esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Bacterial Inactivation Effect and Kinetics Model Analysis
#### 3.1.1 Effect of Solid Surface on Inactivation of _S. typhimurium_ and _S. aureus_ by DBD-NTP  
For inactivation of the solid surface (_S. typhimurium_ for Figure 1a and _S. aureus_ for Figure 2a), a steep drop in the slope of inactivation regression lines was noted within the first 20 s. Thereafter, the increment of the pathogen lethality slowed down, following an exponential relationship with treatment time. After 80 s, slopes became mostly flat for most data, indicating that the increase in treatment time does not affect the germicidal efficacy much. Populations of _S. typhimurium_ and _S. aureus_ on the solid surface were reduced to 2.83 and 2.29 log/cm\({}^{2}\) in 60 s. However, treatments for an additional 80 s merely reduced them by 0.29 and 0.6 log/cm\({}^{2}\), respectively. Similar survival curves were found when low-pressure plasma (1-10 Torr) and UV irradiation were evaluated for their bacteria inactivation kinetics [20]. The kinetics of UV irradiation was ascribed to suppression due to reabsorption by emitting gas from plasma. Similarly, the reduction in inactivation of microbes in kinetic curves with DBD-NTP could be attributed to the \"quenching\" effects between reactive oxygen/nitrogen species formed in packages during extended treatment time [21; 22]. At this power level the UV radiation does not play a significant direct role in the inactivation process. In DBD plasma discharges, reactive species generated through various collisional pathway, commonly, used oxygen and nitrogen gas plasma are excellent sources of reactive oxygen-based and nitrogen-based species, such as O, \(\bullet\)OH, NO and NO\({}_{2}\)[23]. Firstly, reactive species were regarded as the primary factors in killing microorganisms. They caused oxidation of unsaturated fatty acids and produced unsaturated fatty acid peroxide formation, thereby impeding transport of the biochemical by-products across the membrane. Beyond that, proteins and nucleic acids react with reactive species to formation of 2-oxohistidine and 8-hydroxy-2 deoxyguanosine [24]. Besides, some reports pointed out that charged particles play important roles in inactivation. Electrons and ions can cause surface lesions in membranes by direct bombardment damage. [PERSON] et al. [25] and [PERSON] et al. [26] suggested that electrostatic force caused by charge accumulation on the outer surface of the cell membrane could overcome the tensile strength of the membrane and cause its rupture. This phenomenon is more likely to occur on gram-negative bacteria, whose cell membrane is always irregular. These irregularities offer small radii of curvatures that cause localized high outward electrostatic forces which trigger rupture of gram-negative bacteria [23].
## 3 Results and Discussion  
### Wood Species Identification  
The internal anatomical structure of the tested sample examined by SR-\(\mu\)-CT are visible in Figure 2a. The images revealed the presence of soil particles. The anatomical characteristics are consistent with _Cedrus libani_ taxonomy. In fact, distinctive growth ring formations with clear transitional boundaries and periodic patterns, which are characteristic of Mediterranean ecdar species, were observed. The cellular architecture displayed systematic tracheid arrangements typical of gymnosperm wood anatomy, with dimensions and organizational patterns specifically concordant with _C. libani_ morphology. Notable anatomical features include well-defined ray structures, resin canal formations and degradation patterns consistent with archaeological wood specimens [80]. This identification aligns with previous historical evidence of extensive cedar importation from Lebanon during the Old Kingdom period, particularly for elite shipbuilding projects, due to its superior mechanical properties including enhanced durability and hydrophobic characteristics [47; 81; 82]. The presence of _C. libani_ in this royal naval architecture exemplifies the sophisticated material selection practices employed in ancient Egyptian manufacturing processes.  
These results were confirmed by both the SEM images (Figure 2b) and the light microscope analyses (Figure 3). In particular, SEM analyses highlighted degraded parts of the wood, soil residues and wood tissue separations.  
The micromorphological features of the wooden sections, observed under a light microscope (Figure 3), highlighted the presence of a distinct cellular arrangement, including tracheids, ray parenchyma and resin canals, which are characteristic of _Cedrus libani_ in the transverse section (Figure 3a). Additionally, the radial section exhibited longitudinal tracheids with bordered pits (Figure 3b,c) [28; 47].
## 3 The Critical Importance of Product Identity in Agricultural Supply Chains
### Challenges and Barriers to Adoption
For small farmers or producers in developing regions, the cost and complexity can be prohibitive. They may lack the needed digital infrastructure or funding to participate [1]. Moreover, if the benefits (e.g., better prices or market access) are not immediately clear to them, they have little incentive to incur extra effort for data entry. Thus, equitable value distribution is a concern: downstream actors (like retailers) might reap the rewards of improved consumer trust, but upstream farmers might not see direct profit gains. Aligning incentives, for example, by offering premiums for blockchain-verified goods or providing free tools and training can help, but it requires coordination. The ROI for blockchain traceability can be hard to quantify, especially when many benefits are intangible (trust, risk reduction) or long-term. This uncertainty can make companies hesitant to invest heavily in a technology that is still relatively new to the sector. * Regulatory and organisational barriers: The regulatory environment for blockchain in supply chains is still maturing. There is often legal ambiguity around smart contracts (are they recognised as legally binding? ), electronic records, and data ownership on a distributed ledger. Different countries have varying stances on blockchain use and data sovereignty, which can complicate cross-border supply chains. Additionally, the lack of clear governance frameworks for industry consortia can be a barrier, as participants need to agree on who operates the network, how decisions are made, and how disputes are handled if the blockchain says one thing and a physical audit says another. Organisationally, change management is a hurdle: implementing blockchain may require redesigning business processes. Resistance to change and lack of blockchain expertise among staff can slow down adoption. Stakeholder buy-in is critical; if some key players refuse to participate, the usefulness of the system diminishes. Building trust in the technology itself is also an issue, as some stakeholders might not trust a system which they do not fully understand. Ironically, the very problem blockchain is meant to solve for data can exist as a barrier for its adoption. In summary, beyond the technology, human and institutional factors (policies, laws, skills, and trust between partners) pose significant barriers that must be navigated for successful blockchain implementation in agricultural supply chains. Table 7 summarises the main adoption barriers for blockchain in agricultural supply chains, indicating their severity and frequency of occurrence based on the recent literature and industry reports. Severity reflects the potential impact on adoption if unaddressed, while frequency indicates how commonly the barrier is reported across studies. * Severity: High = major blocker for adoption; medium-high = significant challenge; medium = manageable with mitigation strategies.Frequency: High = Frequently reported in \(>\)70% of reviewed sources; medium = reported in \(\sim\)40-70%; low = reported in \(<\)40%.
## 3 Results and Discussion
### Telekia speciosa Essential Oil Induces Apoptosis in Melanoma Cells  
To investigate the induction of apoptosis in melanoma cells treated with _T. speciosa_ EO, an annexin V/PI double staining assay was performed, followed by fluorescence microscopy (Figure 1A). Representative images of stained cells are presented in Figure 1B. Melanoma cells were treated with a series of EO concentrations (1.5, 3.1, 6.2, 12.5, and 25 \(\upmu\)g/mL) for 24 h. Treatment of A375 cells with 12.5 \(\upmu\)g/mL of EO resulted in an increase in the percentage of early apoptotic cells and late apoptotic cells to \(54\pm 4\%\) and \(33\pm 3\%\), respectively. C32 cells were less affected by the same concentration of EO, and the percentage of early apoptotic cells and late apoptotic cells increased to 13.4 \(\pm\) 2% and 42.0 \(\pm\) 4%, respectively.  
Figure 1: (**A**) Apoptosis rate of A375 and C32 melanoma cells treated with _T. speciosa_ flower EO for 24 h determined by annexin V/PI double staining, followed by fluorescence microscopy. Data are presented as the mean \(\pm\) SD (* \(p\) < 0.05); (**B**) Representative images of stained cells; (**C**) Expression of PARP, caspase-8, caspase-9 and caspase-3 in A375 cells analyzed by western blotting after 24 h of treatment of A375 cells with _T. speciosa_ flower EO.  
The data showed that a decrease in the viability of A375 cells was associated with an increase in apoptosis; therefore, we tried to identify the pathway of apoptosis activated by _T. speciosa_ EO. A375 cells were treated with a series of EO concentrations (1.5, 3.1, 6.2, and 12.5 \(\mathrm{\SIUnitSymbolMicro mL}\)) for 24 h. Western blot analysis (Figure 1C) revealed cleavage of both initiator caspases, caspase-9 and caspase-8 pro-forms, in cells treated with 12.5 \(\mathrm{\SIUnitSymbolMicro mL}\) of EO. It was accompanied by the activation of caspase-3, a key caspase executor, and the cleavage of PARP, a caspase-3 substrate. The results show that _T. speciosa_ EO causes both intrinsic and extrinsic apoptosis in A375 cells.
## 4 Simulation and Experimental Verification
### Experimental Verification
It should be pointed out that in the initial research process of this strategy, the principle of \"MNSC\" was not adopted to help select the zero vector, which led to the equivalent switching frequency increase in the situation of the NPV control strategy not being needed. After adding the principle of \"MNSC\", just as the left part in Equation (9), that is, \([(s^{\prime}_{a}||s^{\prime}_{b})\&\&(s^{\prime}_{b}||s^{\prime}_{c})\&\&(s^{ \prime}_{c}||s^{\prime}_{a})]\), the cover problem can be avoided, and the proposed strategy is optimized and more flexible and efficient. However, up to the present research progress, the proposed strategy still has some limitations. Typically, it can only be applied to the similar three-level converter control occasion in this paper, that is, a power frequency cycle is divided into six equal segments of 60-degree intervals each, and the three-level converter is equivalent to a three-phase two-level converter for control in each 60-degree interval. In the following further research, we will study how to control the NPV directly with the proposed strategy in the three-level situation, not only in the situation where the three-level converter is equivalent to a three-phase two-level converter for control in each 60-degree interval. This can further increase the applicability of the proposed strategy. At the same time, in order to test the performance of the proposed strategy more comprehensively, we will perform more work to compare it with more other strategies or research results. This can be more comprehensive in finding its advantages and disadvantages, so as to further optimize and improve it.
# Grouping Bilinear Pooling for Fine-Grained Image Classification
###### Abstract  
Fine-grained image classification is a challenging computer visual task due to the small interclass variations and large intra-class variations. Extracting expressive feature representation is an effective way to improve the accuracy of fine-grained image classification. Bilinear pooling is a simple and effective high-order feature interaction method. Compared with common pooling methods, bilinear pooling can obtain better feature representation by capturing complex associations between high-order features. However, the dimensions of bilinear representation are often up to hundreds of thousands or even millions. In order to get compact bilinear representation, we propose grouping bilinear pooling (GBP) for fine-grained image classification in this paper. Firstly, by dividing the feature layers into different groups, and then carrying out intra-group bilinear pooling or inter-group bilinear pooling. The representation captured by GBP can achieve the same accuracy with less than 0.4% parameters compared with full bilinear representation when using the same backbone. This extreme compact representation largely overcomes the high redundancy of the full bilinear representation, the computational cost and storage consumption. Besides, it is because GBP compresses the bilinear representation to the extreme that it can be used with more powerful backbones as a plug-and-play module. The effectiveness of GBP is proved by experiments on the widely used fine-grained recognition datasets CUB and Stanford Cars.  
bilinear pooling; fine-grained image classification; compact +
Footnote †: journal: Applied
## 6 Simulation Results
### First Stage: One Day Evaluation
#### 6.1.2 Simple Moving Average (SMA) Method  
Simulation results for the SMA are shown in Figure 12. It is noted that, as the window size is increased, the output PRR is reduced and the SOC variations increase. A summary of the simulation results for the RS method is shown in Table 4. A window size of W = 10 causes half of the degradation compared to W = 20. Furthermore, the variance of PRR\({}_{\text{out}}\) for W = 10 is more than three times less than W = 5.  
Figure 11: Simulation results for the RS method. (**a**) One-day data results showing the total output power (P\({}_{\text{out}}\)), the ESS output power (P\({}_{\text{ess}}\)), the PRR at the output (PRR\({}_{\text{out}}\)), and the state of charge (SOC). (**b**) Zoomed figures of the corresponding black squares.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measurement** & **PRR\({}_{\text{max}}\) = 8\%** & **PRR\({}_{\text{max}}\) = 10\%** & **PRR\({}_{\text{max}}\) = 12\%** \\ \hline D(\%) & 0.0023 & 0.0021 & 0.0014 \\ \hline NC(80\%) & 0.2349 & 0.2097 & 0.1405 \\ \hline Violations (mins) & 0 & 0 & 161 \\ \hline SOC (end of the day)-SOC(ref) & \(-4.5307\) & \(-9.0051\) & \(-3.5554\) \\ \hline \(\sigma^{2}(\text{PRR}_{\text{out}})\) & 22.7312 & 31.9882 & 41.9434 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the simulation results for the RS method.
## 5 Analysis and Result  
### Feature Extraction Using NCA  
For the feature sets A to E, three NCA analyzes were conducted with different functions to identify the most significant features among the 20 data features extracted in Figure 7. NCA was performed using MATLAB, and three functions were used: lgfgs, Minibatch-lbfgs, and sgd. lbfgs is the limited memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) algorithm, and sgd is the stochastic gradient descent (SGD) algorithm, and minibatch-lbfgs is the stochastic gradient descent with LBFGS algorithm applied to mini-batches. Figure 10 shows three NCA results for feature set A. The feature index in Figure 10 is the order of the features extracted in step 3 of Figure 7, and a large feature weight means that the bias of the data is large. As a result of the analysis, it is confirmed that the 4 th, 9 th, 14 th, and 19 th features exhibit the highest bias. These four features are the geometrical moment of area of R real values, R imaginary values, real values, and imaginary values. In other feature sets, the geometrical moment of area showed the greatest bias equally. Therefore, the analysis was performed using features 4 and 9 in each feature set.  
Figure 10: Extraction of principal component using neighborhood component analysis (NCA) for set A: (**a**) lbfgs; (**b**) Minibatch-lbfgs; (**c**) sgd.  
Figure 9: Procedure of the discriminant analysis method from an input sample.  
Figure 11 shows a 2D plot using two main features (moments of R Real values and real values) derived from NCAs from feature sets A through E. PC1 and PC2 are principal components of the first and second according to feature weight. A and C are normal alumina and cristobalite specimens, respectively, and AD and CD are metal damage test specimens of each material. In addition, the red dashed line can be used to check whether the normal and the damaged caps are linearly separated. As a result of analyzing the graph, data in the ranges of 0-4 kHz, 4-7 kHz, and 7-10 kHz are considered to be inappropriate because some defective data is included in the distribution range of the normal data. In the case of 0-10 kHz data, normal and damaged caps can be linearly distinguished, but the range of overlap of cristobalite and alumina materials is wider than that of the 4-10 kHz range in the distribution of normal data. Therefore, the 4-10 kHz range of the feature set C was set as the frequency region of interest, and the graph of Figure 11c was analyzed. As a result, it was confirmed that the normal and bracket damage data were accurately linearly classified based on the \(-0.5\) value, and the normal cristobalite data and the alumina data were partially intersected but mostly distinguishable. However, the 2D plot results obtained via the NCA can be confirmed immediately, but the same process is required to determine additional data, and it is difficult to accurately determine the nonlinear classification. Therefore, it is necessary to develop a distribution area and a judgment model for material and damage so that an immediate judgment can be made using ML techniques.
## 2 Material and Methods
### Multi-Criteria Decision Support System (MCDSS) for Photovoltaic Set Identification  
For the correct functioning of the method to be developed, specific data must be inputted as the calculation basis for determining the customised photovoltaic plant for the installation site. Calculating the energy potential estimate of a region requires integratingsolar irradiation factors and temperature factors to assess system losses. Since temperature interference varies throughout the day, it is necessary to calculate the behaviour of the photovoltaic system hour by hour for a year to apply temperature losses accurately. Therefore, the system's response will be more precise if the information is more detailed. In addition, monthly averages of solar irradiation data provide better detail than just an annual average.  
Based on this context, the multi-criteria decision support system (MCDSS) for photovoltaic set definition was structured in (i) mapped input data, (ii) data pre-processing, (iii) MCDM application, and (iv) output data. Figure 1 presents the MCDSS for photovoltaic set definition architecture.  
* Mapped Input Data (Detail A of Figure 1)--In this section, input data are mapped and collected. These data include crucial information such as climate conditions, installation requirements, and a photovoltaic database. Climate conditions provide insights into solar irradiation patterns and temperature, while installation requirements encompass practical considerations such as available physical space and ideal orientation of solar panels. The photovoltaic database contains details on products and technologies available in the market, essential for comparison and proper equipment selection.
* Data Pre-Processing (Detail B of Figure 1)--Data pre-processing plays a fundamental role in treating and preparing the mapped input data for analysis. This process is divided into sub-steps, including the analysis of available photovoltaic potential, calculation of demanded photovoltaic potential, and evaluation of the feasibility of photovoltaic system installation. These steps help determine the maximum amount of solar energy that can be generated, the system's required capacity to meet electricity demand, and whether installation is viable in each location.
* MCMD Application (Detail C of Figure 1)--The application of multi-criteria decision methods (MCMD) is the heart of the system, where processed data are analysed and used to make decisions. AHP and TOPSIS are applied to determine the best photovoltaic set configuration. Evaluated criteria typically include system efficiency, installation cost, and return on investment time.
* Output Data (Detail D of Figure 1)--The system produces outputs that include specific recommendations for PV sets based on defined criteria. These criteria may include selecting photovoltaic module models, inverters, and other relevant considerations.  
Figure 1: MCDSS for photovoltaic set definition architecture.  
These results are presented clearly and comprehensively, providing users with essential information for making informed decisions about implementing photovoltaic systems.
# An Improved Marker Code Scheme Based on Nucleotide Bases for DNA Data Storage
## 5 Decoding Algorithm
### Low-Complexity INMS  
#### 5.2.1 _Inms_  
In related works, the belief propagation (BP) algorithm was used to correct residual substitution errors after regaining synchronization [10; 11]. However, the BP algorithm has high complexity [18]. To reduce decoding complexity, we propose an improved decoding algorithm for our proposed scheme called INMS.  
Notations in the algorithm development are as follows: \(k\) denotes the length of the information bits, \(n\) denotes code length, \(\sigma\) denotes the correction factor, \(L_{j}\) denotes the initial LLR of the \(j\)-th bit, \(L_{j}^{tot}\) denotes the total LLR, \(I_{\max}\) denotes the maximal number of the iterations, \(M\) denotes the set of all marker bits, \(N_{c}(j)\) denotes the set of all check nodes connected with the \(j\)-th bit, \(N_{v}(i)\) denotes the set of all variable nodes connected with the \(i\)-th check node, \(N_{c}(j)/i\) denotes \(N_{c}(j)\) except for the \(i\)-th check node, and \(N_{v}(i)/j\) denotes \(N_{v}(i)\) except for the \(j\)-th variable node. The detailed procedure of the INMS algorithm is given in Algorithm 1.  
In Algorithm 1, marker bits are fixed; thus, their \(L_{c\to v}\), \(L_{v\to c}\), and \(L^{tot}\) are not calculated because the marker bits are known to the decoder. Therefore, marker bit nodes reduce decoding complexity while providing reliable information to other nodes.  
```
1:Initialize:
2:for\(1\leq j\leq n,i\in N_{c}(j)\)do
3:\(L_{j}=l_{j},L_{v\to c}(i,j)=l_{j}\)
4:endfor
5:for\(iter=1:I_{\max}\)do
6: Stop if \(\xi H^{T}=0\) or \(iter=I_{\max}\)
7: Check node update:
8:for\(1\leq i\leq n-k,j\in N_{v}(i)\)do
9:if\(j\
otin M\)then
10:\(L_{c\to v}(i,j)=\sigma\times\min\limits_{j^{\prime}\in N_{v}(i)/j}|L_{v\to c }(i,j^{\prime})|\times\prod\limits_{j^{\prime}\in N_{v}(i)/j}\text{sgn}(L_{v \to c}(i,j^{\prime}))\)
11:else\(L_{c\to v}(i,j)=0\)
12:endif
13:endfor
14: Variable node update:
15:for\(1\leq j\leq n,i\in N_{c}(j)\)do
16:if\(j\
otin M\)then
17:\(L_{v\to c}(i,j)=L_{j}+\sum\limits_{i^{\prime}\in N_{c}(j)/i}L_{c\to v}(i,j ^{\prime})\)
18:else\(L_{v\to c}(i,j)=L_{j}\)
19:endif
20:endfor
21:Hard decision:
22:for\(j=1:n\)do
23:if\(j\
otin M\)then
24:\(L_{j}^{tot}=L_{j}+\sum\limits_{i\in N_{c}(i)}L_{c\to v}(i,j)\)
25:else\(L_{j}^{tot}=L_{j}\)
26:\(\hat{c}_{j}=\begin{cases}1,\text{ if }L_{j}^{tot}\geq 0\\ 0,\text{ else }\end{cases}\)
27:endif
28:endfor
```  
**Algorithm 1** INMS for the binary LDPC code decoder.