esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Analysis of Observation Space
#### 2.4.2 Single-Agent Observation Dispersion  
To quantify the diversity of observations encountered by each individual agent, a multi-step process was employed:  
1. **Data Loading and Preprocessing**: For each agent, its collected observations over 30 episodes were loaded, and then 5000 frames were subsampled and flattened into vectors.
2. **Dimensionality Reduction and Denoising**: Principal Component Analysis (PCA) was applied to reduce the dimensionality of the flattened observations to 50 dimensions. This step serves to denoise and compress the data, focusing on the directions of highest variance, which can lead to more robust and meaningful clustering in the subsequent step. Working with high-dimensional raw pixel data directly for clustering can be computationally expensive and sensitive to noise; PCA mitigates these issues by capturing the most salient features.
3. **State Space Discretization via Clustering**: The PCA-transformed features from all agents within an environment were pooled together. K-means clustering was then performed on this pooled set of features to define a common set of discrete state categories. To assess the sensitivity of our metrics to the granularity of discretization, we performed this step with K values of 50, 100, and 200. The results for K = 100 are presented in the main analysis, with K = 50 and K = 200 used for comparison.
4. **Occupancy Histogram Generation**: For each agent, an occupancy histogram (probability distribution, \(p(c)\)) over the K clusters was computed.
5. **Dispersion Metrics Calculation**: Based on this probability distribution \(p(c)\) (where \(c\) is a cluster index), the following metrics were calculated:* **Entropy (H(\(p\)))**: Measures the uncertainty or diversity of the visited clusters. Higher entropy indicates that an agent visits a wider range of distinct state categories more uniformly. \[H(p)=-\sum_{c}p(c)\,\log p(c).\] (1)
* **Effective Support Size (\(N_{\mathit{eff}}\))**: Estimates the number of effectively visited clusters, providing an interpretable scale for diversity. \[N_{\mathit{eff}}=exp(H(p)).\] (2)
* **Coverage Ratio (Cov)**: The proportion of the K-defined clusters that were visited at least once. \[\text{Cov }=\,\frac{\big{|}\left\{c\mid p(c)>0\right\}\big{|}}{K}.\] (3)
* **Gini-Simpson Index (G)**: Measures diversity, with values closer to 1 indicating higher diversity (i.e., probabilities are spread out more evenly across many clusters). \[G=1-\sum_{c}p(c)^{2}.\] (4)
## 3 The Assessment Methods of Essential Oils' Antimicrobial Potency  
The antimicrobial activity of essential oils has been previously evaluated by disc diffusion and microbroth dilution assays.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & acyclic & hydrocarbons & o-cymene, \(\beta\)-myrcene, \(\beta\)-ocimene \\  & acyclic & alcohols & geraniol, linalool, nerol \\  & & aldehyde & gerani, citronellal \\ \cline{2-4} Monoterpene & monocyclic & aldehyde & puelgone, perillaldehyde, phellandral, carvenone \\  & monocyclic & alcohols & menthol, limonene, terpinolene \\  & & phenol & carvacrol, thymol, eugenol \\ \cline{2-4}  & bicyclic & caranes & \(\delta\)-carvone \\  & & thujanes & sabinene, \(\alpha\)-thujone \\  & bicyclic & pinenes & \(\alpha\)-pinene, \(\beta\)-pinene \\  & & camphanes & camphene \\  & fenchanes & fenchyl alcohol \\ \hline  & acyclic & farnesanes & farnesane \\  & monocyclic & bisabolanes & zingiberene, \(\beta\)-bisabolene, \(\alpha\)-bisabolol \\  & monocyclic & germacranes & germacrene \\  & & elemanes & elemane \\ \cline{2-4}  & polycyclic & caryophyllenes & caryophyllene, cadinene \\  & & eudesman & chamazulene, \\  & & furanoeudesmas & humulene \\ \hline  & acyclic & phorbasins & subersin \\  & monocyclic & zoapatanols & zoapatanol \\  & polycyclic & labdanes & labdane \\  & & clerodanes & clerodane, cascarillins, teughrebin \\ \hline \hline \end{tabular}
\end{table}
Table 1: Major terpene constituents of essential oils documented in the literature.
## 3 Results
### Effects of the GC Hybrid Implant on Biofilm Formation  
The activation of bacteria is detrimental to implant placement, and the suppression of biofilm formation is crucial for the stable establishment of a titanium implant. Various bacteria are present in the mouth, which rapidly form a biofilm. _S. mutans_ is a representative gram-positive bacterium, and it can stimulate early biofilm formation. To confirm the anti-biofilm formation property of the GC hybrid implant visually, fluorescent nucleic acid staining was used. The live bacteria cells were stained with green fluorescent nucleic acid stain and the dead bacteria cells were stained with red fluorescent nucleic acid stain. As shown in Figure 2a, the substrates of the 1% GC hybrid implant were entirely stained by red fluorescence, whereas the substrates of the chitosan-coated implant were mainly stained by green fluorescence. The results of the biofilm thickness formation assay indicated that biofilm formation was inhibited by graphene with a smaller concentration, and it was reduced by about half that observed on the chitosan-coated implant. In addition, the crystal violet assay demonstrated that the bacteria viability was highly regulated by the concentration of graphene, which increased as the graphene concentration increased (e.g., antibacterial effect: 1% GC \(>\) 3% GC \(>\) 5% GC \(>\) chitosan). To compare the bacteria viability according to the type of substrate and bacteria, the live and dead assay was performed with both _Staphylococcus aureus_ and _S. mutans_ on pure glass and on the titanium substrate (Figure S1). The confocal images showed that the GC-coated substrate could moderately modulate the adherence of bacteria, and the antibacterial properties increased greatly regardless of the type of bacteria and substrates. Interestingly, the GC hybrid glass and titanium implant showed high antibacterial and anti-biofilm formation effects in contrast to pure glass and titanium.  
Figure 1: Characterization of the graphene–chitosan (GC) hybrid implants (1%, 3%, 5% GC) and chitosan (C) hybrid implant. (**a**) Raman spectra, (**b**) water contact angle, and (**c**) roughness measurement. The data were analyzed using one-way analysis of variance (ANOVA) (\(\lx@math@degree\)\(p<0.05\)).  
Figure 2: Effects of graphene–chitosan hybrid implants on bacteria activity. (**a**) Confocal microscopy images of _S. mutans_ on graphene–chitosan hybrid implants (1%, 3%, and 5% GC) and the chitosan implant (C). Green fluorescence indicates viable cells and red fluorescence indicates dead cells (\(n=10\) per group). (**b,c**) Quantification of biofilm thickness and optical density. The data were analyzed using one-way ANOVA (\(\lx@math@degree\)\(p<0.05\)).
## Materials and Methods  
### Bacterial Strains, Culture Conditions, and Preparation of Inoculums  
In the study, _Lactobacillus heleticus_ T80, T105, and B734 were used, derived from traditionally fermented Polish milk products and kindly provided by Prof. [PERSON] (University of Warmia and Mazury in Olsztyn, Poland). The strains were previously analyzed to determine their taxonomic affiliation, genetic and microbiological characteristics, and some biochemical properties, including enzymatic activity [11; 12; 13; 14; 15; 16]. _Lb. helveticus_ DSMZ 20075 (Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH, Braunschweig, Germany) was used as the reference microorganism.  
Cultures of all the tested strains were stored at \(-80\)\({}^{\circ}\)C in a stock solution containing 15% glycerol [17; 18]. Before analysis, the microorganisms were systematically transferred (2% _v/v_) into fresh and sterile Man-Rogosa-Sharpe broth (BTL, Poland) and incubated in anaerobic conditions at 42 \({}^{\circ}\)C for 18 h [18].  
The inoculums of each strain were separately prepared in accordance with the description provided by [PERSON] et al. [19] (with some modifications) and applied in the study to hydrolyze a 1% water solution of whey protein isolate (WPI) with 91.87% protein content (DAVISCO Foods International, Le Seur, MN, USA) and to carry out the fermentation of milk samples 13% regenerated skim milk (RSM) purchased from OSM Krasnystaw, Poland) containing a 1% addition of the tested protein preparations.  
Briefly, samples containing 100 mL of sterile De Man, Rogosa and Sharpe (MRS)broth were inoculated with the individual _Lb. helevticus_ strain cultures (obtained after 18 h anaerobic incubation in MRS broth at 42 \({}^{\circ}\)C) to the optical density (OD) of the bacterial cell solutions at the level of OD\({}_{550}\) = 0.5, which were subsequently incubated (at 42 \({}^{\circ}\)C) to reach the value of OD\({}_{550}\) = 0.8. Thereafter, biomass was collected by centrifugation (8000\(\times\)\(g\), 4 \({}^{\circ}\)C at 15 min). The pellets were washed three times in a sterile saline solution and re-suspended (also in saline) to obtain equal suspensions of each strain exhibiting OD\({}_{550}\) = 0.7.
# Encapsulated Probiotics: Potential Techniques and Coating Materials for Non-Dairy Food Applications
## 5 Application of Probiotics Encapsulation in Non-Dairy-Based Food and Beverage Products
### Fruit and Vegetable-Based
Freeze-dried probiotics were observed to possess higher survivability under storage (4 \({}^{\circ}\)C for 30 days) and simulated gastrointestinal conditions than free probiotics. Probiotics (_L. plantarum_ TISTR 2075) enriched carrot tablets were developed using the fluidized bed drying technique [26]. The results showed that the encapsulated probiotics in the tablets were more resistant to heat and digestion treatments when compared to the free probiotics. Fruit pieces are also potential vehicles to deliver probiotics. In a recent study by [PERSON] et al. [10], _L. salivarius_ was encapsulated in alginate beads through the emulsion technique before adding to mandarin juice. The probiotic-supplemented mandarin juice was then used to incorporate _L. salivarius_ into apple discs. The probiotics-impregnated apple discs were then dried and stored for 30 days. From the study, the encapsulated _L. salivarius_ was found to have higher viability than free cells, indicating that encapsulation had improved the heat resistance properties of the probiotics. The encapsulation also proved to exert stronger resistance onto the probiotics against simulated gastrointestinal conditions. [PERSON] et al. [12] also encapsulated probiotics (_L. plantarum_ 299v) in apple slices. The _L. plantarum_ 299v was coated with carboxymethyl cellulose followed by zein protein, and the coatings were reported to increase the resistance of probiotics under simulated gastrointestinal conditions. In another study, [PERSON] et al. [11] utilized a fluidized bed drying technique to dry and coat apple cubes with a mixture of hydroxyethyl cellulose and polyethylene glycol containing _B. coagulus_. The encapsulation was able to preserve the viability of probiotics in the dried apple snacks throughout the storage period. Nowadays, non-edible parts of fruits have received much attention from researchers due to their abundance of bioactive compounds and promising functional properties. Recently, a powdered premix was developed using grape pomace, pomegranate, beetroot peel extract powders, and _L. casei_ 431 co-encapsulated in quintec seed gum-alginate hydrogel beads. Encapsulation increased the survival rate of _L. casei_ throughout the freeze-drying process, from 42.16 (free cells) to 86.40% (normal encapsulation without the inclusion of prebiotic) and 87.56% (co-encapsulation with prebiotic). Quince seed gum-alginate hydrogel beads showed high encapsulation efficiency of 95.20% and maintained the viability of _L. casei_ for up to 2 months [37].
## 2 Materials and Methods
### Data Processing  
GPP, ET, and meteorological variables were resampled to a spatial resolution of 0.5\({}^{\circ}\) using the nearest-neighbor and grid-average methods, respectively, to match the resolution of the SPEI data. Additionally, to investigate the dominant factors influencing WUE at the annual scale, GPP, ET, and meteorological variables were aggregated to an annual scale. Land cover data were resampled using the mode of the nearest-neighbor vegetation types to represent the land use type for each grid cell, maintaining the same spatial resolution as the SPEI data. Furthermore, to more accurately describe the effects of climate change on vegetation WUE, grid cells where the land use type was bare for over 10 years were excluded, resulting in a final dataset of 2875 valid grid cells. For this research, to calculate the contribution of meteorological variables to vegetation WUE, data from 2001 to 2018 were specifically utilized, while the remaining analyses utilized data from the full 2001-2020 time series (Table 1).
# HSM4 SSL: Leveraging HSMs for Enhanced Intra-Domain Security +
## 3 HSM4 SSL Architecture
### HSM4 SSL: Low-Level Architecture
#### 3.3.2. HSM4 SSL Management Layer
* Certificate Manager: Manages the validation and revocation of digital certificates used for secure communication and authentication purposes. It maintains a repository of trusted root certificates and intermediate certificates, enabling the validation of certificates within the system. The certificate manager also handles the revocation of certificates in case of compromised or expired certificates.
## 4 Experiment Setup
### Metrics  
This paper will use Sensitivity, Specificity, F-measure and CER as metrics to judge the speech recognition performance. To help with the analysis, we define the following symbols:  
\[\begin{array}{c}\text{C}=\text{Number of correctly recognized characters}\\ \text{S}=\text{Number of substitutions}\\ \text{I}=\text{Number of insertions}\\ \text{D}=\text{Number of Deletions}\end{array} \tag{7}\]  
1. CER  
The CER (character error rate) is a metrics that compares the character sequence that is obtained by the model with the actually transcribed character sequence. In this paper, the recognition performance of the model is measured by the character error rate calculated by the edit distance. The edit distance is a quantitative measure of the degree of difference between two strings. The measurement method refers to how many edit operations are required to change one string to another. Allowed editing operations include replacing one character with another, inserting one character, and deleting one character. The CER calculation formula is as follows:  
\[\text{CER}=\frac{S+I+D}{C+S+I+D} \tag{8}\]  
2. F-measure  
F-measure is a weighted harmonic average of precision and recall. It is a comprehensive evaluation indicator for evaluating the performance of the model. In speech recognition, the precision rate is the ratio of the number of correctly recognized characters to the sum of the number of correctly recognized, replaced, and inserted characters. The recall rate is the ratio of the number of correctly recognized characters to the sum of the number of correctly recognized, replaced, and deleted characters. We use the following formula to calculate the precision rate, recall rate, and F-Measure:  
\[\begin{array}{c}\text{Precision}=\frac{C}{C+S+I}\\ \text{Recall}=\frac{C+S+D}{C+S+D}\\ \text{F-Measure}=\frac{\frac{a^{2}+1}{\text{Precision}}+\frac{a^{2}}{\text{ Recall}}}{\frac{1}{\text{Precision}}+\frac{a^{2}}{\text{Recall}}}\end{array} \tag{9}\]  
When a = 1, it is the most commonly used F1. In this paper, F1 is used as a comprehensive indicator of the precision and recall rate to judge the speech recognition performance  
\[\text{F1}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{10}\]  
3. Sensitivity  
Sensitivity refers to the true positive rate, which is similar to the recall rate. Sensitivity can be used to measure the ability to identify the correct character in a speech recognition task. Sensitivity is the ratio of the number of correctly recognized characters to the sum of the number of correctly recognized, replaced, and deleted characters. It can be defined by the following formula:  
\[\text{Sensitivity}=\frac{C}{C+S+D} \tag{11}\]  
4. SpecificitySpecifics refers to the true negative rate. It can be defined by the following formula:  
\[\text{Specificity}=\frac{C+D-I}{C+S+D} \tag{12}\]
# Fine-Grained Sentiment-Controlled Text Generation Approach Based on Pre-Trained Language Model
## 3 Method
### Loss Functions  
**Generation loss function:** through an LM training objective, we train our conditional generative model with the general generating loss term conditioned on previous \(x_{t-1}\) and input sentiment information \(s\):  
\[\mathcal{L}_{G}=-\sum_{t}log[p(x_{t}{}^{\prime}|s,x_{t-1})]_{I^{r}(x_{t})} \tag{2}\]  
where \(x_{t}{}^{\prime}\) is the predicted token at time step \(t\). \(I^{x}(\cdot)\) is the index function of a vector.  
**Sentiment control loss function:** To encourage the generator to output texts incorporating the input sentiment information (phrases), we train the generator additional with our proposed sentiment-control loss function. The main idea of this loss function is to maximize the probability value of the one with the highest probability in terms of given aspect/opinion word from all the next-word predictions of a sentence. Specifically, for  
Figure 5: Strategy of the query-hint mechanism, this illustration demonstrates two different instances of query-hint strategy, i.e., \({}^{\prime}\)1 to 1\({}^{\prime}\) and “1 to \(n\),” which correspond to the one-to-one and one-to-many situations for aspect-opinion pairs, respectively.  
every aspect phrase \(a\) and opinion phrase \(o\) presented in the source text, the training loss is defined as:  
\[\begin{split}&\mathcal{L}_{Senti}=\mathcal{L}_{a}+\mathcal{L}_{o}\\ &\mathcal{L}_{a}=-\sum_{a}\sum_{t}log[Q(\mathcal{X}^{\prime},Mask _{a,t})]_{\Gamma^{x}(x_{a,t})}\\ &\mathcal{L}_{o}=-\sum_{o}\sum_{t}log[Q(\mathcal{X}^{\prime},Mask _{o,t})]_{\Gamma^{x}(x_{a,t})}\\ & Q(x,Mask)=Mask\odot p_{max}(x)+(1\oplus Mask)\times\phi_{ mean}\\ & p_{max}(x)=MaxPooling([p(x_{1}|s,x_{0});p(x_{2}|s,x_{:1});\cdots;p(x_{t}|s,x_{:t-1})])\end{split} \tag{3}\]  
where \(\mathcal{L}_{a}\) and \(\mathcal{L}_{o}\) are the losses for aspect and opinion term inclusion, respectively. \(Mask_{a,t/o,t}\) is a one-hot vector with the size of \(\mathcal{V}\) (vocabulary size), and only the element in the index of \(a_{t}/o_{t}\) is 1. \(\phi_{mean}\) is a hyper-parameter controlling how much the prediction of aspect/opinion terms should be enhanced. \(p_{max}(\cdot)\) is a max-pooling operation with a kernel size of \(l_{t}\times 1\) (\(l_{t}\) is the length of the target text). \(\odot\) and \(\oplus\) represent the element-wise product and XOR, respectively.  
As a result, our final loss function comprehensively considers the loss of generation quality and the loss of sentiment control:  
\[\mathcal{L}_{total}=\lambda_{G}\mathcal{L}_{G}+\lambda_{Senti}\mathcal{L}_{Senti} \tag{4}\]  
where \(\lambda\) values are hyper-parameters controlling how much the loss terms dominate the training.
## 3 Data and Methods
### MODIS Products Used in This Study
#### 3.1.2 MOD13C2 NDVI/EVI  
The Vegetation Index (VI) has become a common tool for assessing the different aspects of plant processes while simultaneously determining spatial variations in vegetation cover. The VI utilizes the differential absorption properties of leaves in different spectrum ranges to measure the amount and health of green biomass [30]. Two types of MODIS VIs, the NDVI and the Enhanced Vegetation Index (EVI), are globally produced over land at 1-km resolutions at 16-day compositing periods. Whereas the NDVI is chlorophyll sensitive, the EVI is more responsive to canopy structural variations, including canopy type, plant physiognomy and canopy architecture [49].  
The NDVI has proven to be a valuable tool in researching large-scale changes in plant or ecosystem processes associated with global change. However, the actual range of the NDVI varies depending on the instrument used, background reflectance and canopy structure. Beyond a certain canopy density, increasing amounts of green biomass make little difference in the NDVI [50]. The EVI is more functional for Near Infrared (NIR) reflectance than for red absorption; therefore, it does not become saturated as rapidly as the NDVI in dense vegetation. Additionally, the EVI has been shown to be highly correlated with photosynthesis and plant transpiration [51]. The equations used in the NDVI and EVI are shown below (Equations 1 and 2):  
\[NDVI=\frac{\rho_{air}-\rho_{red}}{\rho_{air}+\rho_{red}} \tag{1}\]  
\[EVI=2.5\frac{\rho_{air}-\rho_{red}}{\rho_{air}+\left(6\rho_{red}-7.5\rho_{blue} \right)+1} \tag{2}\]  
where \(\rho_{\rm{mir}}\), \(\rho_{\rm{red}}\), and \(\rho_{\rm{blue}}\) are the spectral reflectance in MODIS bands 2, 1 and 3, respectively [49,52].  
The NDVI accuracy is within \(\pm 0.025\), while that of the EVI is within \(\pm 0.015\) and the accuracy of retrieving VI for a good quality day would be to within \(\pm 0.020\) for NDVI and \(\pm 0.010\) for EVI [53]. MODIS/Terra Vegetation Indices Monthly L3 Version 005 with a spatial resolution of 0.05 degrees (MOD13C2) were used in this study.