esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions
The findings demonstrate the potential of LLMs, particularly when fine-tuned with targeted data, to advance OE and serve as indispensable tools for knowledge representation in specialized fieldssuch as wildfire SAR operations. These insights pave the way for future studies to refine fine-tuning methodologies and extend the applicability of LLMs across diverse domains. While this study demonstrates the feasibility of fine-tuning LLMs for ontology engineering in SAR contexts, an important next step is the integration of these ontologies into operational decision support systems. In practical applications, ontology-driven reasoning can be embedded into SAR platforms to enhance incident classification, automate information retrieval, and support knowledge-based decision-making. For example, ontology-enhanced knowledge graphs could improve real-time situational awareness by dynamically linking incident reports, geographical data, and response protocols to assist emergency teams. Future improvements could involve incorporating retrieval-augmented generation (RAG) strategies and active learning approaches to continuously refine the model based on real-time domain expert feedback. This would allow the system to adapt to new terminologies, evolving threat landscapes, and emerging response protocols in SAR environments. Such an approach could ensure that the model remains aligned with the operational needs of first responders while improving its generalization capabilities across different emergency scenarios. Furthermore, we recognize the need for additional details regarding data preparation steps, computational efficiency, and real-world deployment scenarios. Future research could explore a fully operational prototype of an ontology-powered SAR assistant, systematically evaluating scalability, response accuracy, and integration challenges in real-world settings. These enhancements would further solidify the role of LLMs in ontology-driven decision support at scale. The experiments detailed (source files, screenshots, ttl files and chat conversations) are saved in the following GitHub repository: [[https://github.com/dimitrisdoumanas19/Fine-tuning-LLMS.git](https://github.com/dimitrisdoumanas19/Fine-tuning-LLMS.git)]([https://github.com/dimitrisdoumanas19/Fine-tuning-LLMS.git](https://github.com/dimitrisdoumanas19/Fine-tuning-LLMS.git)) (accessed on 7 December 2024). Conceptualization, D.D., A.S. and K.K. ; Methodology, D.D., A.S. and K.K. ; Writing--original draft, D.D. ; Writing--review & editing, D.D., D.S., C.V. and K.K. ; Supervision, D.S., C.V. and K.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data presented in this study are openly available in [[https://github.com/dimitrisdoumanas19/Fine-tuning-LLMS.git](https://github.com/dimitrisdoumanas19/Fine-tuning-LLMS.git)]([https://github.com/dimitrisdoumanas19/Fine-tuning-LLMS.git](https://github.com/dimitrisdoumanas19/Fine-tuning-LLMS.git)), accessed on 7 December 2024. The authors declare no conflict of interest.
## 3 Results and Discussion
### Static Fermentation of Whole Milk in Consecutive 24-h Batch Subcultures
Considering that milk kefir grains contain bacteriocin-producing LAB strains (e.g., _Lactococcus_ and _Lactobacillus_ species) [10; 18; 23; 24], it could be logical to suppose that the increase in the TAA levels could be also related to the production of bacteriocins by these strains. The decline in TAA in the four subcultures (Figure 11) could be linked to a reduction in the metabolic activity of the microorganisms present in the kefir grains [10]. This is evidenced by a decrease in the consumption of nutrients (L, TN, and Pr) [18], as well as a decrease in the production of lactic acid, acetic acid, ethanol, and glycerol, and possibly in bacteriocin synthesis. The drained kefir grain weight (KGW) increased (although not significantly, \(p\) > 0.05) from 12.24 \(\pm\) 0.44 to 12.44 \(\pm\) 0.90, 12.81 \(\pm\) 0.91, 13.24 \(\pm\) 0.83, and 13.50 \(\pm\) 1.04 g in the first, second, third, and fourth subculture (Figure 1J). The growth of the kefir grains is commonly associated with an increase in the microbial population (bacteria and yeasts) anchored to the grains and the production of kefiran, an exopolysaccharideachatride produced by _Lb. kefranofaciens_, _Lb. kefranutum_, _Lb. parkefir_, _Lb. kefir_, and _Lb. delbrueckii_ subsp. _bulgaricus_[10; 25]. The high final counts of LAB (8.66 \(\pm\) 0.06, 8.96 \(\pm\) 0.08, 8.84 \(\pm\) 0.02, and 9.14 \(\pm\) 0.08 log CFU/mL), AAB (8.89 \(\pm\) 0.02, 8.64 \(\pm\) 0.05, 8.95 \(\pm\) 0.03, and 9.13 \(\pm\) 0.06 log CFU/mL), and yeasts (6.30 \(\pm\) 0.09, 6.21 \(\pm\) 0.03, 7.30 \(\pm\) 0.05, and 6.29 \(\pm\) 0.04 log CFU/mL) in each subculture are also noteworthy. These counts significantly exceeded the threshold level of 6.0 log CFUs of probiotic cells per mL or gram needed to observe beneficial effects on the host [26; 27]. The counts of Enterobacteriaceae and _Pseudomonas_ were below the minimum detection limit (counts were performed only for plates containing 30-300 colonies), indicating that the four beverages have good hygienic conditions. This is an important consideration in the production of fermented beverages, as contamination with undesirable bacteria could affect consumer health. The statistically higher (\(p\) < 0.05) LAB and AAB counts, compared to those of yeasts, could be related to the initial pH of the milk (6.69 \(\pm\) 0.01), which was probably more favorable for the growth of the two bacterial populations [28; 29] than for the yeast population [30; 31]. Considering that the UHT whole milk is an ultra-pasteurized product, packaged in a sterilized container, the initial concentration of microorganisms is considerably low in this substrate. Therefore, the final concentrations of LAB, AAB, and yeasts in the fermented beverages at the end of each subculture solely comprise the microbiota of the milk kefir grains. This indicates that a probiotic-enriched beverage can be obtained by fermenting UHT whole milk with milk kefir grains.
## 3 Results
### Effects of BenPros on COX-2 and Bcl-2 Proteins in BPH Rats  
Chronic inflammation in BPH induces the overexpression of COX-2, which increases the expression of Bcl-2. As shown in Figure 4, the BPH group exhibited a marked increase in COX-2 protein expression compared with the NC group, whereas the finasteride and BenPros 400 mg/kg groups showed a significant decrease in pro-inflammatory COX-2 levels.  
Figure 3: Effect of BenPros on DHT and PSA in BPH rats. (**A**) The serum concentrations of DHT. (**B**) The serum concentrations of PSA. (**C**) Representative western blots of PSA. \(\beta\)-actin was used as an internal control. Blot is normalized to \(\beta\)-actin. Values are presented as the mean \(\pm\) standard error of the mean of 6 rats per group. * \(p<0.05\), ** \(p<0.01\), *** \(p<0.001\) vs. NC; # \(p<0.05\), #\(p<0.01\) and ### \(p<0.001\) vs. BPH. Fina: finasteride, Saw: saw palmetto.  
The BPH group showed an upregulation in the level of Bcl-2, whereas the finasteride and BenPros-treated groups revealed a significant inhibition in the Bcl-2 protein. Although Bax expression in all groups was not changed, finasteride and BenPros 400 mg/kg groups had a markedly decreased Bcl-2/Bax ratio. These results suggest that that the increase in prostate epithelial tissue is regulated by apoptosis inhibitory/inducing protein, and BenPros administration reduced prostate overgrowth by reducing Bcl-2/Bax ratio.
## 2 Materials and Methods
### Analysis
\(PAR_{\text{diff,u}}\) is the diffuse \(PAR\) under the canopy and was calculated according to [61], \((PAR_{\text{diff}}-PAR_{\text{diff,u}})/LAI\), which represents the diffuse \(PAR\) per unit leaf area within the canopy; C quantifies the contribution of multiple scattering of the total \(PAR\) to the diffuse irradiance per unit leaf area within the canopy; \(\beta\) is the mean leaf-sun angle and was set at \(60^{\circ}\) for a canopy with a spherical leaf-angle distribution [61]; and \(\theta\) is the solar zenith angle. More detailed information about the SL-LUE model and the TL-LUE model is in [5]. \(LAI_{\text{su}}\) and \(LAI_{\text{sh}}\) are the \(LAI\) of sunlit and shaded leaves. They are computed as [28; 61]:  
\[LAI_{\text{su}}=2\times\cos\theta\times(1-\exp(-0.5\times\Omega\times LAI/\cos \theta)), \tag{13}\]  
\[LAI_{\text{sh}}=\max(0,LAI-LAI_{\text{su}}), \tag{14}\]  
where \(\theta\) is the solar zenith angle; \(LAI\) is the leaf area index; \(\Omega\) represents the vegetation clumping index according to different vegetation types and was set to 0.9 for cropland in this study [62]. The key parameters of the three models (\(\varepsilon/SIF_{\text{yield}}\), \(\varepsilon_{\text{max}}\), \(\varepsilon_{\text{msu}}\), and \(\varepsilon_{\text{msh}}\)) were optimized by applying a Bayesian approach that combines the Bayesian theory with the Markov chain Monte Carlo (MCMC) estimator using the 2015 training dataset. Additionally, to evaluate the extent to which the model is affected by the integrated meteorological conditions independent of temporal aggregation, we calculated the meteorological condition index (ECI) by combining \(APAR\), air temperature, and \(VPD\)[3]. We first took the normalized transformation of the three variables based on the maximum-minimum observations throughout the season. A minimum that accounted for 95% of the minimum observation was used for each variable to get a positive transformed value of the observed minimum. Taking \(APAR\) as an example, the scaled \(APAR\) at the time that OCO-2 passes over can be expressed as follows:  
\[S^{i}_{APAR}=\frac{APAR^{i}-APAR_{\text{min}}}{APAR_{\text{max}}-APAR_{\text{ min}}}, \tag{15}\]  
where \(APAR\) denotes the APAR observations when OCO-2 overpasses from the mid-day timescale to the monthly timescale (corresponding to the superscript \"i\"), and \(APAR_{\text{min}}\) and \(APAR_{\text{max}}\) are the minimum and maximum \(APAR\) acquired during the transit of OCO-2 throughout the season's different temporal aggregation levels, respectively. In [3], it is suggested that whole canopies are prone to experiencing exposure to excess light when the ECI is greater than 0.8 compared with when the ECI is lower. Therefore, we used 0.8 as a threshold value at which the ECI values were fitted to these models separately. Finally, GPP estimations derived from the 2016 testing dataset were used to validate the SIF-based model with that of the LUE-based models for mid-day, daily, and monthly timescales, respectively.
## 2 Analytical Solution
### Absorptivity of IN718  
Inconel 718 (hereinafter referred to as IN718) alloy is a precipitation hardening nickel-based zinc oxide alloy rich in Cr and Fe elements, which is known for its high tensile strength, fatigue strength and fracture strength at high temperature [22]. Due to its excellent performance, it can be widely used in various high-demand occasions, such as engine turbine blades, aerospace structural parts and chemical equipment [23].  
Because SLM is a thermally driven process, an accurate laser-material absorptivity is required for predictive modeling. From the existing literature, we can find that the reported absorptivity of In718 has a large difference, with a value between 0.3 and 0.87. In order to simplify the calculation, many mathematical models approximated absorptivity as a constant [24]. However, the latest research found that absorptivity is related to the morphology and temperature of the melt pool. The approximation of the temperature-dependent absorptivity can be derived [25; 26], which is the function of the laser wavelength and the electrical resistivity.  
\[A\approx 0.365\left(\frac{\rho_{et}}{\lambda}\right)^{\frac{1}{2}}-0.0667\left( \frac{\rho_{et}}{\lambda}\right)+0.006\left(\frac{\rho_{et}}{\lambda}\right)^ {\frac{3}{2}} \tag{5}\]  
where \(\rho_{et}\) is electrical resistivity of In718. The electrical resistivity is temperature dependent, the relation between electrical resistivity and temperature is as following, which is shown in Figure 1. \(\lambda\) is the wavelength. Here, \(\lambda=1080\) nm is assumed.  
\[\rho_{et}=\left\{\begin{array}{ll}1.105\cdot 10^{-6}+2.617\cdot 10^{-10}\cdot T,&T<1123K\\ 1.341\cdot 10^{-6}+5.182\cdot 10^{-11}\cdot T,&1123K<T\leq T_{s}\\ 4.511\cdot 10^{-7}+6.341\cdot 10^{-10}\cdot T,&T_{s}<T\leq T_{l}\\ 1.260\cdot 10^{-6}+1.317\cdot 10^{-10}\cdot T,&T_{l}<T\end{array}\right. \tag{6}\]  
[PERSON] [17] found that the absorptivity was affected by laser power, scanning speed, powder layer thickness and laser beam radius. They demonstrated that absorptivity can be presented as universal functions of the normalized enthalpy and thermal diffusion length. In the transition and keyhole regimes, it can be expressed as the following exponential function:  
\[A_{e}=0.70(1-e^{-0.66\beta_{Am}L_{in}^{*}}) \tag{7}\]where \(A_{e}\) is effective absorptivity, \(\beta_{Am}\) is normalized enthalpy that is defined as the ratio of the absorbed laser energy density to the volumetric melting enthalpy, \(L_{th}^{*}\) is normalized thermal diffusion length.  
\[\beta_{Am}=\frac{A_{m}P}{\pi H_{m}\sqrt{\alpha uu^{3}}} \tag{8}\]  
\[L_{th}^{*}=\big{(}\sqrt{\alpha a/u}\big{)}/a \tag{9}\]  
where \(A_{m}\) is the minimum absorptivity, \(H_{m}\) equals to \(\rho C_{p}T_{m}\).
## 3 Methodology
### Post-Processing
#### 3.4.2 Addressing Missing PS Points and Noise  
PSCs are critical for accurate PSInSAR analyses; however, the inconsistency in stacks makes it challenging. Fluctuations in the AS-Index can cause some PS points to appear and disappear intermittently. This variability poses a significant challenge in maintaining a reliable dataset for VUG analysis.  
This phenomenon is illustrated in Figure 6, which shows the range of AS-Index values (from 0.80 to 0.94) across different stacks. These fluctuations highlight the reason behind the fact that only a fraction of the points --41,000 out of a potential five million--is consistently detectable across all the COSMO-SkyMed SAR stacks. This represents a significant reduction in data points that can be reliably used for height measurement and analysis.  
To manage noise within the time series data derived from the merged PS points aligned by their consistent SAR coordinates, we address potential artifacts in the height estimation for individual points over time. Specifically, we identify and replace spurious height estimates where the difference between consecutive stack measurements for the same aligned PS point exceeds 3 m. This threshold was directly informed by our high-resolution drone survey data, which indicated that the typical floor height in modern Wuhan constructions is approximately 3 m. Using this physically meaningful value helps filter out large, unrealistic jumps in the estimated height of presumably stable scatterers. Such jumps are considered more likely attributable to processing noise (e.g., phase unwrapping errors, residual atmospheric effects specific to a stack) rather than actual changes in the physical scatterer's height. While this stack-processing approach for VUG estimation is novel, this filtering step based on observed floor height provides a practical method for cleaning the individual PS time series before assessing overall growth trends.  
Subsequently, to tackle incomplete data series (i.e., series where a PS point, which was initially detected, is missing in some later stacks), we fill the missing height values using the median height calculated from the available data for that specific PS point. This step is performed carefully to ensure that filling only occurs for stacks processed after the beginning of the data.  
Figure 6: AS-Index fluctuations across multiple COSMO-SkyMed SAR stacks in Wuhan.  
the initial detection of each PS point. For instance, if a PS point first appears in the 2016 stack, any missing values in subsequent stacks are filled based on its available height data from 2016 onwards. These procedures--noise filtering based on the 3 m threshold and gap filling--aim to improve the continuity and reliability of the height information associated with each individual PS point, providing a cleaner dataset for subsequent analysis of temporal trends and spatial aggregation for VUG assessment.
# A Forwarding Latency Optimization Method for Software Data Plane Based on Spin-Polling
###### Abstract  
Modern software data planes use spin-polling and batch processing mechanisms to significantly improve maximum throughput and forwarding latency. The user-level IO queue-based spin polling mechanism has a higher response speed than the traditional interrupt mechanism. The batch mechanism enables the software data plane to achieve higher throughput by amortizing the IO overhead over multiple packets. However, the software data plane under the spin-polling mechanism keeps running at full speed regardless of the input traffic rate, resulting in significant performance waste. At the same time, we find that the batch processing mechanism does not cope well with different input traffic, mainly reflected in the forwarding latency. The purpose of this paper is to optimize the forwarding latency by leveraging the wasted performance. We propose a forwarding latency optimization scheme for the software data plane based on the spin polling mechanism in this paper. First, we calculate the CPU utilization of the software data plane according to the number of cycles the CPU spends on the valuable task. Then, our scheme controls the Tx queues and dynamically adjusts the output batch size based on the CPU utilization to optimize the forwarding latency of the software data plane. Compared with the original software data plane, the evaluation result shows that the forwarding latency can be reduced by 3.56% to 45% (in a single queue evaluation) and 4.35% to 55.54% (in a multiple queue evaluation).  
software data planes; forwarding latency; spin-polling; batch processing mechanism +
Footnote †: journal: Applied Sciences  
[PERSON], [PERSON], [PERSON], L. A Forwarding Latency Optimization Method for Software Data Plane Based on Spin-Polling  
20221127858103390/app12178758
## 4 Tests Results and Discussion
According to Figure 12, the highest \(V_{S}\) values can be establish using zero crossing technique, next frequency domain and cross correlation, but only for higher stresses. This finding is not in concordance with the results for \(p^{\prime}=45\) kPa, where the highest \(V_{S}\) give peak-to-peak methods. However, the difference between the various methods of BEs results interpretation is at the level of from 1% to the maximum 3%. As it can be seen in Figure 12, the highest \(V_{S}\) values for RCA with rubber chips at \(p^{\prime}=45\) and 90 kPa concerns frequency domain method, whereas at \(p^{\prime}=180\) kPa--peak-to-peak method. On the other hand, cross correlation method produces the lowest \(V_{S}\) values, in spite of the stress level. Moreover,  
Figure 12: Comparison between selected \(V_{S}\) values of pure RCA specimens and RCA with rubber chips compositions by four/five different methods of interpretation.
## 5 Hybrid Protocols  
Hybrid protocols combine the advantages of tree-based and Aloha-based protocols to avoid their problems and provide better features in tag identification [61; 62; 63; 64]. Most of them first implement a tree-based procedure and tag estimation procedure in order to predict the number of tags. Therefore, the combined Aloha-based and tree-based protocol procedures are known for their high  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Protocol Name** & **OQTT** & **BS** & **CT** & **CwT** \\ \hline \multirow{6}{*}{**Protocol feature**} & The reader performs an initial estimation to split the tags in groups. These groups are identified using the CT protocol. & The reader transmits a serial number. Only tags having an equal or lower ID value than the received serial number will & It is an improvement of QT which uses Bit tracking technology in order to find which bits collided the new tag \\ \cline{1-1} \cline{2-5}  & The reader performs an initial estimation to split the tags in groups. These groups are identified using the CT protocol. & The reader transmits a serial number. Only tags having an equal or lower ID value than the received serial number will & It wastes a high number of tag bits on every collision, increasing the energy consumed by the reader and the tags responses. \\ \cline{1-1} \cline{2-5}  & Very complex protocol. Hard to be physically implemented. & The reader restarts the reading process after a tag is identified. & It wastes a high number of tag bits on every collision, increasing the energy consumed by the reader during the process. \\ \cline{1-1} \cline{2-5}  & 61.4\% & 30\% & 50\% & 35\% \\ \hline \multirow{2}{*}{**Disadvantages**} & Very expensive & Low & Medium & Medium \\ \cline{1-1} \cline{2-5}  & Very high & Low & Medium & Medium \\ \hline \hline \end{tabular}
\end{table}
Table 5: A comparison of tree-based protocols using bit-tracking.  
Figure 20: Flow chart of CwT protocol: (**a**) for reader; (**b**) for tags.  
complexity and hardware demands. This kind of protocol can significantly increase performance as compared to the previous ones.  
Recent proposals include the Tree Slotted Aloha (TSA) and Binary Tree Slotted Aloha (BTSA). TSA uses a tree structure, and the tag's responses are organized in slots, as in FSA. In the BTSA protocol, tags randomly choose a slot after the reader query.
## 4 Review of Mask-Face Detection Methodologies
### Classification  
In some studies, the input images were preprocessed with methods such as enlarging, cropping, etc., to maintain only the focused areas for classification. They only focused on classification architectures, leaving out the detection component.  
In order to improve the accuracy of mask-face classification, [PERSON] et al. [48] proposed a new idea to focused on the parts of non-covered faces and obtained the best aspects of the collection areas through CNN.They made a comparison among different classification models of SSDMNV2, LeNet-5, AlexNet [30], VGG-16 [44] and ResNet-50 [28]. As a result, their system achieved 98.7% accuracy.  
In [46], [PERSON] et al. first performed data augmentation on the following three datasets: Face Mask Detection (FMD) [21], Face Mask (FM) [55], and Real-World Mask Face Recognition (RMRF) by implementing flipping, rotation, etc., to expand the size of training data. After that, they proposed a model based on the MobileNetV2 [32] architecture, followed by autoencoders, which were used to transform the high-dimensional output feature vector into low dimensions. Finally, they performed ablation studies with their proposed model with VGG16, EfficientNet [56] and other well-known CNN models. Their model achieved a precision of 99.696%, recall of 99.97%, F1-score of 99.97%, and accuracy of 99.98%. Furthermore, the light-weighted architecture of the proposed model achieved 199.01 FPS over GPU, 44.06 FPS over CPU, and 18.07 FPS over the Raspberry Pi resource-constrained device.  
Besides the two-class mask-face detection methods, some researchers paid attention to the issue of incorrect mask-wearing, which plays a critical role in the protection of infection during the epidemic. Therefore, in the next section, we summarize studies on mask-face detection models of three-class, which includes wearing mask, without mask, and incorrect mask-wearing.