esa-sceva/satcom-chunk-collection huggingface dataset:
# Roles of D-Amino Acids on the Bioactivity of Host Defense Peptides
## 2 Natural d-Amino Acids (d-AA)-Containing HDPs
Similarly, the presence of d-AAs mediates the receptor-ligand binding of crustacean hyperglycemia hormone CHH [36] and the mouse formyl peptide receptor Fpr that is responsible for vomeronasal pathogen sensing [37]. Aside from a number of notable exceptions, virtually all d-AA-containing HDPs are synthetic. In particular, to the best of our knowledge, there are no natural HDPs that are composed solely of d-AAs. The family of gramicidin bacteriocin peptides is noteworthy not because it is one of the few naturally occuring d-AA-containing HDPs but because it is the first antibiotic peptide to be used clinically [38]. Gramicidins (Figure 1) are synthesized by the soil bacterium _Bacillus brevis_ and are comprised of alternating l-AAs and d-AAs as the general sequence feature. They exert antimicrobial activity via the characteristic HDP mechanism of forming ion channels in cell membranes [39]. Gramicidin D was the first form to be discovered by [PERSON] in 1939. The D type is in fact a mixture of three linear peptides, namely gramicidin A, B and C. All three possessed d-Leu and d-Val and differ in only the eleventh residue: Trp for type A, Phe for type B and Tyr for type C. Later in 1942, the cyclic S form was discovered by [PERSON] and quickly found application as an antiseptic in the World War 2 [40]. It should also be noted that all gramicidins are synthesized via non-ribosomal pathways. In addition to gramicidin, several other natural diastereomeric AMP types that act via the characteristic HDP mechanism of membrane destabilization also exists such as gratisin GR [41] and the family of tyrocidine [42], both of which are isolated from _Bacillus brevis_ (i.e., although gratisin GR originates from the specific strain _Bacillus brevis_ Y-33). Furthermore, Bombinin H is isolated from the skin secretion of several species of the _Bombina_ frog genus [43]. It is also worthy to note that gratisin GR and all natural tyrocidine subtypes (A, B and C) are cyclic. All natural diastereomeric HDP classes presented here exhibit rather strong hemolytic activity and are therefore unsuited for systemic administration. Figure 1: Three-dimensional structure (**A**) and primary sequence (**B**) of Gramicidin D. The first panel shows the protein structure (PDB 1 MAG) as a cartoon representation with d-AA shown as grey colored ball-and-sticks. As for the second panel, residues at the first position are represented by purple colored X that can be either valine or isoleucine. Furthermore, l- and d-AA are represented by black and red colored text, respectively, whereas the residue distinguishing the subtype of Gramidicin is indicated by the blue colored text.
# Adsorption of Ni2+ and Cd2+ from Water by Calcium Alginate/Spent Coffee Grounds Composite Beads
###### Abstract  
The use of heavy metals in technological applications has led to detrimental effects on human health and the environment. Activated carbon and ion-exchange resins are commonly used to remove pollutants but they are expensive. Therefore, the research of low-cost alternatives derived from natural resources and organic wastes is being considered. The aim of this study considers the use of Calcium Alginate/Spent Coffee Grounds (CA-SCGs) composite beads to adsorb heavy metals from aqueous solutions, particularly, the removal of Ni2+ or Cd2+ at concentrations from 10 ppm to 100 ppm. CA-SCGs beads were made of equal proportions of alginate and spent coffee grounds and compared with calcium alginate beads (CA beads) and spent coffee grounds (SCGs) in terms of capacity and rate of adsorption. Three cycles of adsorption/desorption were done. The beads were characterized by Scanning Electron Microscopy coupled with an energy-dispersive X-ray spectroscopy (SEM-EDX), Fourier-transform infrared spectroscopy (FT-IR), Raman spectroscopy, and point of zero charge. Langmuir, Freundlich, and Sips models, and a pseudo-second-order kinetic equation were used. Sips model showed the best correlation with the adsorption of CA-SCGs beads with capacities of adsorption of 91.18 mg/g for cadmium and 20.96 mg/g for nickel. CA-SCGs beads had a greater adsorption than the CA beads, achieving adsorption percentages close to 100% than alginate alone, showing their effectiveness in heavy metal removal.  
removal of heavy metals; calcium alginate; spent coffee grounds; water pollution; low-cost adsorbent +
Footnote †: journal: Applied Physics Letters
## 4 Simulation Results and Analysis
Figure 5 illustrates the performance results for a 4 \(\times\) 225 LIS configuration, considering two users and various receiver types. This setup consists of four panels, each containing 225 antennas, arranged in a 15 \(\times\) 15 grid per panel, leading to a total of 900 antennas in the LIS. Similar to the previous figure, the scenario involves one reference user and one interfering user. The results indicate that, as observed earlier, the combination of MRC and EGC with a neural receiver outperforms iterative receivers, as well as ZF and MMSE methods. Among these, MRC with a neural receiver demonstrates the best performance, followed by EGC with a neural receiver, which still surpasses the results obtained with the ZF and MMSE techniques. Figure 6 shows the performances of the following two LIS configurations: 4 \(\times\) 400 and 4 \(\times\) 225. Two users were considered in the simulations. As expected, the results obtained with 4 \(\times\) 400 LIS configuration were always better than those obtained with 4 \(\times\) 225 for all different receiver types. Moreover, it is observed that the best overall performance was obtained with the 4 \(\times\) 400 LIS configuration and MRC neural receiver. Figure 7 shows the performance results of the 4 \(\times\) 400 LIS configuration, with 16 users, with an iterative receiver and with a neural network, for both MRC and EGC. The results for ZF/MMSE are also plotted. Similar to the observations in Figure 4 (with two users), Figure 7 demonstrates that, for a scenario with 16 users and the given LIS configuration, the MRC neural network receiver achieves the best overall performance. As expected, the performance of ZF/MMSE stays within that of MRC/EGC iterative receivers and MRC/EGC neural network receivers. Figure 8 presents the performance results for the 4 \(\times\) 4004 LIS configuration with varying numbers of users (2, 8, and 16), using both iterative and neural-network-based receivers, for MRC and EGC schemes. The results for ZF and MMSE receivers are also included. As expected, the performance declines as the number of users increases. However, the degradation observed when increasing from 8 to 16 users is minimal. This occurs because the LIS is able to mitigate interference very efficiently. The receiver that achieves  
Figure 4: Performance results of the 4 \(\times\) 400 LIS, with 2 users, with different receiver types. the best overall performance, regardless of the number of users, is the MRC with the neural network receiver, followed by EGC. Figure 5: Performance results of the 4 \(\times\) 225 LIS, with 2 users, with different receiver types. Figure 6: Performance results of several LIS configurations, with 2 users, and different receiver types.
## 2 Spectral UAV Sensors
### Mosaic filter-on-chip cameras  
In the mosaic filter-on-chip technology, each pixel carries a spectral filter that has a certain transmission, such as the principle of a Bayer pattern in an RGB camera. The combined information of the pixels within a mosaic or tiles then represents the spectral information of the area seen by the tile. The technology is based on a thin wafer on top of a monochromatic complementary metal-oxide semiconductor (CMOS) sensor in which the wafer contains band pass filters that isolate spectral wavelengths according to the Fabry-Perot interference principle [81; 82]. The wafers are produced in a range of spatial configurations, including linear, mosaic, and tile-based filters. This technique was developed by Imec using the FPI filters to provide different spectral bands [81]. Currently, the chip is available for the range of 470-630 nm in 16 (4 \(\times\) 4 pattern) bands with a spatial resolution of 512 \(\times\) 256 pixels, and in the range of 600-1000 nm in 25 bands (5 \(\times\) 5 pattern) with a spatial resolution of 409 \(\times\) 216 pixels. The FWHM is below 15 nm for both systems. The two chips are integrated into cameras by several companies, and weigh below 400 g (e.g., [83; 84]). So far, only first attempts with this new technology have been published [85; 86], including a study on how to optimize the demosaicing of the images [87]. Recently, Imec has announced a SWIR version of the camera [88].  
The advantage of filter-on-chip cameras is that they record all of the bands at the same time. Additionally, they are very light, and can be carried by small UAVs. The disadvantage is that each band is just measured once within each tile and thus, accurate spectral information for one band is only available once every few pixels. Currently, this is tackled by slightly defocusing the camera and interpolation techniques. They have a higher spatial resolution than multi-point spectrometers, but the radiometric performance of the filter-on-chip technology has not yet reached the quality of established sensing principles that is used in point and line scanning devices. This mainly results from the technical challenges during the manufacturing process (i.e., strong variation of the thickness of the filter elements between adjacent pixels) and the novelty of the technique. Figure 2 shows images captured by a sequential 2D imager, a multi-point spectrometer, and a filter-on-chip snapshot camera.
## 3 Results and Discussion
### Influence of Adsorbent Dosage on the Adsorption of Chromium(III)  
The results concerning the adsorption of chromium(III) from the aqueous solution containing this metal in a 0.1 M NaOH medium and various adsorbent doses in the range 0.25-4 g L\({}^{-1}\) revealed an increase in the percentage of adsorption as the adsorbent dosage was increased (Figure 2).  
The experimental results of the chromium(III) adsorption onto the nanotubes were fit to various kinetics models; from these fits, it was assumed that the pseudo-second order kinetic model, Equation (3), best represented the adsorption kinetics for the initial carbon nanotubes dosages in the 0.25-4 g L\({}^{-1}\) range.  
These results confirmed that a chemisorption process conveniently described the measured Cr(III) removal from the aqueous solution. Table 4 summarizes the results from this fit.  
\begin{table}
\begin{tabular}{c c} \hline
**Speed (min\({}^{-1}\))** & **Cr(III) Uptake (mg g\({}^{-1}\) Adsorption)** \\ \hline
250 & 6.4 \\
500 & 7.0 \\
1000 & 8.4 \\
1500 & 8.3 \\ \hline \end{tabular}
\end{table}
Table 2: Chromium(III) adsorption at various stirring speeds (Temperature: 20 \({}^{\circ}\)C. Time: 2 h).  
\begin{table}
\begin{tabular}{c c c} \hline
**NaOH, M** & **Cr(III) Adsorption (\%)** & **Metal Uptake (mg g\({}^{-1}\))** \\ \hline
0.1 & 84 & 8.4 \\
0.2 & 89 & 8.9 \\
0.28 & 96 & 9.6 \\
0.35 & 99 & 9.9 \\
0.43 & 51 & 5.1 \\
0.5 & 37 & 3.7 \\ \hline \end{tabular}
\end{table}
Table 3: Influence of the NaOH concentration on chromium(III) adsorption onto the nanotubes.  
The equilibrium data fitted to the Langmuir Type-2 isotherm model, Equation (4), and the corresponding plot in Figure 3. The relative parameters for this fit are [Cr(III)]\({}_{\rm{cnt,m}}\) = 204 mg g\({}^{-1}\) and k\({}_{\rm{L}}\) = \(2.3\times 10^{-2}\) L mg\({}^{-1}\). Moreover, by using the next expression,  
\[\rm{R}=\frac{1}{1+k_{L}[Cr(III)]_{aq,0}^{2}} \tag{5}\]  
where [Cr(III)]\({}_{aq,0}\) is the initial metal concentration in the solution (10 mg L\({}^{-1}\)), it is assumed that the adsorption process is favorable (R = 0.31), since 0 \(<\) R \(<\) 1.  
\begin{table}
\begin{tabular}{c c c} \hline
**Nanotubes Dosage (g L\({}^{-1}\))** & \(\rm{\bf r^{2}}\) & \(\rm{\bf k_{2}}\)**, (g min\({}^{-1}\) mg\({}^{-1}\))** \\ \hline
0.25 & 0.9986 & \(1.8\times 10^{-1}\) \\
1 & 0.9997 & \(1.8\times 10^{-2}\) \\
4 & 0.9998 & \(1.4\times 10^{-2}\) \\ \hline \end{tabular}
\end{table}
Table 4: Parameters for the fit of Equation (3) to various nanotubes doses.  
Figure 3: Plot of 1/[Cr(III)]\({}_{\rm{cnt,e}}\)_versus_ 1/[Cr(III)]\({}_{\rm{aq,e}}\) for the Langmuir Type-2 equation. Experimental conditions are as in Figure 2. Time: 2 h. Dotted lines represented 95% confidence interval of the regression line.  
Figure 2: Influence of adsorbent dosage on chromium(III) adsorption. Aqueous solution: 0.01 g L\({}^{-1}\) Cr(III) in 0.1 M NaOH. Temperature: 20 \({}^{\circ}\)C.
## 3 Results
### Experimental Setup  
All experiments were conducted on a 64-bit Windows operating system with an NVIDIA GeForce RTX 3090 (24 G) graphics card, using the Python 3.7 programming language and the PyTorch 1.12.1 library. The stochastic gradient descent (SGD) optimizer was called to train the model from end to end. Each experiment was run for 80 epochs. The learning rate was initialized as 0.1 and reduced by 0.1 times at the 10 th and 50 th epochs. The batch size was set to 32 and the weight decay to 0.001. Each input consisted of a continuous skeleton of 200 frames, and less than 200 frames of the skeleton series were padded by zeros.  
Figure 4: Joints acquired by Kinect v2 and Azure Kinect.  
The main stream of the proposed STJA-GCN comprised four stacked spatial-temporal graph convolutional layers. The ResNet mechanism was introduced to each layer. Each layer included 9 spatial-temporal convolutional kernels with a dropout of 0.5. The output size of the first 2 layers was set to 128, and that of the last 2 layers to 256. In the first and third layers, the step size was set to 2 to reduce the time dimension by half of the number of frames and improve the computational efficiency.  
To obtain more convincing results, a fivefold cross-validation was performed on each dataset. The performance of the models were measured by the cross-validated average top-1 accuracy, FLOPs, and the number of model parameters (#Params). FLOPs represents the number of operations performed by the model and indicates its computational complexity.
## 4 Proposed CH Selection Protocol
### Energy Model  
We considered the energy model from [7; 8; 18]. Energy is consumed when handling data in the WSN through three processes, namely data transmission, reception, and aggregation--the energy consumed is, respectively, denoted as data transmission energy \(E_{\text{TX}}\), reception energy \(E_{\text{RX}}\), and aggregation energy \(E_{\text{DA}}\). Unlike \(E_{\text{DA}}\), which remains fixed over time, the values of \(E_{\text{TX}}\) and \(E_{\text{RX}}\) vary depending on the situation. Specifically, \(E_{\text{TX}}\) depends on distance \(d\) from the transmitting sensor node to the receiver, and different definitions are used depending on whether \(d\) is above or below threshold distance \(d_{0}\). If \(d<d_{0}\), the free-space model is used, and the multipath model is used otherwise:  
\[E_{\text{TX}}=\left\{\begin{array}{rl}l\times E_{\text{elec}}+l\times \epsilon_{\text{fs}}\times d^{2}&,\text{ if }d<d_{0}\\ l\times E_{\text{elec}}+l\times\epsilon_{\text{mp}}\times d^{4}&,\text{ otherwise}\end{array}\right., \tag{14}\]  
where \(E_{\text{elec}}\) is the electric energy required to convert 1 bit of data into a signal, \(l\) is the data length, \(\epsilon_{\text{fs}}\) and \(\epsilon_{\text{mp}}\) are the power amounts used by the free-space and the multipath models, respectively, and they serve as criteria for determining \(d_{0}\) as follows:  
\[d_{0}=\sqrt{\frac{\epsilon_{\text{fs}}}{\epsilon_{\text{mp}}}}. \tag{15}\]  
As reception energy, \(E_{\text{RX}}\) is that required to convert a received signal into data, and energy \(E_{\text{elec}}\) is also consumed during this process; like for \(E_{\text{TX}}\), and \(E_{\text{RX}}\) depends on the length of received data:  
\[E_{\text{RX}}=l\times E_{\text{elec}}. \tag{16}\]
## 2 Results and Discussion
### Sources and Influencing Factors of T/O Compounds in Freshwater Bodies of the World
#### 2.5.3 Nutrients
ION was positively correlated with total phosphorus in Western Chaohu Lake [42] and Taihu Lake in China [23], negatively correlated in DECL (this study) and Chaohu Lake [22], and unrelated in Songhua Lake [22] and Taihu Lake in China [22]. Regarding other forms of phosphorus, dissolved inorganic phosphorus was negatively correlated with GSM in Chaohu Lake, China [22]. Dissolved inorganic phosphorus and dissolved organic phosphorus were unrelated to GSM in Songhua Lake, and unrelated to MIB and ION in Songhua Lake and Chaohu Lake, China [22]. Concerning orthophosphate, GSM was positively correlated with it in Western Chaohu Lake, China [42], MIB was negatively correlated with it in seven landscape lakes in Beijing [45; 69], and no correlation was found with the four odorants in DECL in China (this study). Furthermore, research explored the relationship between the N/P ratio and odorants. The N/P ratio was positively correlated with all four odorants in East Taihu Lake, China [44], and negatively correlated in Liangxi River [51], where MIB showed no correlation with the N/P ratio in Lake Yangcheng, China [47]. These comprehensive investigations delved into the intricate relationships between T/O compounds and various nitrogen and phosphorus forms in diverse water bodies (see references in Table 4). While specific instances illustrated positive correlations, such as the association between GSM and total nitrogen in certain Chinese reservoirs, the overall findings revealed considerable variability across studies. MIB, CYC, and ION exhibited diverse correlations with total nitrogen, emphasizing the complex nature of these interactions. Studies further unveiled nuanced relationships with nitrate and ammonia nitrogen, showcasing positive, negative, or non-significant correlations among different T/O compounds in different locations. Regarding phosphorus forms, the investigation uncovered intricate associations between T/O compounds and total phosphorus, with varying correlations observed for GSM, MIB, CYC, and ION across different water bodies. Additional exploration into other phosphorus forms and the nitrogen-to-phosphorus ratio highlighted the complexity of these relationships, displaying diverse trends in different contexts. The observed variability underscores the need for a contextual and location-specific approach to studying these relationships. Further research is essential to unravel the complexities and refine our comprehension of the factors influencing T/O compounds in aquatic ecosystems.
## 4 Results  
### Data Acquisition of Thermal Image  
Temperature data for each moving speed were acquired using LS-based induction thermography. Table 3 shows the number of frames for each moving speed. The number of frames for each moving speed of 5, 7, 9, 11, 13, and 15 mm/s was calculated to be 2934, 2323, 1652, 1480, 1301, and 1051, respectively.  
When capturing images at a specific moment while the specimen is moving, the temperature of the specimen only increases as it gets closer to the induction coil. Therefore, by applying the process shown in Figure 2, all frame images were accumulated based on a single y-axis line to obtain a single image. Figure 5 shows the accumulated single image for each moving speed. The incorp function was applied to the image so that only the shape of the specimen is visible.  
Figure 4: Aluminum specimen with 6 artificial crack defects. There are naturally occurring foreign substances around the Line 1 crack on the surface.  
Figure 5: _Cont_.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Moving Speed** & **Frame** \\ \hline
5 mm/s & 2934 \\ \hline
7 mm/s & 2323 \\ \hline
9 mm/s & 1652 \\ \hline
11 mm/s & 1480 \\ \hline
13 mm/s & 1301 \\ \hline
15 mm/s & 1051 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The number of frames for each moving speed.  
In the thermal image, thermal noise occurs at the upper part due to foreign substances on the specimen surface, making the crack shape of the first row unclear. The foreign substance on the surface reduces the reflectivity and relatively increases the emissivity, resulting in a relatively high temperature distribution. Therefore, the Gaussian-filtering-based FFT algorithm was applied to remove the distribution caused by the foreign matter and perform thermal equalization.
# ICT Adoption and Stock Market Development: Empirical Evidence Using a Panel of African Countries
## 5 Research Findings and Discussion
### Empirical Results on the Impact of ICT Adoption on Stock Market Development in Africa
This finding also resonates with the findings of [PERSON] (1984) as well as that of [PERSON] and [PERSON] (2010), who reported a positive relationship between the ICT and stock market capitalisation variables. [PERSON] and [PERSON] (2010), in their study, examined the mediating relationship between the spreading of ICT and economic development by investigating specifically how the spreading of ICT, such as broadband internet services and mobile telephone, have influenced a country's market capitalisation. Their results indicated that the number of Internet users, mobile cell subscriptions and fixed broadband subscriptions per 100 individuals each have a statistically strong and positive effect on market capitalisation. The stock market turnover ratio was employed as the dependent variable in the estimations of the third model (Model 3). The only noteworthy finding is that the number of fixed telephone users variable had a positive and significant effect on this metric. This is similar to the finding on the estimation of the second model. The fourth model (Model 4) that was estimated was on the impact of ICT adoption measures on stock market development proxied by number of listed companies per 10,000 people. The estimations did not yield any significant results to report on. It could be that the number of listed companies is not a good proxy for stock market development. The other salient finding to report on is that the financial freedom variable does not seem to have a significant effect on the stock market development. The financial freedom variable measures the extent of regulation of these markets. The a priori expectation was that highly regulated markets were bound to stifle portfolio flows and thus impede the development of stock markets. However, in the case of African stock markets, this seems not to be a deterrent.
# Surface Reconstruction Planning with High-Quality Satellite Stereo Pairs Searching
## 5 Experiments and Evaluation
### Evaluation on Planning Using Single Satellite
However, when stereo quality is to be maximized, the high-quality pairs are searched and added to the schedule, achieving better quality on \(Q_{2}\). By considering both quality and timeliness, the proposed method can keep a balance, which is demonstrated by a smaller difference between the two halves of the year on \(Q_{2,3}\) in Figure 7.