esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Understanding the Function of Aegerolysins
## 2 Aegerolysins
### Bacterial Aegerolysins from Firmicutes
#### 2.5.2 Aegerolysins from Clostridium biferentans  
_Paraclostridium biferentans_([PERSON] and [PERSON] 1918) [PERSON] et al., 2016 (_Clostridium biferentans_) is an anaerobic, motile, Gram-positive bacterium capable of forming endospores. A particular subspecies, _C. biferentans subsp. malaysia_, has been reported to have high mosquitcidal activity and no toxicity against mammals and various other nontarget organisms [40].  
A number of proteins have been identified as potentially mosquitcidal. In 1998, two putative hemolysinin-like proteins, **Cbm17.1** and **Cbm17.2**, were isolated from the bacterium _C. biferentans_ and later designed as aegerolysins [140]. Efficient expression of the _cbm17.1_ and _cbm17.2_ genes _C. biferentans_ was achieved by introducing them as His\({}_{6}\)-tagged proteins into _E. coli_ and _B. thuringiensis_[140]. However, when biological activity of the recombinant proteins **Cbm17.1** and **Cbm17.2** was assayed, no toxicity to mosquito larvae and no hemolytic effect was observed [141; 142]. The Cry operon of _C. biferentans subsp. malaysia_ was analyzed in 2014 and was found to contain four genes, two of which encode aegerolysins, **Cbm17.1** and **Cbm17.2**, and two of which encode larger protein partners, **Cry16 Aa (cbm71)** and **Cry17 Aa (cbm72)**. All of these proteins (**Cry16 Aa/Cry17 Aa/Cbm17.1**/**Cbm17.2**) have been shown to be required for toxicity to larvae of mosquitoes of the genus _Aedes_[40].  
Interestingly, in both _B. thuringiensis_ and _C. biferentans subsp. malaysia_, these aegerolysins and their partner proteins are encoded on an aegerolysin operon and are thus under the control of a single promoter [40].
# Realizing Efficient Security and Privacy in IoT Networks
## 4 System Model
### Optimization of Swift Privacy Rate in a MIMOME
\begin{array}{l}\mathbf{y}_{r}^{j+1}=\mathop{\mathrm{arg\,min}}_{\mathbf{y }_{r}}g^{j}(\mathbf{y}_{r})\\ \text{s.t.}\sum\limits_{i=1}^{r}\mathbf{z}_{r}(i)+P_{m}\leq P_{A}^{\max}\\ \mathbf{z}_{r}(i)\geq 0,\forall i=1,...,r\\ P_{m}\geq 0\\ 0\leq P_{B}\leq P_{B}^{\max}.\end{array}  \tag{36}\]  
Similarly, at this point, if any possible values of \(r\) is deployed, an optimal output will be recorded. The summary of our proposed optimization procedure can be seen in Algorithm 3. Although different optimization methods to tackle this kind of problem were proposed in [13; 18] comparatively, these methods give almost similar outcome, nonetheless, our proposed Algorithm 3 significantly executes with minimal complexity. Our figures illustrate the efficiency and out-performance of our swift optimization algorithm as compared with nonoptimal parameters. ```
1:for\(r=1:A_{Tx}\)do
2: Set \(\mathbf{y}_{r}\) to satisfy the constraints
3:while\(\frac{\left\|\mathbf{y}_{r}^{j}-\mathbf{y}_{r}^{j-1}\right\|}{\left\|\mathbf{y }_{r}^{j-1}\right\|}>\varepsilon\)do
4: execute (28) till \(\mathbf{y}_{r}^{j+1}\) is realized
5: execute (25) using \(\mathbf{y}_{r}^{j-1}\) till \(\bar{\sigma}_{3}^{0}\), \(\bar{\sigma}_{4}^{0}\) is updated
6:\(j=j+1\). 7:endwhile
8:if\(h\left(\mathbf{y}_{r}^{j}\right)<h^{\min}\)then
9:\(h^{\min}=h\left(\mathbf{y}_{r}^{j}\right)\)
10:\(\mathbf{y}^{\min}=\mathbf{y}_{r}^{j}\)
11:endif
12:endfor
13:Revert \(\mathbf{y}^{\min}\). ```  
**Algorithm 3** Swift privacy rate optimization algorithm.
## 3 Results and Discussion
### Determination of Microencapsulation Efficiency (ME)  
The change in the microencapsulation efficiency of microencapsulated olive oil powders is shown in Table 3. Increasing the MD ratio in the wall material increased the microencapsulation efficiency, resulting in more stable capsules. The ME value, varying according to the wall composition, is in the range of 87.09-90.56 for Nizip oil powders and 87.30-90.59 for Kilis oil powders. The highest microencapsulation efficiency was determined to be at a WPI:MD ratio of 1:10 in both olive oil powders. As in our study, the increase in the ratio of maltodextrin to wall material improved the protective properties of the wall materials, as stated by [PERSON] (2018). As the proportion of maltodextrin in the emulsion formulation increases, the microencapsulation efficiency value is statistically significant (\(p<0.05\)). On the other hand, [PERSON] et al. (2018) investigated different emulsification ratios prior to the freeze-drying of olive oil, and showed that a mixture of 50% MD and 50% WPI provided an optimal balance between structural integrity and encapsulation efficiency, achieving an approximately 82% encapsulation efficiency in microcapsules.
## 3 Results and Analysis
### Evaluation of Cooling Energy Performance
#### 3.1.4. Cooling Energy Consumption  
Section 3.1.4 discusses the evaluation of the cooling energy consumption by varying the MLSP. Figure 7 shows the cooling energy consumption for each case, including totals for the chiller, fan, and reheat systems. Chiller energy consumption accounts for the energy used by the chilled water circulation pump. Reheat energy consumption encompasses the energy used by the boiler and hot water circulation pump.  
The chiller energy consumption from Cases #2 to #7 decreased by 3.0%, 5.5%, 7.7%, 8.5%, 8.4%, and 8.2%, respectively, compared to Case #1. This reduction was attributed to the decreased cooling coil loads, which, in turn, lowered the chiller loads. Fan energy consumption remained constant across all simulation cases because the supply air reached the set-point temperature in the chiller before reaching the fans, resulting in the fans operating at minimum loads. For reheat energy usage, Cases #2 to #4 showed the same levels, while Cases #5 to #7 exhibited a minor increase compared to Case #1. Consequently, the total cooling energy consumption for Cases #2 to #7 decreased by 2.1%, 3.8%, 5.3%, 5.9%, 5.8%, and 5.7%, respectively, relative to Case #1, primarily due to reductions in chiller energy consumption. Notably, Case #5 demonstrated the greatest savings among all simulation cases. These results suggest that the MLSP recommended by ASHRAE Standard 90.1 may not be optimal for economizer control in South Korea, highlighting the need to establish a region-specific MLSP that aligns with the OA conditions in South Korea.
# Analytical Performance Evaluation of Massive MIMO Techniques for SC-FDE Modulations
## 6 Performance Results  
In this section, performance results for the three mentioned receivers are presented. For these results, there are different scenarios where the number of transmitting and receiving antennas are changed. Initially, a MIMO scenario with 3 iterations, 4 transmitting antennas and 8 receiving antennas is presented. Then, this scenario is extended to a massive MIMO scenario and the number of antennas was increased 8 times, while keeping the same \(R/P\) ratio and number of iterations.  
For simulation purposes, it is considered that in each MT, \(P\), a SC-FDE modulation is applied transmitting 100 blocks with 256 data symbols (\(N=256\)), selected from a QPSK constellation under Gray mapping. Perfect synchronization and channel estimation are also assumed.  
Performance values are presented as BER values, which in turn is in function of \(E_{b}/N_{0}\), where \(E_{b}\) is the average bit energy associated with the receiving antennas and \(N_{0}\) represents the unilateral power spectral density of the Additive White Gaussian Noise (AWGN) channel noise. The lowest bound for the BER performance that a receiver can reach is fixed in the MFB performance which is also presented.
## 3 Results and Discussion
### Criterion of Interfacial Failure Based on Inclined Shear Test  
The shear strength that was obtained by the single shear test is the maximum shear stress without normal stress. When the normal stress was varied, the critical value of shear stress corresponding to failure was significantly affected. Fixtures with different inclination angles were used in this study to obtain the failure criterion for the interface. Notably, the single shear strength can be regarded as the result under the condition of 90\({}^{\circ}\) inclination angle. Table 5 and Figure 13 show the results of shear tests.  
The experimental results show that the normal stress and shear strength have a linear relationship [26]. Therefore, the criterion of interface failure can be expressed, as follows:  
\[\tau_{max}=\tau_{C}+k\sigma_{C} \tag{2}\]  
where \(\tau_{C}\) and \(k\) are the constants that characterize the interfacial strength properties. According to Figure 13a, the interfacial failure criterion for steel-ECC bonding interface can be expressed as follows:  
\[\tau_{max}=1.3458+0.898\sigma_{C} \tag{3}\]  
Through the above analysis, the following conclusions can be drawn:  
(1) The initiation of interface failure occurs at the maximum interfacial shear stress.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Inclination Angle (\({}^{\circ}\))**} & \multicolumn{3}{c}{**Shear Strength (MPa)**} & \multicolumn{3}{c}{**Normal Stress (MPa)**} \\ \cline{2-7}  & **Measured Value** & **Mean Value** & **Standard Deviation** & **Measured Value** & **Mean Value** & **Standard Deviation** \\ \hline \multirow{3}{*}{26.57} & 7.653 & & & 6.845 & & \\  & 7.187 & 7.439 & 0.235 & 6.428 & 6.654 & 0.211 \\  & 7.477 & & & 6.687 & & \\ \hline \multirow{3}{*}{38.66} & 3.925 & & & 3.065 & & \\  & 4.363 & 4.119 & 0.223 & 3.407 & 3.217 & 0.174 \\  & 4.069 & & & 3.177 & & \\ \hline \multirow{3}{*}{45.00} & 2.988 & & & 2.113 & & \\  & 3.192 & 3.193 & 0.206 & 2.257 & 2.258 & 0.146 \\  & 3.399 & & & 2.404 & & \\ \hline \multirow{3}{*}{90.00} & 1.564 & & & 0.000 & & \\  & 1.503 & 1.525 & 0.034 & 0.000 & 0.000 & 0.000 \\  & 1.507 & & & 0.000 & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of shear tests.  
Figure 13: Shear test results: (**a**) Interfacial failure criterion, (**b**) Mohr–Coulomb criterion.  
2. The initiation of interface failure is influenced by normal stress at the interface. The greater the interfacial compressive stress, the greater the maximum interfacial shear stress.
3. The interface failure criterion is similar to Mohr-Coulomb criterion (shown in Figure 13b), i. e., the effect of interface compressive stress on the maximum interface shear stress is linear when failure occurs. The criterion is found to exclude the effect of experimental conditions and reflect the intrinsic strength characteristics of interface.
# Unconstrained Bilingual Scene Text Reading Using Octave as a Feature Extractor
## 5 Experiments and Discussions
### Implementation Details  
The proposed model was first pre-trained using our synthetically generated bilingual dataset and Synthetic [22], then fine-tuned using the union of other real-world datasets indicated in Section 4.2.2. Due to the lack of real sample images in the fine-tuning stage, data augmentation and multi-scale training were applied by randomly modifying brightness, hue, contrast, the angle of the image between \(-30\) and \(30\). Following [9], for multi-scale training, the shorter sides of the input images were randomly resized to five scales (600, 800, 1000, 1200, 1400). We used Adam [46] (base learning rate = 0.0001, \(\beta 1\) = 0.9, \(\beta 2\) = 0.999, weight decay = 0) as an optimizer. Following the result of [21], we set the value to \(\alpha\) = 0.25 which denotes the ratio of the low-frequency part.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Language** & **Total Images** & **Training** & **Testing** & **Type** \\ \hline \multirow{3}{*}{Ours} & Real & Bilingual & 1200 & 600 & 600 & Irregular \\  & Synthetic & Bilingual & 500,000 & 500,000 & - & Regular \\ ICDAR2013 [44] & English & 462 & 229 & 233 & Regular \\ ICDAR2015 [40] & English & 1500 & 1000 & 500 & Regular \\ Synthetic [22] & English & 600,000 & - & - & Regular \\ Total-Text [45] & English & 1555 & 1255 & 300 & Irregular \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of datasets applied for training and testing the proposed model.  
Figure 3: Sample of collected real scene text images.  
The experiment of the proposed bilingual scene text reading model is conducted on the Ubuntu machine containing Intel Core i7-7700 (3.60 GHz) CPU with 64 GB RAM and GeForce GTX 1080 Ti 11176 MiB GPU. For the implementation, we use Python 3.7 and PyTorch1.2.
## 3 Results and Discussion
### General Applications  
The 51 geospatial layers used for the above clustering analysis have previously been used to predict sound levels across the CONUS and were selected because of their potential relationship with outdoor sound levels [26]. These layers are therefore potentially relevant to geospatial acoustic modeling and, more broadly, characterizing acoustic environments. The k-means clustering analysis above attempted to identify geospatially distinct clusters with unique characteristics from the 51 layers. Because these layers may be useful in characterizing acoustic environments, it is not unreasonable to suppose that the clusters may also correspond to acoustically distinct environments. In general, the clusters correlate most strongly with the type of land cover, and different types of land cover likely correspond to different acoustic sources and propagation effects (i.e., distinct acoustic environments).  
Characterizing distinct acoustic environments has potential applications in land use planning, both in anthropogenic and natural environments. Of particular interest are acoustic environments corresponding to large amounts of anthropogenic noise, since ambient noise often results in much higher sound pressure levels and can have harmful effects on human health [42] and wildlife [2]. Identifying acoustic environments with high/low amounts of anthropogenic noise is valuable in urban planning, public health, and wildlife policy and management (including identifying suitable wildlife corridors). More broadly, characterizing distinct acoustic environments is important to understanding and classifying different geographic locations, as well as finding commonalities between regions independent of physical distance.
# Potassium Recovery from Potassium Solution and Seawater Using Different Adsorbents
## 3 Results and Discussion
### K Sorption by Different Adsorbents
#### 3.1.1 K Sorption Isotherms Using KCl Solutions
The specific surface areas of the natural zeolite and the ammonium acetate-treated zeolite were 29.2 and 33.0 m\({}^{2}\) g\({}^{-1}\), respectively, and the pore volumes of  
Figure 1: (**a**) Adsorption isotherm; (**b**) Langmuir isotherm; and (**c**) Freundlich isotherm of potassium (K) in potassium chloride (KCl) solutions on natural zeolite (zeolite), ammonium acetate\(-\)treated natural zeolite (Treated zeolite), and manganese nodule ( manganese nodule) at pH 4.57.  
the natural zeolite and the ammonium acetate-treated zeolite were 0.088 and 0.10 cm\({}^{3}\) g\({}^{-1}\), respectively. Figure 2 shows the sorption isotherms of K on three different adsorbents under neutral conditions (i.e., pH = 6.99). The sorption data were fitted to the Langmuir and Freundlich isotherm models (Figure 2b,c), and the parameters calculated are shown in Table 1.  
The K sorption capacities (i.e., \(q_{e}\)) increased with increasing K concentrations at equilibrium (i.e., \(C_{e}\)) (Figure 2a). Considering the R\({}^{2}\) values, the Freundlich isotherm  
Figure 2: (a) Adsorption isotherm; (b) Langmuir isotherm; and (c) Freundlich isotherm of potassium (K) in potassium chloride (KCl) solutions on natural zeolite (zeolite), ammonium acetate –treated natural zeolite (treated zeolite), and manganese nodule (manganese nodule) at pH 6.99.  
model fitted the K adsorption on the natural zeolite and manganese nodule better than the Langmuir isotherm model, while the Langmuir isotherm model fitted the K adsorption on the ammonium acetate-treated zeolite better than the Freundlich isotherm model (Table 1). The maximum sorption capacity (i.e., \(q_{m}\)) derived from the Langmuir isotherm was greater for the manganese nodule (i.e., 9.7 mg g\({}^{-1}\)) than the zeolite-based adsorbents than the (i.e., 5.8 and 8.1 mg g\({}^{-1}\)). However, the affinity of the binding sites (i.e., \(K_{d}\)) was greater for the zeolite than the other adsorbents (Table 1).
## Result and Discussion
### Result of Resistance of Concrete to Chloride Ion Penetration Test (RCPT)  
The ZWR exhibited the lowest total passing charge (Coulomb: C) followed by OPC, WR-In, FA-Co, Silica-Co, and Sand-Co. The RCPT was conducted by cutting each \(\mathcal{O}100\times 200\) mm cylindrical specimen to a height of 50 mm, coating the surface with epoxy, and fabricating three samples for each specimen. OPC showed the values ranging from 4298 to 5663, whereas Sand-Co, Silica-Co, and Fa-Co showed the values ranging from 6289 to 7414. The WR-In specimen showed the values ranging from 5242 to 6310, and it exhibited a lower deviation than the specimens mentioned above. The specimen that exhibited the lowest deviation was the ZWR specimen, and its standard deviation was approximately 500. The total passing charge of the ZWR specimen was significantly lower than that of OPC, which is the reference specimen. An average value of 1438 was obtained from the three tests, and this was 29% of the average value of OPC. Table 13 shows the results of RCPT conducted at 28 curing days.  
Figure 11: Result of the water penetration test specimen.  
The standard deviation may slightly vary depending on the conditions and time of immersing the specimens in water, but pores were not filled with the water-repellent's active ingredient for the specimens other than ZWR. It appears that the penetration and movement of the chlorides contained in water occurred in areas where water repellency was lost. The ZWR appeared to be resistant to the chloride penetration caused by water penetration because it was evenly distributed in cement mortar. In the case of the Sand-Co, Silica-Co, FA-Co, and WR-In specimens, the air content was high and pores inside them increased, and this appears to have reduced resistance to water penetration. Therefore, chlorides could move freely, and penetration resistance was low; thus, the total passing charge was much higher than that of the ZWR specimen. Figure 12 shows the results of RCPT.
## 3 Results and Discussion
### EMI Shielding Properties
This indicates that absorption is the dominant EMI shielding mechanism in these composite samples, consistent with findings reported in the literature [43]. At this point, it should be noted that the addition of GB/OD@CCF-20 with the GB/OD@CCF-30 samples was studied in order to verify whether higher additive concentrations could reach an SE\({}_{\text{T}}\) of \(\sim\)15-20 dB, which is considered sufficient for EM shielding, e.g., electronic devices, etc. [45]. Similar samples to those described above were studied, making cement boards that can be used outdoors. Figure 13 depicts the S\({}_{21}\) (transmission; Figure 13a) and S\({}_{11}\) parameters (reflection; Figure 13b) of the CB/OD@CCF composite samples for a series of concentrations of octadecane shape-stabilized in carbon foam, OD@CCF, in the frequency range of 3.2-7.0 GHz. Like previously, electromagnetic absorption appears to be the primary shielding mechanism, as evidenced by the steadily increasing absorption levels with higher OD@CCF content in the cement board composites. Figure 14 depicts the absorption (Figure 14a) and total shielding efficiency (SE\({}_{\text{T}}\)) (Figure 14b) spectra of the CB/OD@CCF composite samples for a series concentrations of shape-stabilized octadecane in carbon foam, OD@CCF, in the frequency range of 3.2-7.0 GHz. It is worth noticing that the \(SE_{A}\) levels of the cement board samples are higher than the corresponding ones of the gypsum samples, reaching a value of \(\sim\)19.5 dB for the combination of CB/OD@CCF-20 with the CB/OD@CCF-30 samples (compared to \(\sim\)18 dB for GB/OD@CCF-20 and CB/OD@CCF-20). Concrete and cementitious composites, in general, are very poor electrical conductors, but still conductors, at least compared to gypsum [46, 47]. This is actually the reason for their enhanced EMI effect. Moreover, it has been demonstrated that the dry resistance of cement specimens is influenced by the  
Figure 14: Absorption (**a**) and SE\({}_{\text{A}}\) (**b**) measurements across the 3.2 to 7.0 GHz frequency range (C-band) for various cement board samples. Figure 13: Transmission S\({}_{21}\) (**a**) and reflection S\({}_{11}\) (**b**) coefficients from 3.2 to 7.0 GHz (C-band) for different cement board samples. material's porosity and the presence of free-moving ions [48]. As shown in Figures 4 and 5, the pore sizes inside the gypsum samples are much smaller, compared to cement boards. As a result, large pathways facilitate free ion movement in CB/OD@CCF composite boards (Figure 4), which are closed in GB/OD@CCF samples, and consequently, the mean path of ion movement increases [49]. In turn, this changes the electrical conductivity of gypsum (compared to cement) boards and increases the resistance, which decreases EMI SI accordingly.
# Design and Accuracy Analysis of a Micromachine Tool with a Co-Planar Driving Mechanism
## 3 Error Model and Analysis
### Tilt-Drive Angular Position Error and Feed Error Analysis  
Since the tilt-drive angle of the mechanism affects the axis resolution, if assembly errors (between the ballscrew and the sliding rod) and errors in the ballscrew-sliding rod motion exist, it will cause motion errors for the platform movement. The influence of tilt-drive angle error on platform error was analyzed as a basic reference for design and assembly. The X-axis tilt-drive angle error was taken as an example to explain. The rotation error of the sliding rod is shown in Figure 14. When the ballscrew assembly generates rotation errors, it will result in resolution error for the platform motion. Different errors at different tilt-drive angles have different effects on the feed resolution of the platform. The prototype machine was designed with a tilt-drive angle of 25.84\({}^{\circ}\), and this was also used for analysis. When the ballscrew assembly produces rotation errors, the movement ratio of the ballscrew and the platform changes, which also results in a change in the feed resolution of the platform (as illustrated in Figure 15). It can be seen from Figure 15, when the ballscrew moves for a distance B and there is a tilt-drive angle error of ry, an error of \(\Delta\)x\({}_{7}\) occurs in the feed direction (i.e., the X-axis direction). When the ballscrew moves to the next position (B+\(\Delta\)B), an additional feed error \(\Delta\)x\({}_{7}\) is introduced. It is clear that when a tilt-drive angle error is present, the feed error of the platform is different at different travel locations of the ballscrew, and the error has a proportional relationship with the travel distance of the ballscrew, following the triangle principle. By substituting the condition that the tilt-drive angle is 25.84\({}^{\circ}\) into Equation (2), the feed resolution of the platform obtained is 900 nm. If the tilt-drive angle has an error \(\pm\)0.5\({}^{\circ}\), the feed resolution will, respectively, become 896.2 nm and 903.7 nm for tilt-drive angles of 26.34\({}^{\circ}\) and 25.34\({}^{