esa-sceva/satcom-chunk-collection huggingface dataset:
# The Mechanical Properties of Reinforced Concrete Columns with Longitudinal Pre-Embedded Holes
## 2 Finite Element Model Building
### Finite Element Model Verification  
To verify the reliability of the finite element analysis model, this study conducted numerical simulation validation using specimen L30-600 from reference [26]. The numerical simulation yielded an ultimate bearing capacity of 16,440 kN, which is 6.35% higher than the experimentally measured value of 15,458 kN reported in the literature. This discrepancy primarily arises from the assumption of perfect bonding between concrete and reinforcement in the simulation, neglecting the actual relative slip behavior between the two materials. Nevertheless, the 6.35% deviation falls within the acceptable margin of error, demonstrating that the established finite element model exhibits good reliability. Furthermore, these validation results provide indirect evidence that the constitutive relationship employed in this study can reasonably describe the mechanical behavior of reinforced concrete structures.  
To investigate the influence of longitudinal pre-embedded holes on the mechanical performance of reinforced concrete columns, a finite element modeling approach was adopted. Several groups of finite element analysis models were established, considering different pre-embedded hole diameters, concrete strength grades, stirrup ratios, and slenderness ratios (as shown in Figure 4). These models were used to analyze the failure process of reinforced concrete columns with longitudinal pre-embedded holes under compressive loading. The study aimed to determine the effects of the aforementioned factors on the compressive load-bearing capacity of the columns. The main test specimen parameters and results are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Pre-Embedded Hole** & **Concrete** & **Length** & **Slendeness** & **Stirrup** & **Ultimate** & **Bearing** \\  & **Diameter** & **Strength** & **(mm)** & **Ratio** & **(\%)** & **Capacity** & **Capacity** \\ \hline D0-C30-S1-L8.2 & 0 & C30 & 4200 & 8.2 & 1.04\% & 7400.07 & Axial compression \\ D50-C30-S1-L8.2 & 50 & C30 & 4200 & 8.2 & 1.04\% & 7291.99 & Axial compression \\ D50-C25-S1-L8.2 & 50 & C25 & 4200 & 8.2 & 1.04\% & 6460.96 & Axial compression \\ D50-C35-S1-L8.2 & 50 & C35 & 4200 & 8.2 & 1.04\% & 8032.88 & Axial compression \\ D50-C40-S1-L8.2 & 50 & C40 & 4200 & 8.2 & 1.04\% & 8858.84 & Axial compression \\ D50-C50-S1-L8.2 & 50 & C50 & 4200 & 8.2 & 1.04\% & 10,196.10 & Shear failure \\ D50-C30-S1-L6.2 & 50 & C30 & 3200 & 6.2 & 1.04\% & 7269.38 & Axial compression \\ \hline \hline \end{tabular}
\end{table}
Table 4: Main test specimen design parameters and results.
## 3 Results  
This section documents the variability of the LiDAR-based structural parameters across the study sites (Table 1), the performance of the retrieval of canopy structural parameters for all MISR-HR BRF and RPV model scenarios, for all vegetation types combined (Tables 2 and 3, Figures 5-7), and for the scenarios 1 and 4, for the three vegetation types separately (savanna, indigenous forest and commercial forest plantations) (Figure 8).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{3}{c}{**Lowveld-Savanna**} & \multicolumn{3}{c}{**Si**imangaliso St-Luca Indigenous Forest**} & \multicolumn{3}{c}{**Si**imangaliso St-Luca Forest Plantation**} & \multicolumn{3}{c}{**Combined Samples**} \\  & \multicolumn{3}{c}{\(n=6150\)} & \multicolumn{3}{c}{\(n=439\)} & \multicolumn{3}{c}{\(n=755\)} & \multicolumn{3}{c}{\(n=7344\)} \\ \hline Parameters & \(H_{\text{mean}}\) & CC & \(H_{\text{mean}}\) & CC & \(H_{\text{mean}}\) & CC & \(H_{\text{mean}}\) & CC \\ \hline  & (m) & (\%) & (m) & (\%) & (m) & (\%) & (m) & (\%) \\ \hline Mean & 3.6 & 24.3 & 6.19 & 48.9 & 11.7 & 50 & 4.6 & 29.6 \\ SD & 0.8 & 14.2 & 2.51 & 23.8 & 3.9 & 16.4 & 2.9 & 19.5 \\ Min & 1.2 & 0 & 1.02 & 0.9 & 1 & 1.6 & 1 & 0 \\ Max & 8.4 & 71.9 & 14.2 & 90.6 & 21.4 & 90.9 & 21.4 & 90.9 \\ CV & 0.2 & 0.6 & 0.4 & 0.5 & 0.3 & 0.3 & 0.6 & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics for the two LiDAR structural parameters, mean tree height (H\({}_{\text{mean}}\)) and canopy cover (CC) per forest vegetation type, and for all the forest vegetation types together at 275 m spatial resolution.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline
**Target Variable** & \multicolumn{6}{c}{**H\({}_{\text{mean}}\) (m)**} & \multicolumn{6}{c}{**CC (\%)**} \\ \hline
**RPV Parameters 1 ** & **RMSE** & **RMSE** & **RMSE** & **RMSE** & **RMSE** & **RMSE** & **RMSE** & **RMSE** \\  & **Bands** & **R2** & **RMSE** & **RMSE** & **RMSE** & **RMSE** & **RMSE** & **RMSE** & **RMSE** & **RMSE** & **RMSE** \\ \hline Scenario 5 \(\rho_{0}\) (All) & 4 & 0.65 & 1.76 & 38.22 & 0.00 & 0.00 & 4 & 0.49 & 13.90 & 46.69 & \(-\)0.05 & \(-\)0.15 \\ Scenario 5 \(k\) (All) & 4 & 0.62 & 1.83 & 39.59 & 0.01 & 0.24 & 4 & 0.48 & 13.94 & 46.84 & 0.00 & 0.01 \\ Scenario 50 (All) & 4 & 0.48 & 2.14 & 46.32 & \(-\)001 & \(-\)011 & 4 & 0.43 & 14.71 & 49.42 & 0.14 & 0.47 \\ Scenario 5 All (All) & 12 & 0.71 & 1.61 & 34.84 & 0.01 & 0.10 & 12 & 0.60 & 12.19 & 40.96 & 0.04 & 0.13 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Validation statistics of the predictive models for the mean tree height (H\({}_{\text{mean}}\)) and canopy cover (CC) metrics at 275 m spatial resolution, using different parameters of the MISR RPV (all forest vegetation types or combined samples from Lowveld and Siimangaliso Saint-Lucia (\(n=7344\)).
## 3 Results
### Calibration of the UV Illuminance Uniformity
After applying the correction mask, a map of the brightness distribution of the LCD matrix, as per Figure 13, was obtained. For ease of comparison, the same scale as in the map was used with the values measured before correction (Figure 8). The correction mask was applied using UV tools. Repeated measurements of light intensity after applying the correction showed a reduction of the original standard deviation from 0.26 to 0.17 mW/cm\({}^{2}\), representing a decrease of more than one third. Future research may focus on developing alternative methods for correcting non-uniform illumination that would overcome the limitation caused by the limited number of effective brightness levels available for LCD control.
## 3 Results and Discussion  
### Particle Size Distribution and Powder Composition  
The results of the particle size distribution are presented in Figure 1 as cumulative distribution curves. Additionally, the D90 values have been plotted here and marked accordingly in the curves.  
Compared to the other powders the reference powder (dark green curve) shows a sharp increase between D10 and D90, indicating a narrow particle size distribution. This is also confirmed by the corresponding D10, D50, and D90 values. It is worth noting that the reference powder appears slightly coarser than originally specified by the manufacturer.  
Measurements of the unsieved GC material in its as-received condition show a significantly flatter curve compared to that of the reference powder. The deviations are particularly substantial in the coarser particle size range. The WC-Co-Cr powder has a D90 value of 62.14 um, whereas the unsieved GC powder has a value of 180.31 um--over three times higher. In the finer particle size range, the deviation is considerably smaller.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Load [N] & 10 \\ Linear speed [cm/s] & 10 \\ Wear track diameter [mm] & 6 \\ Laps [-] & 10,000 \\ Stop condition [-] & laps \\ Lubricant [-] & none \\ Temperature [\({}^{\circ}\)C] & 22 \\ Ball material [-] & WC-Co \\ Ball diameter [mm] & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: POD test parameters.  
Clearly demonstrating that both the cumulative distribution curve and the D50 and D90 values shift significantly towards those of the reference material, sieving the GC materials is an effective method. The D90 value of the sieved GC material is 81.67 \(\upmu\)m, which is above the sieve mesh size. This suggests that the particles are not spherical and that agglomeration may have occurred, which could potentially distort the results (as the measurement represents an average of two diagonal lengths per particle).  
This is further confirmed by the representative SEM image of the sieved GC material shown in Figure 2a.  
Figure 1: Representative cumulative curves of the particle size distribution measurement for the reference and GC powders (sieved and unsieved).  
Figure 2: (**a**) SEM micrograph of the sieved GC powder; (**b**) EDX analysis of the GC powder (Au-sputtered).  
It is evident that the examined powder contains a higher proportion of fine particles, which were not detectable in the previous measurement using the flowmeter. These fine particles tend to adhere either to each other or to larger particles.  
The EDX analysis from Figure 2b reveals, in addition to the elements belonging to the cermet particles (C, W, Co, and Cr), further elements such as O, Ni, Al, Mo, and Fe. These oxides are marked as an example in Figure 2a.  
For better comparability of the powders used, an XRD analysis was also carried out (see Figure 3).  
The results show that there are significantly more phases in the GC powder. It also becomes clear that secondary carbides W2C are already present in the GC powder before coating.
## 5 Experiments and Analysis
### Small-Scale Berths  
Small-scale berths are characterized by large vessels, long unloading times, and fixed path ends, all of which are transported to designated oil storage tanks. Due to the peculiarities of small berths, calculations were made for operation plans involving 10 vessels and 42 vessels.  
Calculations of 20, 100 and 500 iterations are carried out using the NSGA-II-Dijkstra algorithm. The first level Pareto results of these different iterations are as illustrated in Figure 7:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Design Value** \\ \hline _CI_ & 30 USD/m \\ _Cs_ & 50 USD/unit/h \\ _CI_ & 1000 USD/unit \\ Cost of electric ball valves & 5 USD/once \\ Cost of electric gate valve & 3 USD/once \\ Cost of exhaust valves & 2 USD/once \\ Cost of manual ball valves & 10 USD/once \\ Cost of manual gate valves & 8 USD/once \\ Cost of multi-way valves & 12 USD/once \\ Cost of ordinary valves & 2 USD/once \\ Cost of safety valves & 2 USD/once \\ Rated power of pump, level 1 & 4000 m\({}^{3}\)/h \\ Rated power of pump, level 2 & 2520 m\({}^{3}\)/h \\ Rated power of pump, level 3 & 1560 m\({}^{3}\)/h \\ \(V\) & 4.5 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 5: The basic data.  
Figure 7: Results of small-scale berths.  
When 10 vessels are planned in a cycle, increasing the number of iterations does not lead to a significant change in the results, indicating that the NSGA-II-Dijkstra algorithm can effectively solve the PAVP for this scenario. However, when the number of vessels increases to 42, the results vary significantly with the number of iterations. Specifically, the results of 500 iterations are markedly better than those of 20 and 100 iterations. This demonstrates that increasing the number of iterations has a substantial impact on the outcomes.  
Subsequently, simulations were conducted for 10 vessels and 42 vessels in the small-scale berths, with 500 iterations performed using both the NSGA-II-Dijkstra and INIIDA algorithms. The comparative results are demonstrated in Figure 8:  
It can be concluded that both algorithms can effectively solve the PAVP problem of small berths. The results of the INIIDA on small berths are slightly better than those of the NSGA-II-Dijkstra algorithm. However, INIIDA shows a significant advantage in computation time, taking approximately one quarter of the time required by the NSGA-II-Dijkstra algorithm. Additionally, the results for 42 vessels are superior to those for 10 vessels, indicating that the INIIDA algorithm performs better with larger data volumes. According to the actual conditions of terminal operations, a Gantt chart is used to represent one of optimal solutions for small-scale berths with 10 vessels, as shown in the figure below. In Figure 9, the number of oil transfer arms, the length of pipeline and the number of oil storage tanks are demonstrated.  
Figure 8: Results of small-scale berths with 10 vessels.
# Design of Cislunar Navigation Constellation via Orbits with a Resonant Period
## 4 Simulation Analysis
### DOP Value Distribution Analysis of Resonant Constellations
#### 4.2.3 Time-Varying Pattern Analysis  
By setting the initial phase of the baseline orbit to 90\({}^{\circ}\), the DOP values are recalculated. The temporal variation curves of the spatial average DOP values in the near-Earth and lunar regions are plotted in Figure 14.  
Figure 14: Variation in near-Earth region’s mean DOP value with time.  
Figure 13: Variation in near-Earth region’s mean DOP value with a spherical radius.  
The figures show that the trends in the mean DOP values over time are consistent in both regions. Within the constellation's resonant period, eight peaks occur, with intervals matching the baseline orbital period. The number of peaks correlates with the 1:1:4:4 resonant ratio. However, under the current initial phase, the lunar region exhibits larger peaks and more pronounced DOP fluctuations compared to the near-Earth region. Optimization methods can be applied to determine the optimal initial phase to enhance performance.
## 3 System Model and Problem Formulation
### GDRL-SFCR Intelligent Routing Framework  
The model architecture of GDRL-SFCR, illustrated in Figure 2, consists of three components: embedding of the LEO satellite network, obtaining a valid state space, and making the routing decision. In the proposed GDRL-SFCR method, a GNN and DRL are combined to optimize routing decisions. The GNN processes the graph-structured data representing the network topology, capturing essential structural features such as node connections and link properties. Then, a MLP is used to obtain the current valid state based on the features of the current node and the neighboring nodes. These features are then fed into the DRL framework, which learns the optimal policy for routing decisions. We will explain the components in detail.
## 3 Results and Discussion
### Dynamic Optimization  
After the investigation of the configuration performance taking into account the different thermodynamic and financial parameters, it was crucial to carry out an optimization procedure, considering all the previously defined cases. As a result, the best combination of the net electricity load and TES tank volume was determined. A multi-criteria objective function was assumed, according to Equation (38). For this analysis, the hydrogen price was considered at 25 EUR/kg. The objective function (\(F\)) took various values and, more specifically, the lower ones, which calculated a net electricity load that was equal to 75 or 175 kW, or for lower values of the TES tank volume, in general, as illustrated in Figure 16. The lowest value was found at 0.92 and was determined when the net electricity load was equal to 75 kW and the storage tank volume at 10 m\({}^{3}\). For this optimum operation scenario, the annual electricity and hydrogen production was calculated at 210,163 kWh and 2356.5 kg, respectively, while the annual energy efficiency was found at 14.52%, and the corresponding exergy efficiency at 15.48%. Finally, the payback period, the simple payback period, the internal rate of return, and the net present value were defined at 6.73 years, 5.8 years, 16.89%, and EUR 1,073,384, respectively. All the optimum case outputs are presented in Table 7.  
Figure 15: Net present value for different values of net electricity load and TES tank volume when the hydrogen price is equal to 25 EUR/kg.
# Nearly Linear-Phase 2-D Recursive Digital Filters Design Using Balanced Realization Model Reduction
## 2 Gramians of 2-D Discrete Systems  
For a given 2-D system, multiple types of Gramians can be specified correctly. Similarly, for a 2-D discrete system, there are various types of balanced realizations that result in various balanced approximations [25; 30]. In this section, we are interested in the structured controllability and observability Gramians for 2-D discrete systems. The benefits of using this type of Gramians over the others are that the resulting positive-definite block-diagonal Gramians satisfy the 2-D Lyapunov equation and are, therefore, sufficient for 2-D stability [25]. Further, they can be used to obtain a block-diagonal similarity transformation which will lead, as it will be discussed in Section 4, to a guaranteed stable 2-D reduced-order system. We use an LMI-based algorithm to compute these Gramians where the problem is formulated as a minimization problem that can be solved numerically by minimizing the trace of the Gramians \(P^{s}\) and \(Q^{s}\) under LMI constraints. Using this algorithm, the Gramians \(P^{s}\in\mathbb{R}^{n\times n}\), \(Q^{s}\in\mathbb{R}^{n\times n}\) are constrained to be positive-definite \(P^{s}>0\), \(Q^{s}>0\), symmetric \(P^{s}=P^{sT}\), \(Q^{s}=Q^{sT}\), and block-diagonal \(P^{s}=diag(p_{1},p_{2})\), \(Q^{s}=diag(q_{1},q_{2})\).  
Consider a 2-D discrete state-space system realization \(\Sigma=(A,b,c,d)\) described by [PERSON]'s model [41]:  
\[\begin{bmatrix}x^{h}(i+1,j)\\ x^{v}(i,j+1)\end{bmatrix} =\begin{bmatrix}A_{11}&A_{12}\\ A_{21}&A_{22}\end{bmatrix}\begin{bmatrix}x^{h}(i,j)\\ x^{v}(i,j)\end{bmatrix}+\begin{bmatrix}b_{1}\\ b_{2}\end{bmatrix}u(i,j) \tag{1}\] \[\equiv\mathbf{Ax}(i,j)+\mathbf{bu}(i,j)\] (2) \[y(i,j) =[c_{1}\ \ c_{2}]x(i,j)+du(i,j)\] (3) \[\equiv\mathbf{cx}(i,j)+\mathbf{du}(i,j) \tag{4}\]  
where \(x^{h}(i,j)\in\mathbb{R}^{m\times 1}\) is the horizontal state vector, \(x^{v}(i,j)\in\mathbb{R}^{n\times 1}\) is the vertical state vector, \(u(i,j)\in\mathbb{R}\) is the input, \(y(i,j)\in\mathbb{R}\) is the output, and \(A_{11},A_{12},A_{21},A_{22},b_{1},b_{2},c_{1},c_{2}\), and \(d\) are real matrices with appropriate dimensions.  
The structured controllability and observability Gramians [30] are defined by the positive-definite solutions \(P^{s}\) and \(Q^{s}\) of the following Lyapunov inequalities:  
\[AP^{s}A^{T}-P^{s}+bb^{T} <0 \tag{5}\] \[A^{T}Q^{s}A-Q^{s}+c^{T}c <0 \tag{6}\]  
The matrices \(P^{s}\) and \(Q^{s}\) are block-diagonal matrices, \(P^{s}=diag(p_{1},p_{2})\) and \(Q^{s}=diag(q_{1},q_{2})\), representing the solutions to the above Lyapunov inequalities. In the following section, a method to obtain the structural controllability and observability Gramians using LMI will be presented.
# Microalgal Proteins and Bioactives for Food, Feed, and Other Applications
## 1 Introduction
### Microalgae for Pharmaceutical Applications
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Species**} & **Animal, Duration of** & **Content of** & **Findings** & **References** \\  & **Experiment** & **Microalga in Diet** & **Findings** & **References** \\ \hline \multirow{4}{*}{_Nannochloropsis ocanica_} & & & Increase of abundance of proteins & \\  & & & related to amino acid catabolism & \\  & & 5 weeks & 4.5\% & and synthesis & \\  & & & Results suggested that more tender & \\  & & & meat may result from algae feeding & \\ \hline \multirow{2}{*}{_Porphyridium_ sp.} & Chickens & & Decreased feed intake (10\%) & \\  & 10 days & 5-10\% & Decreased serum cholesterol & \\  & & & level (28\%) & \\ \hline \multirow{2}{*}{_Schizochytrium_ sp.} & Dairy cows & & & \\  & 6 weeks & 4\% & Decreased feed intake & [102] \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._recombinant therapeutic proteins over antibodies or proteins include simple synthesis, high specificity and selectivity and low accumulation in tissues. On the other hand, they have a short half-life due to their low stability and are expensive to produce [108; 109; 110; 111]. In genetic engineering, therapeutic proteins are made in various host organisms such as bacteria, yeast, plant and mammalian cells and, more recently, in microalgae. However, all of these host organisms have some drawbacks. In the case of bacteria they do not make the same post-translation modifications of proteins as higher eukaryotes, so they are not appropriate for the production of eukaryotic proteins. Plant cells have different glycosylation patterns, and mammalian tissues are costly and instable. Microalgae cells may be effective hosts for the expression of recombinant therapeutic proteins [111; 112; 113].
# Towards Collaborative and Dynamic Spectrum Sharing via Interpretation of Spectrum Access Policies
## 5 Policies
### Policy Manager  
The PM manages policies and associated policy interpretation rules. In particular, it maintains a list of active policies, i.e., the policies that are applicable to the current circumstances as imposed by the Authoritative Policy Source. The function of the PM is to support the authoring of new policies, editing of the existing policies, storage and retrieval of policies in policy database and activating/deactivating policies. For this purpose, the Policy Manager supports functions that execute tasks such as Select Active Policies, Add Active Policy, Remove Active Policy, Select Allowed Policies, Select Disallowed Policies and so on.  
Looking deeper into the structure of the system, the PM manages the policies that are stored locally in the Inference Store (see Section 6.3), based on actions initiated from the Controller. Each time a modification is made (rules are modified, policy is activated, etc.), the Inference Store automatically invokes BaseVISor [17]--an OWL 2 RL Reasoner--to infer any implicit facts about the policies. All facts, explicit and derived, are subject to querying when, for instance, the PE is determining the transmission opportunities, which requires access to the currently active policy definition.
## 3 Results and Discussion
### Zones, Flash, and Material Stirring
This can be explained by  
Figure 8: Transient (red) and steady-state (yellow) regime zones in (a) P2 specimens (15% infill with filler); (b) P4 specimens (100% infill without filler); and (c) P0 specimens (15% infill with filler and a non-tilted tool) showing very poor material mixing. considering that a lower speed implies a longer tool-material friction time, which generates the preheating of the tool and the welding zones. Then, the transient zone could be shortened by reducing the feed rate during the initial welding phase, or in other words, when the travel speed is delayed until the tool reaches a stable temperature. Otherwise, to achieve a homogeneous result along the entire seam, a preheating phase should be conducted. This can be performed by significantly reducing the feed rate at the start of the process until the tool reaches a stable working temperature. In group P3 (100% infill with filler), the analysis of the three rotational speeds allowed us to conclude that the highest value gave the best results. Figure 9 shows the results of the increasing rotational speeds: 2000 rpm (left), 2300 rpm (center), and 2500 rpm (right). As the rotational speed increases, the weld quality tends to improve. This is likely due to the more intense stirring of the material, driven by greater frictional forces, which, in turn, generate more heat during the process. In the same group, the analysis of the welding direction highlighted the fact that the welding direction must be set forward. The test performed with a negative inclination in the movement direction results in poor-quality welds with a significant cross-sectional reduction, as shown in Figure 10a,b, respectively. According to other researchers, the forward direction increases friction and heat generation, and consequently enhances material stirring [43].