esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Climate Externalities and Their Role in Agroecosystem Intrusions
#### 3.1.2 Regulating Services  
The interconnected relationship between agroecosystems and the environment is best illustrated through regulating services, which are essential in maintaining ecological stability and resource efficiency. Environmental incidents such as drought, flooding, and extreme temperatures disrupt these services by affecting carbon sequestration, water retention, and soil stability.  
* Water Use Efficiency and Carbon Sequestration  
Extreme temperatures increase water demands [52], while drought and flooding directly impact water use efficiency (WUE) and crop carbon sequestration [53]. They observed that spring drought and summer floods negatively impact the WUE in wheat and maize, respectively. During the wheat season, carbon sequestration capacity is higher than in the maize season; however, spring drought reduces WUE in wheat, and summer flooding disrupts maize's efficiency. Similarly, high rainfall intensity in Mediterranean regions erodes soil, diminishing its moisture-retention ability and threatening WUE in vineyards [54].  
* Photosynthesis and Soil Stability  
Extreme temperatures accelerate growth rates in winter wheat [55], impair canopy development [56], and reduce photosynthesis [57]. They also affect the photosynthesis of vineyards during the growing seasons due to the closure of the stomata [58]. This disturbance disrupts climate regulation by increasing respiration rates, further impacting carbon sequestration [59]. [PERSON] et al. [60] found that flood-drought events diminish net photosynthetic rates, enzyme activity, and proline content in rice leaves, which collectively inhibit photosynthetic capacity, particularly during sensitive growth stages like tillering. Additionally, extreme storms and prolonged droughts increase dissolved and particulate organic carbon losses by altering soil hydrological connectivity [61]. Flood-induced runoff often transports larger particles, leading to a loss of particulate organic carbon (POC) and smaller soil particles, which reduces soil stability [62; 63; 64].  
* Soil Structure and Carbon Losses  
Drought-induced soil structural instability disrupts carbon storage by breaking down soil macroaggregates, which increases bulk density and reduces porosity [65]. This disintegration exposes organic matter previously protected within these aggregates, making it accessible for microbial decomposition [66]. [PERSON] and [PERSON] [67] describe how soil contraction during drought releases sequestered organic matter, exacerbating carbon loss. [PERSON] et al. [68] further confirm that drought and post-drought conditionssignificantly weaken soil structure, reducing carbon attached to macroaggregates. Such structural changes in soil profoundly affect soil organic carbon (SOC) dynamics and the soil's long-term carbon sequestration potential [69; 70].
## 4 Overall Discussion of Results  
The results reported above show that HSAG-SP, used in place of a minor amount of silica, is able to bring about substantially the same tensile and dynamic mechanical properties as the composite with silica as the only filler, with slightly lower filler networking phenomenon, improving the rheological properties. The effect of HSAG-SP is appreciably different from that of pristine HSAG, particularly as far as the tensile properties are concerned but also considering the dynamic rigidity and the rheological properties.  
A key role seems to be played by the functionalization of HSAG with SP, in particular by the location of the OH groups brought by SP, on the edges on the HSAG layers. The OH groups are able to establish an intimate interaction with silica and even a chemical bond could be hypothesized. They are, in principle, able to react with the silane TESPT. The compatibility of HSAG-SP with silica and the formation of an integrated hybrid filler system appears to be responsible for obtaining the same dynamic-mechanical and tensile properties and improving the rheological properties. In particular, the absence of polar functional groups on the basal planes of HSAG enables disruption of the silica filler network, and this could be the origin of the greater flowability.  
Slightly anisotropic properties are observed in the presence of the hybrid filler system, based on either pristine or functionalized SP: dynamic moduli are only 20% higher in the IP direction than in the TT direction. This behavior appears to be due to the lamellar shape of the carbon filler, and the modification with SP does not play a role.
# Design of Antibody-Functionalized Polymeric Membranes for the Immunoisolation of Pancreatic Islets
## 3 Results and Discussion
### Immuno-Encapsulation of Pancreatic Islets  
The presence of the Acryl-PEG-Anti-GFP coating on the surface of the pancreatic islet-hydrogels was visualized by confocal microscopy that confirmed the uniform coverage of the pancreatic islets. The interfacial photo-crosslinking protocol was designed to obtain a conformal PEG-based coating around each islet (Figure S2). In Figure 9, pancreatic islet-hydrogels were stained with a secondary Anti-Mouse IgG (Fab specific)-Alexa Fluor(r) 488-conjugated antibody. The green fluorescence (upper panels of Figure 9) associated to the Acryl-PEG-Anti-GFP evidences the homogeneous distribution of the coating around the islet. On the other hand, control (CTRL) islet that was not functionalized with the hydrogel, did not show any fluorescence signal (lower panels of Figure 9).  
Figure 8: Confocal microscopy images of PEGDA hydrogel (**a**) and PEGDA hydrogel functionalized with Acryl-PEG-Anti-GFP (**b**) stained with Alexa Fluor® 488 anti-chicken. Scale bars are 100 \(\upmu\)m.  
Figure 9: Confocal microscopy images showing (upper panels) an islet-hydrogel functionalized with Acryl-PEG-Anti-GFP and (lower panels) a CTRL islet stained with a secondary antibody (Alexa Fluor® 488 goat Anti-Chicken). Scale bar = 100 \(\upmu\)m.  
The encapsulation of pancreatic islets within the immuno-functionalized PEG hydrogel is expected to enhance islet cell survival and functionality and does not affect the islet viability as compared to control islets, as shown in Figure 10. Indeed, the viability qualitatively determined with FDA-PI staining 24 h post PEG functionalization did not show evident differences between control and functionalized islets.  
Moreover, the cellular functionality of the islets, in terms of response to glucose, was measured via a glucose-stimulated insulin secretion (GSIS) assay in vitro 24 h post-encapsulation. For all experiments, the insulin stimulation from basal to stimulated levels illustrated the expected response, with stimulated levels higher than basal ones. As shown in Figure 11, the insulin stimulation index was about 4, both for control and encapsulated islet, which, therefore, did not show significantly different behaviors. Therefore, the insulin secretion capacity of encapsulated islets in vitro was maintained compared to control islets.  
Figure 10: Confocal microscopy images of viable PEGYLATED (upper panels) and CTRL (lower panels) islets. The viability was qualitatively detected with the fluorescein diacetate (FDA) _versus_ propidium iodide (PI) staining method 24 h post functionalization. Scale bar = 100 μm.
## 7 Conclusions  
This paper proposed a communication infrastructure to support highly mobile MAV nodes in a stationary low-power mesh. In contrast to earlier research, simulations have shown that it is feasible to extend a TSMP-like WSN stack, which features an IEEE 802.15.4E time synchronized channel hopping MAC layer, to enable rapid, reliable, low power multihop communications with mobile agents.  
Our architecture reserves a unique set of channel offsets to MAV agents, and permits for both low-throughput and streaming data to be transmitted by the MAVs. This flexibility comes at no cost to network reliability, as effects due to external and intra-network radio interference are mitigated through scheduled communications and channel hopping. Furthermore, battery resources of the stationary mesh are preserved due to an extremely low radio duty cycle. This system proposes to greatly increase the range and duration of MAV missions in RF denied environments; future work will focus on extending the already implemented stationary communication infrastructure for the GINA [16] with our proposed hybrid architecture to gather experimental performance metrics.  
Figure 8: Regardless of the density of the underlying mesh network, the MAV successfully hears a fraction of the advertisement packets, and so can communicate during approximately 35% of the available slot frames.
# Advancements and Prospects of Electronic Nose in Various Applications: A Comprehensive Review
## 5 Conclusions  
In conclusion, electronic nose (e-nose) technology presents a compelling avenue for innovation with vast potential across various sectors, from ensuring food safety to enhancing environmental monitoring and healthcare diagnostics, including the non-invasive early detection of various diseases in plants, animals, and humans. The adaptability and efficiency of e-noses are evident, representing groundbreaking technology capable of analyzing complex gaseous mixtures of volatile organic compounds (VOCs) to provide composite metabolite profiles, thus making them highly desirable in healthcare settings.  
While e-nose devices are increasingly garnering interest from healthcare providers, several key challenges must be addressed to fully realize their potential in disease detection and monitoring. These include the standardization of e-nose instruments, sampling protocols, and analytical methodologies to facilitate data comparisons across studies. Additionally, the development of disease-specific e-nose databases, identification of effective VOC biomarkers, expansion of clinical applications, and advancements in electronics and diagnostic software are crucial for widespread acceptance and integration into clinical procedures.  
Collaboration emerges as a key principle in navigating the complexities of e-nose technology. The convergence of expertise from sensor specialists, data analysts, and domain experts is essential for overcoming obstacles and unlocking the full potential of e-noses. This collaborative effort holds the promise of reshaping our perception and interaction with the environment through the integration of olfactory data.  
In essence, continued research and innovation are essential for maximizing the accuracy, reliability, and effectiveness of e-nose instruments as indispensable tools in clinical diagnostics. With concerted efforts and advancements in various facets of e-nose technology, these instruments hold the promise of revolutionizing disease detection and monitoring in healthcare settings worldwide.  
Conceptualization, A.R., E.C. and D.Z.; methodology, A.R.; validation, A.R., H.H., E.C. and D.Z.; writing--original draft preparation, A.R., D.Z. and E.C.; writing--review and editing, A.R. and H.H.; visualization, A.R.; supervision, E.C. and D.Z.; funding acquisition, E.C. and D.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflicts of interest.
# Optimizing HAWK Signature Scheme Performance on ARMv8
## 3 HAWK Optimization Through Profiling
### Optimized Implementation of Critical Operations  
Optimized implementation methods include minimizing memory access and parallel implementation using vector registers. However, beyond these methods, an important factor in optimization is the use of efficient instructions. In this study, we focus on optimizing computational processes with high overhead, as previously analyzed by profiling in the HAWK algorithm, utilizing the efficient instructions provided by the ARMv8 architecture.  
Before explaining the optimization implementation, we will first explain in detail the instruction(csel, sbfx) used to implement the optimization.  
First, as shown in Figure 1, the 'csel' instruction stores two different registers(Rn, Rm) into Rd(destination register) depending on the condition. This instruction stores Rn or Rm  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol Name** & **Time** & **Weight** \\ \hline Total(crypto\_sign) & 133.00 (ms) & 100.0\% \\ \hline sig\_gauss & 65.00 (ms) & 49.2\% \\ \hline hawk\_ntrugen\_Hawk\_regen\_fg & 22.00 (ms) & 16.6\% \\ \hline mp\_NTT & 22.00 (ms) & 16.5\% \\ \hline etc. & 24.00 (ms) & 17.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Profiling analysis results for the crypto_sign process (parameter: hawk-1024, loop: 1000).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol Name** & **Time** & **Weight** \\ \hline Total(crypto\_verify) & 113.00 (ms) & 100\% \\ \hline mp\_poly\_to\_NTT & 31.00 (ms) & 27.3\% \\ \hline decode\_gr & 23.00 (ms) & 20.1\% \\ \hline mp\_poly\_to\_NTT\_autoadj & 14.00 (ms) & 12.3\% \\ \hline etc. & 45.00 (ms) & 41.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Profiling analysis results for the crypto_verify process (parameter: hawk-1024, loop: 1000).  
in Rd depending on the state of the condition flag. Since the value stored varies depending on the condition flag, it is used after executing a instruction that sets a condition flag such as 'cmp' before using 'csel' instruction. There are four condition flags: N(Negative), Z(Zero), C(Carry) and V(Overflow). For the 'lt'(less than) condition used in the figure, it is judged as true if N and V are different.  
Next, as shown in Figure 2, The'sbfx' instruction copies a specific range of bits from a source register to a destination register, while sign-extending the highest bit in the specified range into the higher bits that are not included in the copied range. Here, ''#lsb' refers to the starting point of the copied range, and ''#width' denotes the length of the range.  
Through profiling analysis in the previous section, we identified the operations with the highest weight in each computation process. The HAWK algorithm's key-generation process takes the largest portion, with the functions zint_mod_small_unsigned() and zint_add_mul_small() accounting for 43% and 10% of the key-generation process, respectively. We performed optimizations on the operations used in these two functions.
# Optimization of Airflow Organization in Bidirectional Air Supply Data Centers in China
## 3 Results  
### Model Validation  
To validate whether the model meets engineering practice requirements, the floor outlet air velocity data at corresponding locations were obtained through numerical simulation. The actual floor outlet air flow rate was then calculated from the measured outlet velocity values, as shown in Equation (12).  
\[Q=\frac{C_{1}Q_{\text{r}}\beta}{3600} \tag{12}\]  
where \(Q\) is the actual air supply volume of the CRAC unitm\({}^{3}\)/s; \(C_{1}\) is the flow coefficient, taken as 0.95 in this study; \(Q_{\text{r}}\) is the rated airflow capacity, m\({}^{3}\)/h; and \(\beta\) is the fan speed demand, a dimensionless constant.  
Figure 5a presents the airflow comparison at the perforated tiles in rows AA and AH. The simulated airflow rates demonstrate close agreement with measured data. The maximum observed deviation between simulated and measured floor airflow rates was 0.029 m\({}^{3}\)/s, with relative errors ranging from 0.25% to 16.17% and a mean relative error of 4.28%.  
Figure 5: Comparison of measured and simulated values. (**a**) Airflow rates of perforated tiles; (**b**) temperature of the racks.  
Figure 5b displays the temperature comparison at both the inlet and outlet of the server racks. Both simulated and measured temperatures exhibit consistent variation trends. For cabinet inlet temperatures, the maximum relative error is found to be between 0.28% and 8.33%, with an average of 3.11%. The outlet temperature comparison shows maximum relative errors ranging from 0.08% to 13.89%, with an average relative error of 2.92%.  
To further evaluate model validity, the Mean Bias Error (MBE) and Root Mean Square Error (RMSE) metrics were introduced. Smaller MBE and RMSE values indicate higher model prediction accuracy. The calculation equations are shown in (13) and (14) [33].  
\[\text{MBE}=\frac{1}{n}\sum_{i=1}^{n}\left(\hat{y}_{i}-y_{i}\right) \tag{13}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(\hat{y}_{i}-y_{i}\right)^{2}} \tag{14}\]  
where \(\hat{y}_{i}\) represents the i-th simulated value, \(y_{i}\) denotes the i-th measured value, and n is the number of data points.  
The calculated MBE for floor airflow rate was \(-0.001\) m\({}^{3}\)/s with an RMSE of 0.011 m\({}^{3}\)/s. For cabinet inlet temperature, the MBE reached 0.70 \({}^{\circ}\)C, and the RMSE was 0.92 \({}^{\circ}\)C. The outlet temperature showed an MBE of 0.67 \({}^{\circ}\)C and RMSE of 1.46 \({}^{\circ}\)C. All values remained within acceptable ranges.  
Several factors contributed to the observed deviations. Personnel movement during measurements caused airflow disturbances. Additionally, simplifications were made in the CFD modeling process, including the omission of server cabling and internal cabinet structures. Despite these limitations, the simulated and measured values exhibited consistent trends for both airflow rates and temperatures. The developed data center model demonstrates sufficient accuracy for subsequent engineering simulations and design optimization studies.
# Valorization of Agri-Food Waste into PHA and Bioplastics: From Waste Selection to Transformation
## 3 Production of Bioplastics from Volatile Fatty Acids
### Accumulation of PHA Fermentation Conditions
[156], an experiment was carried out in which the concentration of the phosphorus source was varied to see how this affected the accumulation of PHAs by this microorganism. Monobasic potassium phosphate (KH\({}_{2}\)PO\({}_{4}\)) was used as the phosphorus source. Shake flasks of 500 mL containing 100 mL of culture inoculated with 10% (\(r/v\)) inoculum from a previous inoculum growth stage were used. They were incubated at 37 \({}^{\circ}\)C for one week. The concentrations of KH2 PO4 tested were 0.5, 0.25, and 0.00375 g/L. In this study, the best result was obtained with 0.5 g/L KH\({}_{2}\)PO\({}_{4}\), the accumulation being 0.95 g/L with 15.6% dry biomass. _Pseudomonas elevovorans_ has also been used for PHA synthesis by [PERSON] et al. [157], as it is able to synthesize mcl-PHA in culture conditions with carbon excess and nitrogen deficiency. [PERSON] describes how a yield of 1.17 g/L biomass was achieved with a synthetic medium (8.4 g/L glucose, 5.7 g/L sodium ammonium phosphate, and 35.4 mM phosphate buffer). The results were satisfactory since, as described by the authors, a biomass yield 1.7 times higher than with the conventional medium was obtained after 48 h of culture. In this case, the _P. olevovorans_ strain used was DSM 1045 in a culture incubated for 48 h at 30 \({}^{\circ}\)C under shaking conditions (150 rpm). Some _Aeromonas_ species are also used to produce PHA due to their simple and robust growth and their ability to synthesize homopolymers, copolymers, and terpolymers with unique material properties [158]. For example, _A.hydrophilic_, using sodium gluconate as substrate and with phosphorus limitation, is able to generate a biomass concentration of 4.63 g/L with a P(3 HB) content of 35.1% [159]. Another example would be the _A. hydrophilla_ 4 AK4 strain that, with phosphorus limitation, achieves a biomass concentration of 16.2 g/L with a P(3 HB-co-HHx) content of 51.5; using, in this case, lauric acid and glucose as substrate [160]. In addition, from these PHAs, _A. hydrophilla_ 4 AK4 is also able to synthesize P(3 HV) from an undecanoic acid medium, achieving yields of 5.01 g/L biomass with 45.2% PHA accumulation [161]. As can be deduced, the variability of the type of PHA accumulated, as well as the yield with _Aeromonas_ will depend on the strain used, the substrate, and fermentation conditions such as the limiting nutrient. Table 3 summarizes the main fermentation conditions, the yields and the type of PHA obtained with each one of the reviewed microorganisms. As shown in this section, there are many types of PHA-producing microorganisms, and each requires a different medium and fermentation conditions. The use of one or the other will depend on the objective, the type of PHA sought, and the type of waste to be recovered (in the case of using a waste medium, such as biogas digestate, for example).
## 3 Result and Discussion
### Bilayer Phosphorene  
The stable stacking order of bilayer phosphorene is of the AB type such that the bottom layer is shifted half the lattice period along the \(x\) or \(y\) directions, and this is in agreement with previous studies [95; 96]. The crystal structure of AB-stacked bilayer phosphorene is shown in Figure 19a,b.  
Figure 19: (**a,b**) Top and side view of the atomic structure of bilayer phosphorene (BLP). (**c**) The band structure of bilayer phosphorene along the high symmetry \(X-\Gamma-Y\) directions. The Fermi level is set at 0 eV. The optimized interlayer separation between the closest phosphorus atoms is indicated with \(d\), being 3.56 Å.  
Figure 18: The electron-energy loss function of monolayer phosphorene for different values of \(q\) along the \(\Gamma X\) and \(\Gamma Y\) directions (upper panel). In the bottom panel, the plasmon modes corresponding to the peaks in the EEL function in the upper panel are plotted. In this case, the Fermi level is shifted up by 0.1 eV, corresponding to \(n=3.9\times 10^{13}\,\mathrm{cm}^{-2}\).  
When two monolayers are combined to create a bilayer, the gap reduces and two additional bands emerge around the gap at the \(\Gamma\) point [97]. Bilayer phosphorene possesses a direct band gap of 0.63 eV, in agreement with the band gap recently reported in [98]. Importantly, the inclusion of many-body effects using GW increases the band gap to 1.45 eV.  
The electronic band structure of AB-stacked bilayer phosphorene is illustrated in Figure 19c based on our DFT simulations. We find that the valence band maximum is contributed by the localized states of \(P\) atoms, while the conduction band minimum is partially contributed from the delocalized states, especially in the interfacial area between the top and bottom layers [99].  
In order to investigate more, we illustrate the real and imaginary parts of interband and intraband contributions of the non-interacting response function of bilayer phosphorene in Figure 20 for a specific momentum transfer in the amount of 0.046 A\({}^{-1}\) and \(E_{\rm F}=0.05\) eV. Our ab initio calculations [100] show that the collective electronic excitations in bilayer phosphorene originated from intraband term of \(\chi^{0}(q,\omega)\), and the other one is not be important enough to be considered.  
We illustrate the loss functions for different amounts of \(q\) and also the plasmon modes of bilayer phosphorene along the armchair direction in Figure 21. For this system, we have shifted the Fermi level up by 0.05 eV to imitate the effect of finite doping. As expected, bilayer phosphorene involves two plasmon modes. One of them follows the \(\sqrt{q}\) behavior as the optical mode, as seen in its monolayer. The other one is a damped acoustic mode with a linear dispersion at the long wavelength limit.  
Figure 21: _Cont._  
Figure 20: The non-interacting response function in units of the Fermi-level density of states as a function of \(\hbar\omega\) of bilayer phosphorene for \(q=0.046\) Å\({}^{-1}\).