esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Grasslands vs. Arable Land: Carbon Sequestration Potential
Additionally, while the CAP supports agroforestry, Romania's national policies and rural development programs have not fully integrated it, complicating farmers' access to subsidies and the navigation of regulations for tree planting on agricultural land [45]. Grasslands, particularly those with high biodiversity, offer significant SOC sequestration potential. However, overgrazing poses a serious risk to these systems, as it can lead to soil degradation and reduced SOC levels [5; 43]. Conservation programs like Natura 2000, which contribute indirectly to carbon sequestration by protecting natural habitats and promoting sustainable land use, could be better utilized within broader climate mitigation strategies [46]. For instance, integrating Natura 2000's objectives with national climate policies could enhance efforts to meet carbon sequestration targets. This alignment is particularly crucial for countries striving to fulfil their Nationally Determined Contributions (NDCs) under the Paris Agreement [47]. Furthermore, programs like the 4 per 1000 Initiative, which seeks to increase soil carbon storage by 0.4% annually [2], provide a valuable model but require stronger commitments and clearer monitoring mechanisms to ensure long-term success. Our results suggest that maintaining traditional farming methods in grasslands, such as hay meadows and low-intensity grazing, would contribute substantially to meeting these targets. Nevertheless, it is important to note that recent overgrazing in certain grassland areas may have introduced biases in our findings, potentially leading to an underestimation of the SOC sequestration potential of less disturbed grasslands. The impact of overgrazing on soil organic carbon content, which reduces biodiversity and increases soil compaction, should be further investigated to fully understand its implications for carbon storage. Finally, this study highlights the importance of fostering a long-term, sustainable relationship between humans and the land. Agricultural policies must go beyond financial incentives to actively promote engagement in responsible land management practices, particularly low-intensity, traditional farming methods that have a proven impact on SOC sequestration. More precisely, existing policies such as the CAP are often financially appealing but lack long-term viability [48]. According to the cited paper, these policies tend to rely on monetary incentives, leading participants to engage primarily for financial gain rather than from a genuine commitment to preserving traditional practices. For soil management policies to be effective and lasting, they must be founded on a strong relationship between humans and nature that fosters sustainable farming practices. Therefore, policymakers should prioritize and promote active engagement in low-intensity, traditional farming methods, as these are more closely aligned with sustainable agricultural practices.
# SS3 DNet-AF: A Single-Stage, Single-View 3D Reconstruction Network with Attention-Based Fusion
## 3 Proposed Methods
### Image Encoder
#### 3.1.2 Residual Blocks  
Four sequential residual blocks are utilized to learn and preserve significant high-level features:  
\[\begin{split}& X_{res1}=ResBlock(X_{pool},W_{res1}),\\ & X_{res2}=ResBlock(X_{res1},W_{res2}),\\ & X_{res3}=ResBlock(X_{res2},W_{res3}),\\ & X_{res4}=ResBlock(X_{res3},W_{res4}),\end{split} \tag{3}\]  
where, ResBlock denotes the operations within a residual block, and \(W_{res1}\), \(W_{res2}\), \(W_{res3}\), and \(W_{res4}\) each represent block weights.  
Figure 3: This Figure presents the proposed three-dimensional reconstruction network, SS3 DNet-AF, designed to reconstruct a 3D shape from a 2D image. SS3 DNet-AF consists of four modules. Module-01 employs ResNet18 to encode the 2D image, while Module-02 uses an MLP to encode the initial 3D shape (a spherical shape). In Module-03, the image and shape features are fused using a dual attention mechanism. Finally, Module-04 decodes the fused features with another MLP to produce the refined 3D shape.
## 3 Design and Analysis of 7-QAM Constellation  
In this section, we give the design of 7-QAM constellation. In addition, the theoretical analysis of its performance is carried out by calculating its PAPR, deriving its exact expression for the SEP over the AWGN channel and analyzing the non-linear distortion introduced to transmitted signals.
## 2 Materials and Methods
### Problem Articulation and Literature Review  
We have conducted a literature review on concept and measurement techniques of resilience while defining the problem. This model development uses NATO's resilience concept and definition (i.e., resilience through civil preparedness) mostly complying with the extant researches.  
Figure 1: Methodology of the study.  
Defining the real problem is the starting point of any modeling process [20]. Therefore, the problem--has been identified as \"how can resilience through civil preparedness (RtCP) be applied to deliver understanding of civil environment and its impact on NATO military operations\". RtCP is the individual and collective capacity to resist and recover from military, civilian, economic or commercial shocks, absorb damage, and resume function as quickly and efficiently as possible [21; 22].  
The main rationale behind discussing RtCP as a (complex) problem is that RtCP is almost infinitely complex and multidimensional with countless nodes and layers of interdependency, and is influenced transnationally by a range of pressures applied by diplomatic, information, military, and economic instruments [21; 22; 23]. Moreover, resilience should not be viewed and analyzed discretely; a shock in one area may be mitigated by capacity in another or it may have cascading impacts that indiscriminately propagate across national borders and increase in scope and complexity [23].  
Having articulated the problem, we have identified key variables. These variables include measurable metrics and calculated indicators. Metrics comprise seven baseline requirements (_Continuity of Government_, _Energy Supplies_, _Movement of People_, _Food and Water_, _Mass casualties_, _Communication_, _and Transportation_) [21; 22] and shocks (_Electricity Blackout_, _Cyber Attack_, _Martial Law enforcement_, _Big Human Movement_, _State of War_, and _Armed Conflict_) [24]. Indicators involve resilience main domains (_Civil support to the military_, _Continuity of Government_, _and Continuity of Essential Services_) [21] and risk assessment functions (_Command and Control_, _Protection_, _Movement/Maneuver_, and _Sustain_).  
NATO's strategic resilience concept (Figure 2) is an adaptive process in which resilience performance is measured by absorbing strategic shocks with minimal impact (\(C_{gap}\)) while maintaining essential functions at an acceptable level, then recovering functionality at a reasonable time (\(t_{1}\)) and at a reasonable cost. Therefore, a well-integrated system focuses specifically on managing the local consequences of an attack and isolating the event from the overall system function, thus making the adversary's value proposition unfavorable. Then, the system adapts and increases its capacity (\(C_{f}\)) to withstand future shocks with a reduced surface area of vulnerability exposed to the adversary and increased speed of system recovery [23].
## 3 Results and Discussion
### Analysis of Effluents from Anaerobically Digested Food Waste and Dairy Manure
\[f_{NH_{3}}=\frac{[NH_{3}]_{solution}}{[NH_{3}]_{solution}+[NH_{4}^{+}]_{solution }}=\frac{1}{1+\frac{10^{-pH}}{K_{a}^{2}}} \tag{12}\]  
where \(K_{a}^{\prime}\) is the dissociation constant corrected for ion effects in the high bicarbonate salt-supplemented BG-11 medium, \(pH=-\)log[H\({}^{+}\)] = 10, and \(f_{NH3}\) is the \(NH_{3}\)\(-\)\(N\) fraction of the total ammoniacal N of the BG-11 media. The corrected disassociation constant is obtained from Equation (13). \[K_{a}^{\prime}=K_{a}\frac{\gamma_{NH_{4}^{+}}}{\gamma_{NH_{3}solution}\gamma_{ H^{+}}} \tag{13}\]  
where \(\gamma\) represents activity coefficients, and \(K_{a}\) is uncorrected disassociation constant. \(K_{a}\) is obtained following Equation (14) [48]. Figure 7: Total nitrogen remaining in a flask containing BG-11\({}_{0}\) medium supplemented with 16.8 g L\({}^{-1}\) NaHCO\({}_{3}\) and 2.5% (\(\gamma/v\)) anaerobically digested food waste effluent, initially adjusted to pH 9. \[K_{a}=100^{0.05-\frac{2288}{7}} \tag{14}\]  
and the activity coefficient for ammonium is obtained following Equation (15) [49]. \[\log\gamma_{NH_{4}^{+}}=-0.5a^{2}\left(\frac{\sqrt{I}}{1+\sqrt{I}}-0.3I\right) \tag{15}\]  
where \(a\) is the charge 1\({}^{+}\) of the ion \(NH_{4}{}^{+}\) and \(I\) is the ionic strength. For algae and duckweed-based stabilization ponds treating domestic wastewater [50], the mass transfer coefficient \(K_{m}\) is obtained following Equation (16). \[K_{m}=\left(\frac{0.0566}{d}\right)e^{0.13(T-20)} \tag{16}\]  
where \(d\) is the depth of the outdoor microalgal open-pond PBR (m). Therefore, the open-pond depth should be shallow enough to allow light penetration but high enough to prevent ammonia volatilization when using effluents. Ammonium (\(NH_{4}{}^{+}\)) removal efficiencies of 64.5 \(\pm\) 2.8% and 51.2 \(\pm\) 1.9% were achieved in shallow and deep ponds, respectively [50]. Alternatively, the following correlation (Equation (17)) from dimensional analysis can be used to determined mass transfer coefficient:  
Figure 6:0  
where \(Sh=\) Dimensionless Sherwood number, Re = Dimensionless Reynold's number, \(Sc=\) Dimensionless Schmidt number, and \(b\), \(n\), and \(m\) are dimensionless coefficients [51]. To draw more definitive conclusions and enable design of appropriate ammonia-containing effluent fed-batch algal cultivation strategies, future studies should be conducted to reconcile predicted and measured ammonia volatilization rates from open-pond systems.
# A Receiving Antenna Allocation Scheme for Downlink MU-MIMO-OFDM Transmission
## 4 Performance Evaluation
### Validity of DL-MU-MIMO-OFDM Transmission Using Receiving Antenna Allocation  
We show the potential channel capacity of the downlink MU-MIMO-OFDM transmission with the receiving antenna allocation per subcarrier. Figure 5 shows the cumulative distribution function (CDF) of the total channel capacities per subcarrier by using the coordinated Tx-Rx BD technique [13] and MU-MIMO-OFDM transmission with antenna allocation [14; 15]. In the coordinated Tx-Rx BD technique, pre-coding and receiver weights were updated 10 times. Antenna allocation was performed randomly or in optimal manner. Optimum means to use the antenna that offers the maximum SINR among all antenna combinations. The result of the single user MIMO (SU-MIMO) with time division  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Center frequency & 5.18 GHz \\ Bandwidth (Subcarrier number) & 20 MHz (52) \\ Transmit power & 13 dBm \\ Number of AP antennas & 4 \\ Number of STA antennas & 2 \\ Transmit/Receiving antennas (Gain) & Dipole (2 dBi) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental conditions.  
Figure 4: Experimental environments. (**a**) Room layout; and (**b**) photograph of room.  
multiplexing access (TDMA) is also plotted as reference data. These results show the coordinated Tx-Rx BD technique achieves a higher channel capacity than the other methods. This is because the DoF of all receiver antennas can be used. However, as shown in the Introduction, its demerit is a higher computational complexity for the receiver weight multiplication and a greater overhead for notification of the quantized receiver weight. The channel capacity of the downlink MU-MIMO-OFDM transmission with the receiving antenna allocation per subcarrier is slightly degraded than that of the coordinated Tx-Rx BD technique even though each STA uses only one antenna. This is because the spatial diversity gain is more effectively attained by allocating antennas on a per subcarrier basis. Therefore, the proposed method is effective when there are massive numbers of wireless devices to service.
# Accurate Collaborative Globally-Referenced Digital Mapping with Standard GNSS
## 5 Empirical Results  
To validate the results obtained in the above analyses, GNSS and visual data were collected in a moderate urban area north of the University of Texas at Austin campus in Austin, TX. This section presents the data collection setup, error statistics of various flavors of code-phase GNSS positioning, and results from GEOSLAM's multi-session GNSS-aided-visual mapping.
## 3 Modeling of Path Planning
### Mathematical Definition of the Problem
Compared with the Bezier curve, the number of control points of the B-spline curve is independent of the order of the curve. The local characteristics of the curve can be adjusted by adjusting the position of the local control point. This explains why it is more and more applied in the robot path representation. The position of the control points can be defined as \(C=\left\{c_{i}=(\delta_{x}^{i},\delta_{y}^{i})|c_{i}\in\mathbb{R}^{2},i=1,2 \ldots,n\right\}\). The curve order is \(K\) and \(B_{i,K}(t)\) is the basic function of the B-spline curve, then an AUV path can be expressed in the form of the equation:  
\[\left\{ \begin{array}{l}X(t)=\sum\limits_{i=1}^{n}\delta_{x}^{i}\times B_{i,K }(t)\\ Y(t)=\sum\limits_{i=1}^{n}\delta_{y}^{i}\times B_{i,K}(t)\end{array} \right. \tag{3}\]  
Therefore, the local path planning problem of AUV can be described as finding the control points of the B-spline curve, so that the obtained curve path satisfies the non-collision, the whole path satisfies the minimum turning radius constraint, and the path direction follows the direction of the ocean current as much as possible. This problem can be modeled as an optimization problem under multiple constraints, using intelligent algorithms.
## 3 Results and Discussion
### Molecular Docking Analyses
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ligand** & \multicolumn{5}{c}{**Tetrahydroberberine N-oxide (Trans-cannadine-N-oxide)**} \\ \hline Target Protein & GCLC & HO-1 & KEAP1 & NRF2 & NQO1 \\ \hline Vina Score & \(-\)7.8 & \(-\)7.7 & \(-\)8.1 & \(-\)8.7 & \(-\)8.8 \\ \hline  & & & Chain X: LEU365 & Chain B: LEU365 & \\  & Chain A: HIS25 & ALA36 CIC367 & ALA366 & CIC367 & \\  & Chain A: CYS213 & PHE33 MET34 & CY3568 VAL369 & CY368 LE416 & Chain I: ILE50 PHE65 \\  & MET214 ASN215 & PHE37 PHE47 VAL50 & ILE416 GLY417 & VAL148 GLY419 & GLN66 TYR67 \\  & TRP314 ASN315 & MET31 LEU54 & VAL418 VAL463 & VAL420 GDY462 & PRO68 TYR126 \\  & SER316 CIC319 & TYR314 THR135 & GLY44 VAL456 & VAL463 GLY464 & PHE178 \\  & ASP222 ARG324 & ARG136 LEU138 & ALA466 AL510 & VAL465 GLY509 & Chain I: H1 SH SER12 \\  & PRO358 Lys359 & GLY139 ASP140 & VAL512 CYS513 & ALA510 GLY511 & SER16 PRO102 \\  & SER360 SER363 & SER142 GLY143 & VAL514 ELU557 & VAL512 CYS513 & LEU103 GLN104 \\  & SER364 TRP514 & GLY41 LEU147 & GLYS558 ILE559 & ALA556 LEU557 & TRP105 PHE106 \\  & MET517 Lys518 & PHE166 PHE167 & THRS50 VAL561 & GLYS58 ILE559 & THR148 GLY149 \\  & HIS521 & PHE207 AN210 & VAL604 GLY605 & THRS560 GLY603 & TYR155 \\  & PHE214 & VAL606 ALA4607 & VAL604 GLY605 & \\  & & VAL608 & VAL606 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._action of alkaloids on NRF2 is tetrahydroroberberine N-oxide (Trans-cannadine-N-oxide) > tetrahydroropalmatine > allocryptopine. This activated NRF2, in turn, amplifies the expression of pivotal antioxidant enzymes, including GCLC, HO-1, and NQO1. The order of action of alkaloids on GCLC is tetrahydroroberberine N-oxide (Trans-cannadine-N-oxide) > allocryptopine > tetrahydroropalmatine. The order of action of alkaloids on HO-1 and NQO1 is allocryptopine > tetrahydroropalmatine > tetrahydroroberberine N-oxide (Trans-cannadine-N-oxide).
## 2 Results
### MIC and MBC of Citral and \(\alpha\)-Terpineol  
Macrodilution-based antibacterial testing of the citral and \(\alpha\)-terpineol revealed that \(\alpha\)-terpineol showed weaker antibacterial effects in the applied test range (0.1-12.8 mg/mL) (Table 4). In contrast, citral strongly inhibited the proliferation of all three Gram-positive bacterium species at low MIC and MBC values.  
Confirmation the Antibacterial Features of Citral and \(\alpha\)-Terpineol with Thin-Layer Chromatography Combined with Direct Bioautography (TLC-DB)  
Qualities of the chromatographic separations on the thin-layer silica plates were confirmed before developments by illuminating the TLC plates under UV light (Figures 5A and 6A). Here, some major components or mass of components became visible. Vanilin sulfuric acid developments of one plate from each parallel run revealed the presence of further compounds in the lemongrass EO sample (Figures 5B(1) and 6B(1)). The TLC-based direct bioautography method showed another antibacterially active compound or compound group that was effective against all three tested bacteria (Figures 5C,D and 6C), but was definitely separated in the case of _K. sedentarius_ (Figure 5D(1)). In the cases of _B. thuringiensis_ and _D. congolensis_ (Figures 5C(1) and 6C(1)), the presenting components of lemongrass EO showed a smeared outlook in contrast to _K. sedentarius_.  
Lemongrass EO was diluted for application to _B. thuringiensis_ and _K. sedentarius_, but not with _D. congolensis_. Citral (Rf = 0.47) and \(\alpha\)-terpineol (Rf = 0.36) were active against both bacteria (Figures 5C,D and 6C).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Citral**} & \multicolumn{2}{c}{**\(\alpha\)-Terpineol**ol} \\ \hline  & **MIC** & **MBC** & **MIC** & **MBC** \\ \hline B. _thuringiensis_ & \(0.1\pm 0\) & 0.1 \(\pm\) 0 & 0.8 \(\pm\) 0 & 0.8 \(\pm\) 0 \\ \hline _D. congolensis_ & \(0.1\pm 0\) & 0.1 \(\pm\) 0 & 0.4 \(\pm\) 0 & 0.8 \(\pm\) 0 \\ \hline _K. sedentarius_ & \(0.1\pm 0\) & 0.1 \(\pm\) 0 & 0.4 \(\pm\) 0 & 0.8 \(\pm\) 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Minimal inhibitory and minimal bactericidal concentrations of citral and \(\alpha\)-terpineol tested with the macrodilution method. Concentrations are presented in mg/mL.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Citral**} & \multicolumn{2}{c}{**\(\alpha\)-Terpineol**} \\  & **(Diameter in mm)** & **(Diameter in mm)** \\ \hline B. _thuringiensis_ & \(29\pm 2\) & \(17\pm 3\) \\ \hline D. _congolensis_ & \(23\pm 2\) & \(7\pm 1\) \\ \hline K. _sedentarius_ & \(14\pm 2\) & \(6\pm 1\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Antibacterial effects of citral and \(\alpha\)-terpineol (5 \(\mu\)L) on the three tested bacterium species. Values represent the diameters of the inhibition zones in mm.
## 3 Results
### Scenario I: Economic Optimization
The 4.2% ROI similarly reflects a net gain relative to the total investment, and although modest, it confirms the project's economic feasibility. As shown in Figure 9, the grid constitutes the largest share of Scenario I's Proposed System costs--around 88%--primarily due to operational expenses. PV accounts for 11% of the total, reflecting a substantial yet smaller capital investment, and the converter makes up only 1%. Although PV has a relatively low-cost share, it significantly reduces reliance on the grid by offsetting considerable electricity purchases. PV generation over the system's lifetime helps minimize operational costs. It also mitigates fuel price volatility. Thus, even an 11% cost contribution from PV can yield meaningful long-term savings. These savings become significant compared to complete dependence on grid power. Moreover, the ability of HOMER to optimize PV capacity based on the site's GHI data and a detailed cost model ensures that PV investments are appropriately sized, contributing to the observed cost distribution and reinforcing PV's role in reducing operational expenses. Figure 10 shows that the grid provides most of the energy year-round for the Proposed System, accounting for 84.1% of the total energy supply directly consumed by the loads. In contrast, PV production contributes 15.9% of total generation, and only 1.6%--as expected from surplus generation--is sold back to the grid, with the rest used entirely to meet the load. This large grid share stems from varying solar availability, load fluctuations, and the system's design to ensure reliable power during periods of low irradiance or peak demand. Figure 8: Cash flow for the Proposed System and the Base Case in Scenario I. Meanwhile, limited surplus and possibly minimal incentives (e.g., net metering or feed-in tariffs) contribute to a small fraction of PV exports. This distribution highlights the grid's critical role in maintaining stability and underscores the importance of balancing renewable integration with dependable conventional energy sources.
## 3 Problem Description
### Constraints  
The problem under consideration includes the following constraints:  
(a) Any virtual machine \(V_{i}\) can only be assigned to one physical node \(P_{j}\). Let \(X_{i,j}\in\{0,1\}\) be a binary variable, \(X_{i,j}=1\) if \(V_{i}\) is assigned to \(P_{j}\) and 0 otherwise:  
\[\sum_{j=1}^{M}X_{i,j}\leq 1;\forall i\in\{1,2,\ldots,N\} \tag{5}\]  
(b) The virtual machine requested by the user must meet its allocation requirements:  
Figure 2: Example of resource allocation for 3 virtual machines deployed on a physical machine.  
\[\sum_{j=1}^{M}X_{i,j}=1;\forall i\in\{1,2,\ldots,N\}; \tag{6}\]  
(c) Physical server resource limit: the virtual machine allocated on each physical node does not occupy more resources than its capacity:  
\[\sum_{i=1}^{M}X_{i,j}R_{i,cpu}\leq H_{j,cpu};\forall j\in\{1,2,\ldots,M\}; \tag{7}\]  
\[\sum_{i=1}^{M}X_{i,j}R_{i,mem}\leq H_{j,mem};\forall j\in\{1,2,\ldots,M\}; \tag{8}\]  
where \(R_{i,cpu}\) (\(R_{i,mem}\)) represents the CPU requirements (memory requirements) of a virtual machine \(V_{i}\), and \(H_{j,cpu}\) (\(H_{j,mem}\)) represents the remaining available CPU resources (memory resources) on a physical node \(P_{j}\).