esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of HPH on Protein Secondary Structure  
The effect of HPH treatment on the secondary structure of _A. pyrenoidosa_ proteins was carried out via FTIR analysis. The wavenumber range between 1600 cm\({}^{-1}\) and 1700 cm\({}^{-1}\) corresponds to the absorption peak range of the amide 1 band, which reflects the protein secondary structure (\(\alpha\)-helix, \(\beta\)-sheets, \(\beta\)-turns, and random coils) [37; 38]. Figure 2 presents the differences between FTIR spectra of untreated (control) and HPH-treated APPC from untreated biomass. Specifically, the wavenumber range between 1635 cm\({}^{-1}\) and 1643 cm\({}^{-1}\) corresponds to the absorption peak range of the \(\alpha\)-helix, between 1608 cm\({}^{-1}\) and 1621 cm\({}^{-1}\) to \(\beta\)-sheet, between 1643 cm\({}^{-1}\) and 1674 cm\({}^{-1}\) to \(\beta\)-turn, and between 1621 cm\({}^{-1}\) and 1635 cm\({}^{-1}\) to random coils. These absorption peak ranges were selected based on the criteria established by [PERSON] et al. (2007) [38]. The secondary structure of untreated (control) and HPH-treated APPC from untreated biomass is presented in Table 1. Increasing the treatment pressure up to 800 bar resulted in a significant decrease in \(\alpha\)-helix and \(\beta\)-sheet structures by 10.7% and 8.3%, respectively, compared to untreated APPC. However, the \(\beta\)-turn structures and the random coils were significantly increased by 6.9% and 4.8%, respectively. These changes were due to the hydrogen bonds that stabilize the \(\alpha\)-helix and \(\beta\)-sheets. Under higher pressure values, these bonds weaken, leading to the transformation of \(\alpha\)-helices and \(\beta\)-sheets into random coils and \(\beta\)-turns [28]. The increase in the \(\beta\)-turn structures and random coils, which are less stable structures compared to \(\alpha\)-helices and \(\beta\)-sheets, has a positive effect on the protein unfolding and leads to the exposure of the hydrophobic protein core [37; 39]. Similar findings have been reported by [PERSON] et al. (2018) in their study on the effect of HPH on the secondary structure of oyster protein isolates. Specifically, they observed that the increase in HPH pressure up to 800 bar (80 MPa) significantly decreased the \(\alpha\)-helix structures from 48.34% (untreated) to 36.24%, while \(\beta\)-turn structures and random coils increased from 9.77% (untreated) to 18.04% and from 18.38% (untreated) to 22.37%, respectively [28].  
Figure 1: Dependence of (**a**) _A. pyrenoidosa_ protein concentrate, (APPC) and (**b**) protein recovery yield (\(y_{\text{p}}\)) on high-pressure homogenization (HPH) treatment of _A. pyrenoidosa_ biomass from 1 bar (untreated) to 800 bar for one- and four-pass treatment. Error bars represent the standard deviation between experimental replications. Different superscript letters indicate significant differences between means \(\pm\) standard deviation as calculated by [PERSON]’s multiple range test for a significance level of \(p<0.05\).
## 3 Results and Discussion
These improvements are crucial for practical applications, such as UAV-based monitoring, which supports precision forestry and conservation in dynamic forest environments. The Chi-Square test results in Table 5 evaluate the association between time of day (morning, afternoon, evening) and classification outcomes (correct or incorrect) for hackberry, northern red oak, and bitternut hickory using the EfficientNet-B3 model for AI-based tree bark identification. Hackberry (\(\chi^{2}\) = 10.9877, \(p\) = 0.0041) and northern red oak (\(\chi^{2}\) = 7.6633, \(p\) = 0.0217) show significant associations (\(p\) < 0.05), confirming that lighting conditions significantly influence their classification accuracy. Hackberry's accuracy peaks at 95.56% in the evening, likely due to low-angle light enhancing its nodular appearance, while northern red oak's accuracy drops to 80.80% in the afternoon, where harsh light obscures its grooved furrow pattern, compared to 91.67% in the morning and 90.00% in the evening. Bitternut hickory's non-significant result ((\(\chi^{2}\) = 4.8251), (\(p\) = 0.0896)) aligns with its consistently low accuracies (20.83-33.33%), reflecting challenges in detecting its subtle bark slits across all lighting conditions, compounded by a smaller evening sample (25 images vs. 30 for others). These findings complement prior results found in [PERSON] et al. (2024) [9], where bark moisture reduced accuracy by 8.19% in wet conditions, likely exacerbating lighting effects (e.g., wet bark in afternoon light for northern red oak), and cardinal direction had minimal impact (4.72% difference). The significant results for hackberry and northern red oak highlight their sensitivity to lighting due to distinct morphological features, while bitternut hickory's non-significant result highlights its morphological challenges. The study suggests augmenting the CentralBark dataset [9] with more afternoon images for northern red oak and diverse conditions for bitternut hickory, alongside preprocessing techniques like illumination normalization [2]. Hackberry's robustness suggests less augmentation for distinct species, while bitternut hickory needs species-specific strategies.
## 3 Strategic Development of Ultrafiltration Membrane
### Operating Pressure  
When it comes to TMP, higher value corresponds to increase in flux. However, to some extent, the permeation flux is not proportional toward the increase of TMP according to Darcy's Law. Despite during the fractionation of saithe protein hydrolysate with Microlab40 pilot plant UF/NF membrane (recirculation mode), no concentration polarization was observed under 15 bar, reversible polarization that leads to nonlinear flux emerged between 15 and 25 bar. In the next step, further pressure increase made the flux becoming independent variable from TMP [194] (Figure 7c). Adjusting TMP also give an impact on selectivity, along with initial peptide concentration and VRF. With recirculation mode, under 30 bar and 30 g L\({}^{-1}\) saithe protein, most of the peptides lower than 1 kDa can be collected in the permeate side [194]. Yet, TMP determine the sieving characteristics of UF membrane since MWCO decreased (retention rate higher) while TMP increased because of the elastic deformation of membrane pores [194]. However, complex hydrolysate mixtures resulted in opposite outcome due to low fouling properties of dextran solution [213].  
In order to increase permeate flux of electroacidified soy protein by cross-flow UF, a computational fluid dynamics study showed that increasing TMP determined more significantly as opposed to enhancing axial velocity which only provide mediocre permeate flux increase [201] (Figure 7c). Regarding the fouling, it is worth to note that firstly, irreversible fouling had to be manipulated not by tuning the TMP, but by the viscosity of hydrolysate (concentration of protein and inherent minerals) in a complex mixture since this parameter is the most sensitive amongst all. Secondly, diffusion coefficient is undoubtedly a major influence of the protein back-transport from membrane surface for nonelectroacidified soy protein hydrolysate [201].  
Different targets of final products require unlikely the TMP optimization. In producing high content of oligosaccharides from goat milk by cross-flow diafiltration up to 4 DV with 50 kDa tubular ceramic membrane, higher TMP transmitted more protein (11%) into the permeate side, which even though favorable to decrease operation time by 10.5 h, lowering the oligosaccharides fractions [200]. While treating feta cheese whey to develop powder with high content of lactoferrin and IgG by 100 kDa cylindrical polyvinylidene fluoride (PVDF) membrane, high TMP (5 bar) is attributed to fouling and concentration polarization, despite to some point increase the permeate flux which has no correlation with better targeted compounds [214]. In contrast, in order to separate specific peptides from whey, one case showed that transmission of \(\alpha\)-lactoglobulin, derived from low-heat skim milk powder, declined by 0.13 point from 0.78 in the increase of TMP between 40 and 80 kPa. However, \(\beta\)-lactoglobulin transmission was improved to be 0.35 (initially 0.30) in the same changes of TMP [148].
## 3 Results and Discussion
### Limitations and Future Prospects  
Despite the promising results, several limitations must be acknowledged to provide a balanced assessment of this valorization approach. The substantial reduction in antioxidant activity during thermal processing, with 80-85% losses in DPPH and ABTS values, indicates that current processing conditions require optimization to preserve bioactive compounds. The preliminary sensory evaluation, conducted with only 24 participants, revealed pronounced gender differences in acceptance, which indicates that the current formulation may not appeal universally, particularly to male consumers who demonstrated lower acceptance scores across all attributes. Additionally, the focus on tapioca flour, while strategically chosen for local resource utilization, may limit global applicability in regions where alternative starches are more accessible. The sensory evaluation was conducted with only 24 participants, which limits the statistical power and generalizability of the findings. This sample size is insufficient for robust statistical testing of consumer preferences and precludes definitive conclusions about demographic differences in product acceptance.  
Future research directions should prioritize processing optimization studies to minimize bioactive compound degradation through modified thermal treatments or protective ingredient incorporation. Comprehensive sensory studies with larger, demographically diverse populations are essential to identify formulation modifications that enhance universal consumer acceptance. An investigation of alternative starch sources would significantly enhance global applicability and allow for adaptation to different regional agricultural systems. Extended nutritional characterization beyond dietary fiber content, including protein quality assessment and mineral bioavailability studies, would strengthen the nutritional positioning of this ingredient. Furthermore, scale-up investigations addressing processing challenges, economic viability analysis, and extended shelf-life studies under various storage conditions are crucial for successful commercialization. The development of different product formats beyond meat substitutes could expand the application opportunities and maximize the valorization potential of _L. cylindrica_ seed press cake across diverse food systems.
## 3 Results
### Contributions of Changes in Important Influencing Factors to WUE Trend
#### 3.3.2 Contributions of Important Influencing Factors to WUE Trend
The contributions of the changes in different influencing factors to the WUE trend for different vegetation types as well as the relative contribution rates are shown in Figure 9. The changes in LAI and precipitation had a positive contribution to the WUE trend of all the vegetation types (Figure 9a). In forest and swamp, LAI had a slightly greater contribution to the WUE variation than precipitation. In meadow, shrub and cultivated vegetation, LAI had a greater contribution than precipitation. The difference between the contributions of LAI and precipitation to the WUE trend of cultivated vegetation was the largest. Changes in relative humidity had a positive contribution to the WUE trend of forest, swamp, shrub and meadow, but they had a negative contribution to the WUE of cultivated vegetation. Wind speed had a greatly negative contribution to the WUE in all the vegetation types except for shrub. Regionally, LAI was the primary contributor to the WUE trend (0.008 g C m\({}^{-2}\) mm\({}^{-1}\) yr\({}^{-1}\)), followed by precipitation (0.002 g C m\({}^{-2}\) mm\({}^{-1}\) yr\({}^{-1}\)). The mean contribution of wind speed to the WUE trend of the study area was negative (\(-\)0.001 g C m\({}^{-2}\) mm\({}^{-1}\) yr\({}^{-1}\)). Figure 9: (**a**) Contributions and (**b**) relative contribution rates of changes in LAI, PRE, RH, and WS to WUE trends for different vegetation types. For the whole region, LAI had the maximum relative contribution rate (44.57%) to the WUE trend (Figure 9b). The relative contribution rates of wind speed and precipitation to the WUE trend were \(-\)7.66% and 8.88%, respectively. The contribution rate of relative humidity was less than 5%. The relative contribution rate of LAI was relatively high in cultivated vegetation and meadow, but low in forest and swamp, similar with that of precipitation. The relative contribution rates of precipitation in forest and swamp were significantly higher than those in the other three vegetation types. Relative humidity had a limited contribution rate to all the vegetation types. The relative contribution rates of wind speed to the WUE trend of all the vegetation types were negative, especially in swamp.
## 3 Solution of the Optimization Problem
### Optimality Conditions for SDP Relaxation  
In this subsection, the tightness of the proposed SDP relaxation in (20) is revealed. Before introducing the theorem, a crucial expression, which is a cornerstone in revealing the tightness of our proposed SDP relaxation optimization problem, is defined as  
\[\mathbf{T}^{*}=-\alpha^{*}\mathbf{I}_{N_{T}}+\sum_{k=1}^{K_{E}}\sum_{n=1}^{N_{R}}\left( I_{E,k}^{(n,n)(*)}-\mathbf{K}_{E,k}^{(n,n)(*)}\right) \tag{22}\]  
and \(\tilde{r}=\text{rank}(\mathbf{T}^{*})\), where \(I_{E,k}^{(n,n)(*)}\in\mathbb{H}_{+}^{N_{T}}\) and \(\mathbf{K}_{E,k}^{(n,n)(*)}\in\mathbb{H}_{+}^{N_{T}}\) denote the \(n\)-th entry matrices on the diagonal of \(\tilde{\mathbf{G}}_{k}^{H}\mathbf{X}_{k}\tilde{\mathbf{G}}_{k}\in\mathbb{H}_{+}^{N_{T}N_{R}}\) and \(\tilde{\mathbf{G}}_{k}^{H}\mathbf{Y}_{k}\tilde{\mathbf{G}}_{k}\in\mathbb{H}_{+}^{N_{T}N_{R}}\), respectively; where \(\tilde{\mathbf{G}}_{k}=\left[I_{N_{T}N_{R}}\ \tilde{\mathbf{g}}_{k}\right]\), and \(\mathbf{X}_{k}\) and \(\mathbf{Y}_{k}\) represent the Lagrange dual variables corresponding to \(N_{E,k}(\mathbf{W},\mathbf{V}_{0},\tilde{\mathbf{\lambda}}_{k})\) and \(T_{E,k}(\mathbf{W},\mathbf{V}_{0},\mathbf{V},\tilde{\mu}_{k},t)\), respectively. Moreover, \(\mathbf{N}_{1}\in\mathbb{C}^{N_{T}\times(N_{T}-p)}\) is modeled as the orthogonal basis of the null space of \(\mathbf{T}^{*}\) and \(\mathbf{\pi}_{1,n}\in{}^{n_{t}\times 1}\), \(1\leq n\leq N_{T}-\tilde{r}\) is the \(n\)-th column of the matrix \(\mathbf{N}_{1}\). Based on this, we give the following theorem. **Theorem 1**. : _The optimal solution \(\{\mathbf{W}^{*},\mathbf{V}^{*},\mathbf{V}_{0}^{*},\rho^{*},t^{*}\}\) of the optimization problem (20) is characterized as the following conditions:_  
_1. The optimal solution \(\mathbf{W}^{*}\) can be formulated as_  
\[\mathbf{W}^{*}=\sum_{n=1}^{N_{T}-p}b_{n}\mathbf{\pi}_{1,n}\mathbf{\pi}_{1,n}^{H}+a\mathbf{\iota }^{H}, \tag{23}\]  
_where \(b_{n}\geq 0\), \(\forall n\) and \(\mathbf{\iota}\in\mathbb{C}^{N_{T}\times 1}\), \(\|\mathbf{\iota}\|_{2}=1\) satisfies \(\mathbf{\iota}^{H}\mathbf{N_{1}}=\mathbf{0}\)._  
_2. If the solution \(\mathbf{W}^{*}\) given in (23) has \(\text{rank}(\mathbf{W}^{*})>1\), i.e., there is at least an \(n\) such that \(b_{n}>0\), then we have the following alternative solution_  
\[\tilde{\mathbf{W}}^{*}=\mathbf{W}^{*}-\sum_{n=1}^{N_{T}-p}b_{n}\mathbf{\pi}_{1,n}\mathbf{\pi}_ {1,n}^{H}=a\mathbf{\iota}^{H}, \tag{24}\]  
\[\tilde{\mathbf{V}}^{*}=\mathbf{V}^{*}+\sum_{n=1}^{N_{T}-p}b_{n}\mathbf{\pi}_{1,n}\mathbf{\pi}_ {1,n}^{H}, \tag{25}\]  
\[\tilde{\mathbf{V}}_{0}=\mathbf{V}_{0}^{*},\ \tilde{\mathbf{\rho}}^{*}=\rho^{*},\ \tilde{t}^{*}=t^{*}, \tag{26}\]  
_with \(rank\big{(}\tilde{\mathbf{W}}^{*}\big{)}=1\) serving as the optimal solution of optimization problem (20)._  
Proof.
# Hide45: A Method for Optimal Payload Data Hiding in Base45 Encoded Strings
## 7 Conclusions  
This work has presented **Hide45**, an improvement over a previously published method for embedding data into printable encoded strings using a Base45 alphabet. The proposed solution optimizes the bit assignments for the sequences normally used for the encoding exploiting the unused sequences to give more bits to the highest frequency sequences.  
Modeling and solving the problem as an integer linear program leads to significant improvement upon previously published methods. Experimental results showed a meaningful improvement (up to 53%) for those data types having skewed distributions of their double octets binary data. Moreover, it may be shown (see Appendix A) that the encoding/decoding table can be compressed in less than 11 Ko.  
Since **Hide45** is able to store a higher amount of information by an optimal assignment of unused sequences, it can be useful to embed into files longer and safer security fingerprints and digital signatures, among more general data. Furthermore, part of the embedded information can be employed to check whether the transmitted file has undergone to an attack aiming at its modification.  
A limitation of this method is related to the fact that the optimization is done using a statistic of sequence frequencies over several files. Even though the average payloads exhibit a very low standard deviation among file types over the tested files (see Table 2), in the case where a file exhibits a frequency distribution different from the one considered by the statistic, it will result in a reduced payload. A possible development of this technique  
Figure 4: Cumulative distributions of the most frequent 128 octet pairs (zoom on the leftmost area of graph in Figure 3) sorted in decreasing order of frequency.  
Figure 3: Cumulative distributions of all octet pairs sorted in decreasing order of frequency.  
can be the building of a encoding/decoding table _EncDec_ specific for every file, by optimizing over its own frequency distribution; this has a disadvantage nevertheless, since the table must be transmitted with the file. This can lead to advantages only for bigger files, since the encoding/decoding table will occupy some of the embedding capacity of the file itself; the objective for future research will be to analyze how to embed this table, compute the required space, and check whether this approach will be more convenient than using a precomputed table.  
Further developments of the method will be also towards the analysis of different optimizations methods.  
All authors contributed equally to this work, in the following categories. M.B., D.C., A.D.: Conceptualization, methodology, software, validation, writing--original draft, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the Italian Ministero dell'Universita e della Ricerca.  
Not applicable.  
No data sharing is required as any set of files having a format analyzed in this work will produce comparable and similar results.  
The authors declare no conflict of interest.
## 2 Results and Discussion
### Evaluation of Catalytic Performance
#### 2.2.4 Substrate Universality Investigation  
To assess P-BVIMCl's capability in converting various epoxides into corresponding cyclic carbonates, we systematically investigated its catalytic performance toward epoxides with different substituents, as summarized in Table 2. The exceptional 93.4% yield for GLC synthesis originated from hydrogen activation of terminal hydroxyl groups in GO, which facilitated reaction progression. Both epichlorohydrin and epibromohydrin demonstrated excellent conversions (>93.0% yield) with high selectivity (>95.0%), attributed to electron-withdrawing effects at the terminal position that enhanced nucleophilic attack by Cl\({}^{-}\) anions. However, epoxides bearing long-chain terminal groups exhibited reduced efficiency. Butyl glycidyl ether showed moderate conversion (84.7% yield), likely due to diffusion limitations caused by steric hindrance from its long-chain terminal group [43]. Notably, cyclohexene oxide displayed dramatically lower reactivity under identical conditions (17.9% yield after 10 h at 120 \({}^{\circ}\)C), primarily constrained by severe steric effects from its bulky terminal benzene ring [44]. Surprisingly, styrene oxide achieved 92.4% carbonate yield despite its aromatic structure, suggesting effective accommodation of this substrate within the catalyst's active sites.  
Figure 6: Effects of temperature (**a**), CO\({}_{2}\) initial pressure (**b**), catalyst dosage (**c**), and reaction time (**d**) on cycloaddition performance.
# Towards a Digital Twin Warehouse through the Optimization of Internal Transport
## 3 Results  
### Equivalent Layouts Occupancy  
Results regarding equivalent layouts can be observed in Table 1. As the number of aisles increases, the associated layouts reduce the overall storage capacity. In order to eliminate scenarios that are not operational from the comparison, either because they do not reach the reference capacity or because they are very close to their maximum storage capacity, the combinations with an occupancy rate of more than 95% have been discarded from the analysis. According to the determined criteria, there are no equivalent layouts for occupancies greater than 85%, and in any case, the number of them decreases in parallel with the growth of the occupancy rate, as expected.
## 3 A Review of Evaluation Criteria
### Evaluation Measures Related to Scalability  
Scalability of a network means that the protocols running in the network perform well as the network grows larger or as the workload increases [29]. Scalability is an important factor in designing routing protocols for WSN. A good routing protocol has to be scalable and adaptive to the changes in the network topology. Routing packets within a large scale WSN without storage overhead and routing table updates is a challenging problem. To constrain this communication overhead, routing in sensor network demands efficient protocols for routing packets.  
For large-scale WSNs, scalability is an important metric that measures how a protocol performs at varying node density, overall network size, or number of data sources and sinks. Many researchers have tried to establish mathematical models for scalability, and using these mathematical models, they put some numerical values against this qualitative metric to compare different protocols/architectures.
## 4 Optimal Placement of PZT Actuators  
The arrangement of PZT actuators has a large impact on the control efficiency of an active reflector. A large number of actuators will increase the weight and reduce the reliability of the active reflector. So, the optimal distribution of PZT actuators must be investigated. The arrangement optimization of PZT actuators is a discrete variable optimization problem. The gradients of an objective function with respect to the discrete variables are difficult to obtain. So, gradient-based methods are not suitable for the arrangement optimization of actuators. The random search algorithm is another type of heuristic method, in which the gradient of the objective function is often not required. The Genetic Algorithm (GA)is a very effective and robust heuristic method for searching the optimal solution of non-convex problems without deriving gradients. It mimics the evolutionary behavior of living creatures. It is employed to optimize the placement of actuators and the corresponding voltages applied.