic antibiotic polymyxin B had MICs of 2-4 \(\upmu\)g/mL indicating that the low activity did not _per se_ reflect resistance to cationic peptides. \(\mathbf{Table}\) **1. ** Peptide 1018 demonstrated weak activity _vs._ planktonically-grown _P. aeruginosa_ cystic fibrosis strains and no activity _vs. B. cepacia_ complex cystic fibrosis isolates. All minimum inhibitory concentration (MIC) assays were performed at least three times. For comparison we included the previously studied _P. aeruginosa_ (non-cystic fibrosis) wild type strains PAO1 and PA14 and _B. cenocepacia_ 4813 [22], as well as showing results for two previously described anti-biofilm peptides (LL-37 [18] and 1037 [21]) and the cationic lipopeptide polymyxin B. Different _Burkholderia_ genomovars denote strains which are phylogenetically differentiable but phenotypically indistinguishable. _Burkholderia multivorans_ (genomovar II), genomovar III (divided into two _recA_ clusters, III-a and III-b, _Burkholderia stabilis_ (genomovar IV), _Burkholderia dolosa_ (genomovar VI), _Burkholderia ambifaria_ (genomovar VII).
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 6 Carbon-Based Nanomaterials
### Activated Carbon Fibers  
Activated carbon fibers (ACFs) exhibit superior adsorption kinetics and enhanced mass transfer rates compared to activated carbons (ACs), owing to their fibrous morphology and the organized arrangement of micropores [160]. ACFs are derived from organic precursor fibers through pyrolysis and activation with H\({}_{2}\)O and CO\({}_{2}\), and they have been effectively utilized as adsorbents or substrates for removing indoor pollutants in air filters. While ACFs have lower adsorption efficiency for polar volatile organic compounds (VOCs), their adsorption properties can be improved through surface modification and coupling with other semiconductors. However, the production cost of ACFs is higher due to the expensive organic precursors, which limits their applications [155; 160].  
ACFs possess a hydrophobic nature, resulting in a greater adsorption affinity for non-polar and weakly polar VOCs [161]. They are particularly effective for removing high concentrations of VOCs in humid conditions, as they do not strongly interact with water molecules. The hydrophobicity of ACFs can be modified by introducing oxygen-containing functional groups onto the fibers or by applying metal oxides to their surfaces. Such modifications enhance their adsorption capacity for target pollutants [155]. Pollutants are initially adsorbed onto the ACFs and subsequently transferred to the photocatalyst. ACFs can also adsorb and re-adsorb intermediates and by-products, increasing their retention time and facilitating the desired reactions. This process leads to a higher degradation rate of the target pollutant, reduces competition for adsorption sites, and minimizes the release of by-products [162].  
The role of ACFs as an adsorbent in TiO\({}_{2}\) photocatalysts has been demonstrated in studies focusing on the photocatalytic removal of methyl ethyl ketone (Figure 18). Specifically, the use of ACFs as support materials for TiO\({}_{2}\) resulted in a significant increase in the adsorption capacity for methyl ethyl ketone, achieving 100% breakthrough after 15 to 19 h. This enhancement in adsorption properties is attributed to the microporous structure, large surface area, and pore volume of ACFs. The adsorption of various compounds is also influenced by van der Waals interactions and interactions with functional groups on the ACF surfaces. In this context, \(\pi\)-\(\pi\) interactions and donor-acceptor complexes are the predominant mechanisms for the adsorption of VOCs.
# Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi
## 6 Discussion
### Latency Versus Bit Error Rate  
As mentioned in Section 6.1, latency's dependency on the BER is most visible in the parts of individual \(BER(\frac{E_{b}}{N_{0}})\) curves with the steepest negative slope. For example, 3.5- and 11 dB on the two curves associated with MCS 2 and 12.4 in Figure 10a, where every additional decoding iteration will considerably reduce the BER. Figure 15 demonstrates how additional iterations are reflected both in terms of latency and BER; however, the relative change in latency heavily varies between MCSs. In Figure 15a the average number of conducted iterations at MCS 2 varies between 1 and 4.75. The result is a considerable BER reduction, yet, latency only increases by 0.13 \(\upmu\)s or 0.005 \(\upmu\)s. Contrarily, executing between 1 and 3.5 iterations on average at MCS 12.4 will significantly increase latency, illustrated in Figure 15b. The 60 \(\upmu\)s (16.2 \(\upmu\)s) higher latency is a direct consequence of the decoder beginning to act as a bottleneck at more than 3 iterations. The two corner cases show that allowing more decoding iterations at lower MCS indexes significantly reduces BER, while negligibly influencing latency during the reception of 262 kB long payloads. Code rates with less coding overhead benefit less from additional iterations, and MCSs on the other end of the scale can only run a limited amount of decoding iterations before stalling PHY throughput. The horizontally-spread BER values at the highest latency values in Figure 15 are attributed to the stochastic nature of the AWGN channel. The number of incurred iterations is averaged over an entire 262 kB payload, relating to roughly 4000-6000 codewords, depending on the MCS.  
Figure 14: From the top down and left to right: incurred latency when increasing the number of allowed decoding iterations (**a**); resulting BER for given MCS and \(\frac{E_{b}}{N_{0}}\) combinations—the latter is represented by line width (**b**); BER difference when comparing 1- to 10- (**c**) and 10- to 100 decoding iterations (**d**).
## 4 Materials and Methods
### Floral Scent Collection and VOCs Identification
The microtraps containing headspace volatiles were stored within Teflon-capped glass autosampler vials (1.5 mL) without refrigeration in the field and then were kept at \(-20\)\({}^{\circ}\)C in the laboratory until they were analyzed by GC-MS. Floral scent was collected from 2-6 individuals per _Jaborosa_ species from field populations, with vegetative (n = 1) and ambient (n = 1) samples collected for each species as controls to identify and account for non-floral volatile artifacts present in the floral scent samples. Trapped samples were thermally desorbed for analysis by coupled GC-MS in two different laboratories, with comparable analytical systems ([PERSON], Cornell University, USA; [PERSON] 2010+ GC-MS, and [PERSON], University of Bayreuth, Germany; Varian Saturn 2000), both using non-polar GC columns (stationary phase = 5% diphenyl, 95% polydimethyl siloxane). Solid-phase microextraction fibers (SPME; Supelco, Inc., Bellefonte, PA, USA), combined with GC-MS, were used to characterize floral scent in the two species that we could cultivate in the greenhouse at Cornell University (_I. integrifolia_ and _J. sativa_). We used two standard SPME fiber stationary phases (100 \(\upmu\)m PDMS and 65 \(\upmu\)m DVB/PDMS) to optimally adsorb different classes of VOCs expected from hawkmoth- and fly-pollinated flowers, as validated in previous studies [77; 134]. SPME fibers were desorbed directly within the injection port of a gas chromatograph (GC17A) and analyzed using a quadrupole, EI mass spectrometer (QP-5000; Shimadzu Scientific Instruments, Inc., Columbia, MD, USA) as detailed by [57]. The number of collected samples using the three different methods is summarized in Supplementary Table S7. Volatile compounds were tentatively identified using NIST, Wiley, and Adams mass spectral libraries and confirmed whenever possible through co-injection of authentic standards. For a subset of species analyzed using a polar GC column (stabilwax), the [PERSON] retention index of each compound was calculated through sample co-injection with n-alkane standards and then compared with published values (see [[http://www.pheobase.com/](http://www.pheobase.com/)]([http://www.pheobase.com/](http://www.pheobase.com/)) (accessed on 31 March 2021) and [[http://www.flavornet.org/](http://www.flavornet.org/)]([http://www.flavornet.org/](http://www.flavornet.org/)) (accessed on 31 March 2021) for compounds with similar mass spectra, as suggested by MS library search. Retention indices for the polar column, along with the 10 most abundant MS ion fragments, were listed for each compound that could not be (tentatively) identified in this way.
# Efficiency of Public R&D Management Agency and Its Improvement toward Open Innovation
## 4 Analysis of Relative Efficiency of Planning and Evaluation Cost  
### Efficiency Analysis Results  
As shown in Table 9, the efficiency analysis of R&D management agencies showed that 6 of the 17 institutions, including NRF, KETEP, and KHIDI, found to be efficient (PTE = 1.00). Furthermore, 11 agencies such as IITP, KEIT and KIAT showed relatively inefficiencies.  
The overall efficiency mean was 0.72, and the standard deviation was 0.27, but 6 out of 11 inefficient agencies were less than 0.5, showing a large gap between agencies. The cause of inefficiency was analyzed to be higher due to pure technical efficiency than by scale efficiency (SE = 35.71%, PTE = 64.29%). On the other hand, in the 'Returns to scale' column, it is possible to determine whether or not the economy of scale is economical by dividing into CRS, DRS, and IRS according to the value of \(\sum_{i=1}^{n}\lambda_{i}=1\).  
First, three agencies were found in the CRS, where the scale was optimal, including NRF, Korea Sports Promotion Foundation and KOFPI. Three agencies with increasing returns on scale (IRS) showed a higher rate of increase in revenue than the rate of increase in size, while 11 agencies had diminishing returns on scale (DRS). In other words, the ratio of agencies in the diseconomies of scale among the analysis subjects showed a rather high result.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Measure** & **Pros** & **Cons** \\ \hline P1 & Maintain status quo & The current planning and evaluation cost structure is a product of a long-term adjustment. & Insufficient expertise improvement in planning and performance management \\ \hline P2 & Convert funding to grants fully & Beneficial for securing long-term stability of agency operation & Lack of flexible response to changes in projects subject to management \\ \hline P3 & \
\begin{tabular}{c} \\ Establish “independent planning and evaluation cost project” and dualize from grants \\ \end{tabular} &
\begin{tabular}{c} Plan new project and enhance performance management \\ \end{tabular} & Considering the current complex budget process including grants, it is difficult to create the dualization in a short period of time \\ \end{tabular}
\end{table}
Table 8: Pros and cons of each alternative to reorganizing the budget process for planning and evaluation costs.
# Entropy Wavelet-Based Method to Increase Efficiency in Highway Bridge Damage Identification
## 2 Description of the Rio Papaloapan Bridge (RPB)
### Experimental Tests  
After a major failure occurred in an upper anchoring element which was used to attach a cable to the corresponding pylon of the RPB, a broad-scope remote monitoring system was installed in this cable-stayed bridge in 2013, which was configured with three subsystems, including sensors, local monitoring, and photovoltaic [1]. As part of the maintenance program, the RPB was monitored in 2019 under two different integrity conditions as follows: a healthy bridge, and a damaged bridge without cable No. 6 from SH3, which was removed in order to inspect its corresponding upper anchoring element.  
The respective vibration data of the RPB deck in vertical direction were measured for both integrity conditions via the use of complementary instrumentation, consisting of twelve \(\pm\)2 g tri-axial MEMS wireless accelerometers model G-Link(r)-LXRS(r), fabricated by LORD MicroStrain(r). The sensors were installed on both sides of the bridge (downstream and upstream) on the deck of the semi-harps SH3, SH4, SH5, and SH6, as can be observed in Figure 4 via the 12 blue dots (S1-S12). The date were acquired first for the healthy condition and then for the damaged condition. The damage scenario is represented in Figure 5, where the red line indicates the cable which was removed. For both scenarios, the bridge was monitored for 1800 s, establishing a sampling frequency of 64 Hz, for a total of 115,200 samples per sensor. Moreover, the dynamic responses were divided into segments of 1 min per one, thus generating 30 tests for each case.  
Figure 4: (**a**) Accelerometer installed on the RPB deck, and (**b**) the location of the sensors (S1–S12) used to acquire the dynamic responses of the RPB.  
Figure 5: Removed stay-cable, generating the damaged condition on the RPB (red line): (**a**) real-life condition and (**b**) schematic representation.
# PGN: Progressively Guided Network with Pixel-Wise Attention for Underwater Image Enhancement
## 4 Experiments
### Ablation Study
#### 4.4.2 Feature Representation Block  
To verify the progressive ability of our supervision strategy in the network, we use different numbers (1, 2, 3) of FRBs with different structures to train our network separately. With our effective supervision strategy, as the number of FRBs increases, the network deepens progressively, and the dilation factors of convolutions increase layer by layer. Thus, the network learns more useful features and achieves better performance. In Figure 10, our full model conforms to this expectation, and the PSNR becomes better as the number of FRBs increases. However, when the supervisions of \(J_{n}\) or PAMs are removed, the network's performance is not substantially improved when the number of FRBs is increased, and even the performance of the network begins to deteriorate when the number of FRBs exceeds 2. This shows that our supervision strategy can continuously guide the network to learn useful features as the network deepens. We also use more Feature Representation Blocks to train our network. Unfortunately, the performance of the model deteriorates heavily when the number of FRBs exceeds 4. This may be due to the two facts: first, as the number of FRBs increases, the model learns more effective features, but at the same time introduces redundancy and noise, thereby affecting the model's performance; second, an excessive number of parameters cause the model to learn complex features on the training set, which in turn affects the model's generalization ability.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **PSNR** & **SSIM** & **MUSIQ** \\ \hline w/o PAM & 24.4826 & 0.9208 & 47.1192 \\ w/o Supervision of \(J_{n}\) & 24.2930 & 0.9165 & 46.9278 \\ w/o Dilated Convs & 24.3673 & 0.9189 & 46.6050 \\ PGN (Ours) & 24.7345 & 0.9216 & 47.1285 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study of our method. w/o means the variant model without the related module.  
Figure 9: Visual comparisons with different structures, where our method achieves the most accurate reconstruction compared to the others.
## 3 Experimental Results
### Time Domain Analysis  
We generated S4P files for the MDCRP structure filter board and the reference board using the measurement results. These S4P files were inputted into ADS schematic tools for the simulation, which set the related parameters. The input terminals were fed with \(\pm\)0.5 V, the rise time was 40 ps, the timing delay was 25 ps, and the internal resistance of the power supply was a stepped power supply of 50 \(\Omega\). Equations (1) and (2) were used to calculate the DM voltage of the output and the CM noise voltage of the output in the time domain transmission (TDT). We compared the MDCRP structure filter board and the reference board in actual measurement results.  
\[V_{TDT-differential}=V_{+}-V_{-} \tag{1}\]  
Figure 8: Comparison of simulated and measured insertion loss results.  
\[V_{TDT-common}=\frac{V_{+}+V_{-}}{2} \tag{2}\]  
The DM output voltage in the TDT is shown in Figure 9a. It can be seen that the final input voltage is 0.5 V. The actual input voltage is divided into 2.5 V through the internal resistance of the power supply and the terminal impedance, which are the inputs to the MDCRP structure filter board and the reference board. The MDCRP structure filter board was compared with the reference board, and we observed that the DM signals of the output were very similar at 0.249 V. The latency times were also very similar. This proves that the MDCRP structure filter does not influence the quality of the DM signal and maintains the DM signal integrity capability. The output results of the CM noise voltage in the TDT are shown in Figure 9b. It can be seen that the output CM noise improves the effect. The measured data reduces the original 144 mV to 51 mV, and the improvement range is about 64.5%. This shows that the MDCRP structure filter had powerful CM noise rejection capability.
## 2 Materials and Methods
### Improved Feature Extraction Algorithm
#### 2.2.1 Threshold-Free Multiscale Response Function  
As shown in Equation (3), the SAR-SIFT response function depends on the value of \(t\), a user-defined parameter. The sensitivity of corner detection is influenced by \(t\): the smaller the \(t\) value, the more feature points are extracted. Typically, \(t\) is set between 0.04 and 0.06. When \(t\) = 0.04, the number of feature points increases, but so does the number of erroneous corner detections; conversely, when \(t\) = 0.06, the number of feature points decreases, but accuracy improves. [PERSON] demonstrated that the stability of the Harris method is related to the smaller eigenvalue of the \(C_{SH}\) matrix in Equation (2). In the STSU-SAR-SIFT method, the Shi-Tomasi method replaces the Sar-Harris method across different scale spaces, directly using the smaller eigenvalue of the matrix as the criterion for corner detection. When the smaller eigenvalue of the \(C_{SH}\) matrix at a given pixel exceeds the set threshold, that point is considered a feature point. This method produces a similar number of feature points as the original SAR-SIFT method but offers higher accuracy and lower false detection rates. Consequently, the corner detector is modified as follows:  
\[R=min(\lambda_{1},\lambda_{2}) \tag{4}\]  
where, \(\lambda_{1}\) and \(\lambda_{2}\) represent the eigenvalues of the \(C_{SH}\) matrix in Equation (3), with min indicating the operation of taking the minimum value. A point is considered a corner when its \(R\) value (corner response value) exceeds a specified threshold.