esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Capacity Optimal Design Method
### Decision-Making  
WPSO can be used to obtain a series of Pareto solutions, including both the global optimal solution and suboptimal solutions. However, based on practical considerations, it is necessary to select a single preferred solution from the Pareto solutions. Therefore, to select the most suitable optimization solution from multiple options in accordance with engineering practicality, the following three different optimization strategies are considered.  
Strategy I: Prioritize increasing the load-bearing capacity of the bridge structure without increasing the material costs. This strategy is generally applicable to bridges that require further improvement in the load-bearing capacity and the material utilization rate of the upper structure.  
Strategy II: Prioritize minimizing the material costs while ensuring that the load-bearing capacity of the bridge structure is not reduced. This strategy is generally applicable to bridges with sufficient safety reserves for which costs must be saved to a certain extent. Scholars have previously applied this strategy to bridge optimization [19; 20].  
Strategy III: Prioritize achieving a balance between the load-bearing capacity and material costs of the bridge structure. This strategy is an equilibrium strategy and needs to be selected in consideration of the actual situation.  
These three optimization strategies are proposed based on practical engineering experience and focus on the internal forces of the key components and the engineering quantity of the upper structure of the bridge. Considering the engineering application context of this study, Strategy II is chosen as the basis for selecting the optimal optimization solution.  
The optimization design process based on the WPSO-RSM model is illustrated in Figure 1, and it mainly consists of the following three steps.  
Step 1: Develop the capacity response surface model.  
A set of bridge samples is generated using the central composite design (CCD) method after determining the ranges of different design parameters. Then, the capacity-related values of bridge samples are subjected to polynomial fitting to determine the model parameters, which can be applied to determine the capacity response surface model by adjusting the polynomial form.  
Step 2: Obtain the Pareto optimal solutions.  
The bridge samples are randomly generated and considered the initial particles. Considering the material cost, the bearing capacity, and the determined constraint conditions, the fitness-weighted matrix is introduced as a Euclidean norm equation, which can be applied to sort the function priority of fitness functions. Considering the specific opti  
Figure 1: The flowchart of the capacity design process based on WPSO-SRM.  
mization strategies, WPSO with the determined parameters is applied to obtain the Pareto optimal solutions.  
Step 3: Determine the optimal design scheme.  
The final optimal design scheme can be achieved by comparing the differences in the Pareto optimal solutions of the three optimization strategies.
## Results
### Seasonal Variations of Cyanobacteria, MCs and T&O Compounds in the EDWS
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Spring** & **Summer** & **Autumn** & **Winter** \\ \hline Chlorophyll+a (ng/L) & 9.0 (5.4-13.7) & 38.5 (22.2-68.0) & 22.2 (8.0-55.7) & 24.6 (5.7-47.9) \\ Phytoplankton density (\(\times 10^{4}\) cells/mL) & 1.7 (0.5–4.0) & 19.1 (18.0–20.2) & 12.7 (5.7–17.2) & 12.2 (7.0–22.1) \\ Cyanobacterial density (\(\times 10^{4}\) cells/mL) & 1.5 (0.3–3.9) & 18.5 (18.6–19.5) & 12.5 (5.4–17.0) & 12.0 (6.7–21.9) \\ _Microcystis density_ (\(\times 10^{4}\) cells/mL) & 0.1 (_nd._–0.3) & 11.6 (2.8–16.5) & 8.8 (0.3–13.1) & 2.3 (0.9–4.8) \\ _Dolichowerment_ density (\(\times 10^{4}\) cells/mL) & 1.4 (0.2–3.8) & 28 (0.6–5.2) & 1.6 (1.1–2.2) & 9.4 (5.7–5.5) \\ EMC-LR (ng/L) & 0.05 (0.03–0.06) & 0.98 (0.07–2.80) & 0.65 (0.07–1.04) & 0.14 (0.02–0.55) \\ LMC-LR (ng/L) & 0.04 (0.02–0.08) & 1.15 (0.12–3.22) & 0.11 (0.03–0.18) & 0.36 (0.04–1.31) \\ EMC-RR (ng/L) & 0.12 (0.06–0.17) & 0.31 (0.21–0.59) & 1.42 (0.08–24.5) & 0.12 (0.06–0.22) \\ LMC-RR (ng/L) & 0.07 (0.05–0.11) & 0.16 (0.11–0.25) & 0.56 (0.03–1.47) & 0.41 (0.10–1.20) \\ EMC-TK (ng/L) & 0.07 (0.04–0.12) & 0.18 (0.11–0.27) & 1.34 (0.21–28.3) & 0.46 (_nd._–0.74) \\ ID-YC-VR (ng/L) & 0.01 (_nd._–0.02) & 0.37 (0.07–12.07) & 0.82 (0.05–13.5) & 0.11 (0.0–0.31) \\ _d_-geosmin (ng/L) & 16.8 (1.6–47.8) & 8.1 (4.8–12.1) & 3.7 (1.3–7.2) & _7.7 (n.d._–17.2) \\ _p_-geosmin (ng/L) & 0.7 (0.2–1.7) & 0.6 (0.2–1.1) & _nd._ & 0.4 (_nd._–0.9) \\ _d_-2-methyl isobonen (ng/L) & 11.1 (4.5–30.7) & 602 (_nd._–193.4) & 14.1 (0.7–27.5) & 5.4 (_nd._–11.8) \\ _p_-2-methyl isobonen (ng/L) & 1.3 (0.4–2.0) & 12.6 (0.6–4.0) & 0.5 (_nd._–1.3) & 0.5 (_nd._–1.7) \\ _d_-\(g\)-cycloctral (ng/L) & 26.5 (8.0–5.01) & 21.9 (8.4–4.92) & 14.9 (2.3–33.8) & 10.6 (_nd._–20.0) \\ _p_-\(g\)-cycloctral (ng/L) & 4.7 (1.0–10.9) & 26.3 (5.4–5.34) & 0.0 (9.8–7.8) & 42.5 (5.4–16.3) \\ _d_-\(g\)-phonon (ng/L) & 5.8 (0.4–28.4) & 10.8 (0.3–23.2) & 7.3 (_nd._–21.8) & 0.4 (_nd._–1.1) \\ _p_-\(\beta\)-ionone (ng/L) & 14.5 (1.8–30.1) & 53.4 (2.6–152.7) & 4.8 (0.7–11.6) & 47.1 (3.5–115.8) \\ \hline \end{tabular} \(n.d.\) below the lower-limit of the calibration range.  
\end{table} 
Table 1: Mean and ranges of chlorophyll-\(q\), phytoplankton density, extra- (B) and intra-cellular (I) microcyts and dissolved (_d_)-and particle-bound (_p_)- taste-and-odor compounds in the eastern drinking water source of Lake Chaohu (including Intake-A, B and C) during four seasons.
## 3 Results
### Volatile Compounds from Aplid-Infested and Endophytically-Colonized Leaves  
A large number of volatile organic compounds (VOCs) were emitted from aphid-infested, endophytically-colonized leaves: terpenoids, phenylpropanoids/benzenoids, amino acid derivatives and fatty acid derivatives such as: aldehydes, alcohols, and ketones (Figure 6). Aldehydes were the main group of VOCs emitted by plants; E-2-hexenal and (E,Z)-2,6-nonadienal were emitted at the highest concentrations. In contrast, terpenoid and phenolic derivatives were emitted in the lowest concentrations. The full listing of VOCs emitted are in Supplementary Tables S1-S3.  
These VOCs are biosynthesized as primary and secondary metabolites by complex enzyme pathways distributed across different organelles or compartments within the plant. In general, the vegetative tissues of melon only release small quantities of VOCs but can be induced by mechanical damage by herbivores. In this regard, the use of homogenized melon plants (i.e., complete destruction of plant tissue) was effective in establishing differences in the volatile profiles emitted from leaves that had been inoculated with different isolates of fungi compared with controls.  
Finally, from Supplementary Tables S1-S3 VOCs could largely be grouped into \(\Delta\)aldehydes, \(\Sigma\)alcohols, and \(\Sigma\)ketones. Specifically: \(\Sigma\)aldehydes = butanal, 3-methyl-butanal, pentanal, hexanal, 3-methyl-hexanal, (E)-2-pentanal, heptanal, 5-methyl-hexanal, (Z)-2-hexenal, (E)-2-hexenal, (Z)-4-heptanal, octanal, (Z)-2-heptrenal, (E)-2-ocrenal, (E)-6-nonenal, (E,E)-2,4-hexadienal, nonanal, (E,E)-2,4-heptalaienal, (E)-2-nonenal, (E,Z)-2,6-nonadien, (E)-4-oxohex-2-enol; \(\Sigma\)alcohols = ethanol, 1-penten-3-ol, 4-methyl-1-pentanol, (Z)-3-hexen-1-ol, (E)-2-hexen-1-ol, 1-octen-3-ol, 2-ethyl-1-hexanol, 2-propyl-1-pentanol; \(\Sigma\)ketones = 6-methyl-2-heptanone, 3-octanone, 6-methyl-5-hepten-2-one, (E,E)-3,5-octaleri-2-one. There were also: terpenoic derivates = \(\beta\)-cyclocitral, eucarvone, 2-pinen-4-ol, \(\beta\)-cital, granial, isomethyl-\(\alpha\)-ionone, E-\(\beta\)-ionone, 2,2,6-trimethyl-cyclohexanone, \(\beta\)-ionone; and \(\Sigma\)phenolic derivates = benzaldehyde, acetophenone, benzyl alcohol, 2-phenyl-isopropanol, phenol, benzophenone, toluene.  
Figure 5: Percentage of _Aphis gossypii_ adults choosing one of the two leaves offered in the choice experiment. The following choices were offered: Unsprayed leaf from a fungus-sprayed plant (i.e., endophytically colonized only); Unsprayed leaf from a control plant. Bars denote percentage of aphids choosing either the control or the endophytically-colonized leaf out of the total number of aphids making a choice. Circles denote the proportion of aphids orienting towards the unsprayed endophytically-colonized leaf (black), the unsprayed leaf from a control plant (grey) and aphids showing no decision (white).
## 2 Materials and Methods
### Characteristics of the Database
#### 2.1.4 Technological Parameters  
Three parameters related to the technology of works were also selected (i.e.,: type of excavation, reinforcement of the support, and rock burst prevention). Based on the first of the mentioned parameters, the samples were classified as longwall galleries (90.4% of samples), drilled headings (7.4% of samples), and opening-out headings (2.2% of samples). Such an uneven share results from rock bursts occurring mainly in longwall galleries. Advancing the longwall face generates mining pressure in the unmined coal body, so a higher stress level can be expected close to the main gate and tailgate. Drilled headings are also at risk of rock bursts. Coal may be ejected from the exposed working face directly into the opening, and the possibilities to protect against it are limited. Opening-out headings have a transport and ventilation function. These excavations are mostly old and located in protective pillars with high-stress levels. Steel arches are the basic support for openings. This support can be reinforced, which may prevent rock bursts or minimize excavation damage. Support reinforcement was applied in 90.9% of the samples in the database. Such a large share resulted from the fact that the dataset came from a mine with a high risk of rock bursts, where support reinforcement, mainly by props, is commonly employed. This applies, in particular, to openings with seismic activity and an increased risk of rock burst. The share of types of support reinforcement is as follows: props (97.2%), props and bolts together (1.2%), wooden support (0.9%), steel horseheads (0.35%), and props and steel horseheads and reduced spacing between steel arches together (0.35%). In the selected mine, active rock burst prevention is used on a large scale, based mainly on distress blasting in coal seams and roof rocks. Destress blasting for rock burst prevention was performed for 57.9% of the samples in the database. In 54.8% of them, long-hole destress blasting in roof rocks was executed together with destress blasting in coal seams; in 40% of these samples, long-hole destress blasting in roof rocks was performed independently; and in 5.2% of them, only destress blasting in coal seams was executed.
## 3 Materials and Methods
### Model Size and Parameters  
This paper studies a mining roadway located in a coal mine the working face in the Datal Coal Mine of China. It is rectangular and there is no cavity near it. The actual roadway has the characteristics of symmetry along the central axis of the cross section of the roadway in terms of lithology, roadway shape, and buried depth. This simulation takes the \(-410\) m horizontal rock roadway of the coal seam floor of the Datal coal mine as the prototype, and the model takes half of the structure of the roadway for numerical simulation. The model grid division is shown in Figure 5. The size of the model and roadway is constantly changing, when we studied the influence of the height-width ratio on results. The method of selecting the size of the model was that the length of the fixed model is \(60\) m, the width of the fixed model is \(40\) m, the height of the fixed model is \(40\) m, and the width of the roadway is \(4\) m. When the height-width ratio of the roadway increases, the height of the roadway also increases. For example, when the height-width ratio takes the maximum value of \(2\), the half-height of the roadway takes the maximum value of \(4\) m, the distance between the upper boundary of the model and the center of the roadway is \(20\) m, and the ratio of the two is \(5\). Therefore, when studying the influence of the height-width ratio on results, the distance between the model boundary and the center of the roadway is at least \(5\) times greater than the maximum size of the roadway. When studying the influence of the lateral pressure coefficient on the results, the distance between the model boundary and the center of the roadway is always \(10\) times the maximum size of the roadway.  
Figure 5: Three-dimensional model used for numerical calculation of roadway sidewall failure (height-width ratio \(h/w=1\)).  
The boundary conditions set in the numerical simulation are illustrated in Figure 6. The top part of the rock mass represents the formation load. The x-direction boundaries are fixed at both ends. The y-direction boundaries are fixed, and the lower end of the z-direction is also fixed. Horizontal stress is applied to the internal elements of the numerical model based on the lateral pressure coefficient.  
In this simulation, the constitutive model adopts the Mohr-Coulomb model. The surrounding rock of the roadway is diabase. The main material properties of diabase are elastic modulus, Poisson's ratio, density, friction angle, and cohesion. In order to obtain these material properties, we made the rock samples taken in the field into standard specimens and carried out unit axial compression and triaxial tests. The mechanical properties of diabase are taken from the high brittle diabase of the coal seam floor of the Datai coal mine, as shown in Table 2. Among them, \(E\) is the [PERSON]'s modulus of the diabase, \(v\) is the [PERSON]'s ratio of diabase, \(c\) is the cohesion of the diabase, and \(\varphi\) is the internal friction angle.
## 2 Materials and Methods
### Concentration Weight Analysis (CWT)  
While the Potential Source Contribution Function (PSCF) analysis method offers insights into the probability of each pollution trajectory transiting through a specific grid, it does not provide a quantification of the pollution level associated with these trajectories within the study area. To address this limitation, this study incorporates concentration weight analysis (CWT) to compute the concentration weight attributed to each grid within the source area [60; 61]. To evaluate the pollution magnitude originating from diverse trajectory source regions impacting the study area, the computational formula for concentration weight analysis (CWT) is delineated in Equation (4).  
\[CWT_{ij}=\frac{\sum\limits_{i=1}^{M}C_{l}\times T_{ijl}}{\sum\limits_{i=1}^{M }T_{ijl}} \tag{4}\]  
Within the equation, \(CWT_{ij}\) signifies the average weighted concentration of PM\({}_{2.5}\) (\(\mu\)g\(\cdot\)m\({}^{-3}\)) corresponding to the grid (\(i,j\)). Here, \(M\) denotes the total number of trajectories, \(l\) stands for an individual trajectory, \(C_{l}\) signifies the PM\({}_{2.5}\) concentration (\(\mu\)g\(\cdot\)m\({}^{-3}\)) associated with trajectory \(l\) passing through grid (\(i,j\)), and \(T_{ijl}\) denotes the residence time (24 h) of trajectory \(l\) within grid (\(i,j\)). To enhance accuracy and minimize errors, the CWT analysis aligns with the PSCF analysis, incorporating an identical weight function to counteract discrepancies arising from a limited number of grids. Thus, the adjusted concentration weight value is denoted as WCWT = \(W_{ij}\times CWT_{ij}\), where \(W_{ij}\) acts as the weight coefficient for the corresponding grid.
## 3 Results  
### Data  
GF-3 is China's first independently developed C-band multi-polarization SAR satellite [57]. It offers a maximum imaging resolution of 1 m, providing high-precision earth observation data. The satellite supports 12 operational modes, including strip map mode, scan mode, and spotlight mode, to meet various observation requirements. In strip map mode, the imaging swath width can reach up to 400 km, enabling the rapid coverage of large areas [58]. In this study, 10 GF-3 satellite images with a resolution of 1 m were used. These images cover two study areas with the following longitude and latitude ranges: 103.9\({}^{\circ}\)-104.41\({}^{\circ}\) E, 30.31\({}^{\circ}\)-31.06\({}^{\circ}\) N, and 116.06\({}^{\circ}\)-116.78\({}^{\circ}\) E, 38.92\({}^{\circ}\)-40.30\({}^{\circ}\) N. According to the atmospheric delay model, the atmospheric delay is primarily influenced by the VTEC of the ionosphere and the PWV of the neutral atmosphere. The ionospheric VTEC is obtained from the CODE GIM, with a spatial resolution of 5\({}^{\circ}\)\(\times\) 2.5\({}^{\circ}\) (longitude \(\times\) latitude) and a temporal resolution of 1 h (2 h before 19 October 2014). The longitude range is 180\({}^{\circ}\) E\(\sim\)180\({}^{\circ}\) W, and the latitude range is 87.5\({}^{\circ}\) N\(\sim\)87.5\({}^{\circ}\) S. The neutral atmosphere PWV is obtained from the ERA5 data, with a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) (longitude \(\times\) latitude); the latitude range is 90\({}^{\circ}\) N\(\sim\)90\({}^{\circ}\) S; and the longitude range is 180\({}^{\circ}\) E\(\sim\)180\({}^{\circ}\) W. The VTEC and PWV are both affected by geographic location, time, and other relevant variables. Consequently, the images were grouped into four categories based on imaging time, geographic location, and orbital direction (ascending or descending track), reflecting temporal differences, as shown in Table 1 and Figure 4.
# Green Production and Biotechnological Applications of Cell Wall Lytic Enzymes
## 5 Bio-Applications of CWLEs
### Use of CWLEs for the Production of Biofuels from Agricultural Feedback
Under mixotrophy and specific growth conditions (e.g., nitrogen starvation), these microalgae may accumulate lipids up to 60% of their dry weight [136; 139]. Interestingly, different _Chlorella_ species such as _C. prototechnides, C. vulgaris_, and _C. sorokiniana_ may grow heterotrophically, accumulating up to 500% and 270% more biomass and lipids, respectively, than those observed under photoautotrophic conditions [140]. Subsequently, these lipids can be converted in biodiesel by a transesterification process. Compared to the major biofuels produced worldwide (i.e., bio-ethanol from sugar cane and veg-diesel from oil crops), the third-generation biofuels are considered as a promising option, since microalgae are highly productive and provide a solution to the paradigm \"food vs. fuel\" [141]. Alternatively, agricultural feedstock can be used as feed for promoting the anaerobic digestion of methanogenic bacteria [142]. Pretreatment of lignocellulosic biomass with CWLEs results in a higher biogas production yield by methanogenic bacteria, thus pointing to the increasing market of different enzyme-based products with cellulolytic activities. These products are obtained by culturing fungi and bacteria with cellulolytic activities and are mainly composed of endo-glucanases, cellobiohydrolases, and hemicelllases such as xylanases and galactanases, while they lack ligninases [33]. Therefore, use of chemical pretreatments capable of eliminating lignin and other phenolic molecules (i.e., alkaline and acid treatments) from lignocellulosic material is desirable because of the absence of lacases and peroxididases in such blends [132]. Moreover, lignin irreversibly binds to the CBM of CWLEs, thus poisoning the enzyme. It is worth noting that the low productivity of these cellulolytic microbes impacts the cost of their CWLE-based products. According to this scenario, the exploitation of plant-expressed CWLEs in the context of renewable energy may be highly competitive (Figure 4b).
# Authentication Based on Non-Interactive Zero-Knowledge Proofs for the Internet of Things
###### Abstract  
This paper describes the design and analysis of a new scheme for the authenticated exchange of confidential information in insecure environments within the Internet of Things, which allows a receiver of a message to authenticate the sender and compute a secret key shared with it. The proposal is based on the concept of a non-interactive zero-knowledge proof, so that in a single communication, relevant data may be inferred to verify the legitimacy of the sender. Besides, the new scheme uses the idea under the Diffie-Hellman protocol for the establishment of a shared secret key. The proposal has been fully developed for platforms built on the Android Open Source Project, so it can be used in any device or sensor with this operating system. This work provides a performance study of the implementation and a comparison between its promising results and others obtained with similar schemes.  
Keywords:non-interactive zero-knowledge proof; authentication; mobile _ad hoc_ network +
Footnote †: journal: Sensors
## 3 Optimization Problem Formulation
### Constraints  
The problem constraints include two parts; the first-stage constraints and second-stage constraints. The first-stage ones are associated with the base case scenario (i.e., deterministic operating condition), and can be expressed as follows:  
* Power balance in steady state; Equation (18) represents the active power balance in MG in steady state [21]. \[\sum\limits_{i=1}^{Ngb}P_{i,t}+\sum\limits_{w=1}^{Nwb}P_{w,t}+\sum\limits_{p =1}^{Np}P_{p,t}+\sum\limits_{k=1}^{Nb_{d}}P_{k,t}^{d}=L_{b,t}+\sum\limits_{k=1} ^{Nb_{c}}P_{k,t}^{c}+\sum\limits_{l=1}^{Lib}F_{i,t}\quad\forall b,\forall t\] (18)where, \(F_{I,t}\) is power flow through line \(l\) in period \(t\), (\(F_{I,t}=\frac{1}{X_{l}}(\delta_{ls}-\delta_{lr})\)), \(\delta_{x,t}\) is voltage angle at node \(x\) in period \(t\). The power flow through line \(l\) is limited as: \[-F_{I,t}^{\min}\leq F_{I,t}\leq F_{I,t}^{\max}\quad\forall l,\forall t\] (19)
* Real power generation constraints; The real power generated by DG units are constrained by (20) and (21) [21]. \[P_{i,t}\leq P_{i}^{\max}u_{i,t}-R_{i,t}^{U}\quad\forall i,\forall t\] (20) \[P_{i,t}\geq P_{i}^{\min}u_{i,t}+R_{i,t}^{D}\quad\forall i,\forall t\] (21)
* Generation-side reserve limits; Constraints (22)-(24) impose limits on the provision of spinning reserve in terms of up and down regulations, as well as non-spinning reserve from the generating units. \[0\leq R_{i,t}^{U}\leq R_{i,t}^{U,\max}u_{i,t}\quad\forall i,\forall t\] (22) \[0\leq R_{i,t}^{D}\leq R_{i,t}^{D,\max}u_{i,t}\quad\forall i,\forall t\] (23) \[0\leq R_{i,t}^{NS}\leq R_{i,t}^{NS,\max}(1-u_{i,t})\quad\forall i,\forall t\] (24)
* Demand-side reserve limits; Constraints (25) and (26) restrict the procurement of up and down reserves from the responsive loads. \[0\leq R_{j,t}^{U}\leq R_{j,t}^{U,\max}\quad\forall j,\forall t\] (25) \[0\leq R_{j,t}^{D}\leq R_{j,t}^{D,\max}\quad\forall j,\forall t\] (26)
* Unit commitment constraints; Equation (27) determines the start-up and shut-down status of units, while (28) states that a unit cannot start-up and shut-down during the same period [29]. \[y_{i,t}-z_{i,t}=u_{i,t}-u_{i,t-1}\quad\forall i,\forall t\] (27) \[y_{i,t}+z_{i,t}-1\leq 0\quad\forall i,\forall t\] (28)
* Generating units startup cost constraint; constraints (29) and (30) represent generating units startup cost limitations [21]. \[SUC_{i,t}\geq\lambda_{i,t}^{SU}(u_{i,t}-u_{i,t-1})\quad\forall i,\forall t\] (29) \[SUC_{i,t}\geq 0\quad\forall i,\forall t\] (30)  
The second-stage constraints account for stochastic operating conditions are the same as the first-stage constraints and mentioned in Appendix A.