esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modeling and Parameter Adjustment
### Domain and Mesh
The mesh sensitivity analysis demonstrated that the 0.05 m cell size provided sufficient accuracy in resolving fire-induced flows and smoke behavior, without incurring excessive computational cost. Therefore, this resolution was adopted for the final simulations [10]. In the present study, a fine mesh with a cell size of 0.05 m was applied in the fire source area, air supply openings, and specific air leakage surfaces, while a coarse mesh with a cell size of 0.25 m was used for the remainder of the domain. The fire source subdomain contains 192,000 cells; the upper air supply subdomain contains 144,000 cells; the lower air supply subdomain contains 72,000 cells; and the combined infiltration subdomains consist of 128,250 cells. Considering the total cell count of 1,198,008, the coarse mesh accounts for 661,758 cells.
# Pollen-Food Allergy Syndrome: Allergens, Clinical Insights, Diagnostic and Therapeutic Challenges
## 7 PFAS Diagnosis
### Basophil Activation Test (BAT)

Table 3: Comparison of ImmunoCAP, ISAC, and ALEX [98; 119; 138; 190; 202; 205]. activated and express markers such as CD63 and CD203c. The degree of basophil activation is quantified using flow cytometry, providing a clear indication of whether the allergen triggers a reaction. BAT can be used with both whole allergen extracts and purified molecular components, making it a versatile tool for assessing allergen-specific reactivity. BAT is particularly valuable in conditions like PFAS, where distinguishing between primary food allergies and cross-reactivity caused by pollens is essential [207]. BAT is particularly useful for differentiating cross-reactivity in PFAS from genuine primary food allergies [207]. For example, basophil activation in response to PR-10 proteins like Bet v 1 (birch pollen) may explain cross-reactivity with apple (Mal d 1), while no activation to primary food allergens indicates a lower likelihood of systemic reactions. BAT also provides insights into the potential severity of allergic reactions. Strong basophil activation in response to stable allergens like LTPs (e.g., Pru p 3 in peach) correlates with a higher risk of severe systemic reactions, while weaker activation to labile proteins like PR-10 typically reflects mild, localized symptoms [206]. By testing multiple allergens simultaneously, BAT can also map patterns of cross-reactivity in PFAS. For example, a patient sensitized to grass profilins (Phl p 12) may show basophil activation to profilins in foods like melon or tomato, supporting a diagnosis of PFAS. BAT offers greater specificity and higher negative predictive values compared to prick tests and specific IgE measurements, without compromising sensitivity or positive predictive values [194,207]. Therefore, BAT results can guide tailored dietary recommendations. For instance, a patient with significant basophil activation to raw apple but none to cooked apple may safely consume the cooked form, as heat denatures PR-10 proteins. Despite its utility, BAT has several limitations that restrict its widespread use [208]. It requires specialized equipment, such as flow cytometers, and trained personnel, making it technically complex and less accessible than standard tests like ImmunoCAP or skin prick testing. Moreover, approximately 10% to 15% of individuals are \"nonresponders\", meaning their basophils respond to non-IgE-mediated stimulants but not to IgE-mediated controls or allergens, rendering the test results uninterpretable [209]. The cost of BAT is also relatively high, which may limit its availability in routine clinical practice. Furthermore, BAT depends on fresh blood samples, as basophils are short-lived, presenting logistical challenges in sample collection and processing [210]. Another significant limitation is the lack of full standardization across laboratories, with varying protocols and thresholds, which can lead to inconsistent results and complicate interpretation [208].
## 2 Theoretical Background
### The Use of a Non-Linear Complementary Filter for Estimation of Headwind Gists  
The general structure of an NCF (Figure 4) consists of low- and high-frequency inputs of the composite signal.  
Figure 4: Non-linear complementary filter diagram.  
Figure 3: Bode diagram (amplitude and phase) for linear CF.  
The non-linear CF (NCF) is based on the proportional-integral controller, in which the proportional part manages the low-frequency input and the integral part handles the high-frequency signal. \(K_{p}\) and \(K_{i}\) indicate the proportional and integral gain, respectively [27]. The non-linear complementary filter transfer function is represented by Equation (14). The amplitude and phase shift of the NCF is presented in Figure 5.  
\[EST=\frac{Kp\cdot s+Ki}{s^{2}+Kp\cdot s+Ki}.LF+\frac{s^{2}}{s^{2}+Kp\cdot s+Ki} \cdot HF \tag{17}\]  
Taking into consideration dependencies (1), (2), (12), (13) and (17), the complete law of headwind estimation (including temporary gusts) can be formulated as (18), and finally, as Equation (19).  
\[U_{g\text{-}est\_NCF}=\frac{Kp\cdot s+Ki}{s^{2}+Kp\cdot s+Ki}\cdot U_{g\text{-} lo}+\frac{s^{2}}{s^{2}+Kp\cdot s+Ki}\cdot u_{g\text{-}hi} \tag{18}\]  
\[U_{g\text{-}est\_NCF}=\frac{Kp\cdot s+Ki}{s^{2}+Kp\cdot s+Ki}\cdot(GS_{x}-TAS) +\frac{s^{2}}{s^{2}+Kp\cdot s+Ki}\cdot(k_{1}\cdot\dot{q}-k_{2}\cdot q+k_{3} \cdot\delta_{H}+k_{4}\cdot u) \tag{19}\]
# Artificial Neural Networks for Navigation Systems: A Review of Recent Research
## 5 Advantage and Disadvantages of ANN in Navigation System  
Current developments in ML have produced ANNs that are significantly more complicated and simpler to train than their predecessors. The LSTM architecture is one of the most significant developments [104]. To identify patterns in sequences, the LSTM technique learns from knowledge segments and stores in the form of series in memory. A filter in navigation can be thought of as a function of a series of machine assessments and feeds to produce a series of state estimates. As a result, LSTM ANNs can act like filters, but they are developed through updating on empirical data rather than by customizing filters based on past experience. By enhancing an existing filter, ANNs facilitate and filter data. The advantages of the augmentations include keeping the functionality of a verified filter with obtaining a major improvement via ANN augmentation. The augmentations involve offering corrections, adding measurements, and adjusting filter settings. This connection can be used by the learning algorithm to enhance the filter. ANNs are one type of system that can be used to extract information from knowledge. This now improves filter efficiency by raising the a priori knowledge of the issue.  
The state modifications of ANN might be updated simply by presenting the error at each step of the time as the objective for guided outcome and generating estimation from the filter at every time interval. The ANN would then pick up on the mistakes the filter made at each time step. The ANN will overstate the error made by the filter at runtime. Since the filter employs updated state projections at the beginning of every time period, the state estimates produced by the filter have less error. This feedback must be included in the ANN output target at each time step in order to prevent the ANN from overestimating the filter's mistakes. The ANN estimator will finally appear at a minimal area or saddle point after repeating this process, producing the same corrections in each iteration. As a result, the output expectation for the next upgrade will not be considerably altered. The additional step of recalculating the ANN's goal output can make this process more complex.
## 5 Experiments and Discussion
### Temperature and Humidity Compensation Tests  
For the temperature compensation test, we selected the temperatures of 0 \({}^{\circ}\)C, 10 \({}^{\circ}\)C, 20 \({}^{\circ}\)C, and 30 \({}^{\circ}\)C to test the system's methane measurement accuracy at a relative humidity of 75%. Figure 7 shows the measurement results before and after temperature compensation. The dotted line represents the measured value before temperature compensation, and the solid line represents the measured value after temperature compensation. The deviation of the measured value before compensation was relatively large compared to the deviation of the measured value after compensation. The measured value before compensation increased as the temperature increased, and the deviation was minimal when the temperature approached 20 \({}^{\circ}\)C. This was because the zero-point calibration of the sensor before testing was at a temperature of 20 \({}^{\circ}\)C. The compensated deviation was less affected by the temperature. It could be seen that temperature compensation could further reduce the impacts of temperature changes and improve sensor accuracy. Furthermore, even though the measured values' deviation before the compensation was large, the accuracy was still high; the dual-channel acoustic tube structure was able to achieve good temperature compensation.  
Figure 6: Results of methane concentration measurements before and after carbon dioxide correction: (**a**) comparison of methane concentration measurements before and after correction; (**b**) comparison of methane concentration measurement deviations before and after correction.  
Figure 7: Results of methane concentration measurements before and after temperature compensation: (**a**) comparison of methane concentration measurements before and after compensation; (**b**) comparison of methane concentration measurement deviations before and after compensation.  
At an ambient temperature of 20 \({}^{\circ}\)C, three saturated salt solutions were selected to attain relative humidity of 59% (sodium bromide), 75% (sodium chloride), and 85% (potassium chloride) in order to verify the humidity compensation performance. The measurement results before and after humidity compensation are shown in Figure 8. Since the zero calibration was performed in an environment with a relative humidity of 75%, measurement deviations before and after compensation were not significantly different at 75% relative humidity. When relative humidity was 59% or 85%, the deviation before compensation was quite large, and the value of the deviation increased as the humidity increased. After humidity compensation, the impact of humidity on the measurement results was significantly reduced.
## 6 TTL Estimation
### Results under \(L\)-Type Offset
#### 6.2.3 Impact of Laser Frequency Noise  
The impact of different laser frequency noise is shown in Figure 13 and Table 8. The angle biases and noise are the same as in the previous subsection and the offset vector was set to \((1500\quad 0.5\quad 0.5)\) mm for each satellite.  
Although higher laser frequency noise was added in case (b), the error of TTL estimation (black) does not exceed the TTL error of GRACE-FO (green) at the frequency band shown in Figure 13b. In other words, one can eliminate the TTL error with 1.5 m offsets to an equivalent low level caused by 0.5 mm offsets even if the frequency stability performance of the laser is an order of magnitude worse than GRACE-FO.
## 3 Results and Discussion
### Adsorption Behavior of the Pd-InN Monolayer
The electron accumulation on NO molecule and the electron depletion on Pd dopant are not as strong as that in CO system, in agreement with the Hirshfeld analysis, as shown in Figure 4c. Moreover, the electron hybridization on the Pd-N bond could be found given the overlap between the electron accumulation and the electron depletion. Additionally, we performed the zero-point vibrational energy (ZPVE) correction to calculate \(E_{\text{ad}}\) in the CO and NO systems. The ZPVE of the Pd-InN monolayer, the gas molecules, and the adsorbed systems are listed in Table 1. Based on these results, the ZPVE correction in the CO and NO systems would be 0.08 and 0.03 eV, and the \(E_{\text{ad}}\) with ZPVE correction were obtained as \(-\)2.04 and \(-\)1.62 eV, respectively. In this regard, we assume that the adsorption is chemically stable with little difference in \(E_{\text{ad}}\) when the temperature is extrapolated to 0 K.  
In short, the Pd-InN monolayer has admirable adsorption behavior upon CO and NO molecules. On the basis of the calculated \(E_{\text{ad}}\) in two systems, chemisorption can be identified [33]. This assumption could be further confirmed by the bond length analysis. According to the previous report [34], the covalent bond length of Pd-C and Pd-N are 1.95 and 1.91 A, respectively. That is, the new-formed Pd-C and Pd-N bonds in the adsorbing systems are much shorter than their covalent bond length, suggesting their strong binding force and their chemical bond nature. Furthermore, it is worth noting \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**System** & **Pd-InN** & **CO Molecule** & **NO Molecule** & **Pd-InN/CO** & **Pd-InN/NO** \\ \hline ZPVE (eV) & 1.91 & 0.13 & 0.12 & 2.11 & 2.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: ZPVE of Pd-InN monolayer, gas molecules and adsorbed systems. that the larger electron hybridization between Pd and gaseous species can lead to more discrepancy in the conductivity of the Pd-InN monolayer [35; 36], which would be beneficial for a resistance-type gas sensor. In order to further verify the electronic behavior of Pd-InN upon CO and NO adsorption, DOS and band structure analysis was performed and is described in the next section.
## 4 Methodology
### Data Preprocessing
#### 4.1.1 Dataset Description  
Two engine datasets were used in the current case study:  
* Real-life Engine Operational Data: This primary dataset was collected from a fleet of modern, powerful low-bypass turbofan engines over 12 years of flight and ground operations. It comprises 43,472 records collected for 53 individual engines. Data was gathered from 12 engine sensors and 2 aircraft signals. While there are 12 physical sensors, 86 different parameters are recorded as \"Performance Data\". The dataset includes columns, representing various parameters like ESN (Engine Serial Number), Time, Total temperature at station 2 (TT2), Mach number, Power Lever Angle (PLA), sensor readings (e.g., Pt2, PbC), operational conditions, and derived metrics (e.g., Total Accumulated Cycles TACS, Engine Operating Time EOT2). Each row represents a snapshot of data for a specific engine on a given flight. The Engine Monitoring System (EMS) records one set of averaged engine operating parameters during every aircraft takeoff. This specific flight phase is crucial, as it demands maximum thrust, subjecting the engine to its greatest stresses and loads, which significantly impact its condition and health status. This makes takeoff data highly reflective of engine performance, efficiency, and degradation. Though 86 parameters were recorded, only degradation-sensitive features are retained through autoencoder compression. Core sensors like TT2 (compressor health), Pt2/PbC (airflow integrity), and derived metrics TACS/EOT2 (cumulative wear) are critical for failure prediction. Mach number and PLA provide operational context for stress normalization. Crucially, this real-life dataset is not run-to-failure data, meaning that the exact engine status, failure moment, and Remaining Useful Life (RUL) for each engine are unknown. This scenario is very common in real-world operational datasets collected over many years. The whole real-life engine operational data originates from the fleet of F100-PW-229 Pratt & Whitney turbofan engines.
* NASA Turbofan Engine Degradation Dataset (CMAPSS): This is a widely used simulated dataset, employed in this study to prove the effectiveness of the designed model and enable comparison with other models existing in scientific publications. Unlike the real-life data, CMAPSS data is run-to-failure data, in which engines reach their failure moment in the last recorded set of parameters. The CMAPSS dataset consists of four sub-datasets (FD001, FD002, FD003, FD004), and the results presented in this paper for CMAPSS are often the mean performance across these sub-datasets.  
Both datasets contain sensor data (e.g., temperatures, pressures sensed at various engine cross-sections, RPM, airflow, fuel metered) from the engines, as well as environmental data and thermodynamic parameters calculated during engine operation.
# A Blockchain and IoT-Enabled Framework for Ethical and Secure Coffee Supply Chains
## 6 Conclusions  
This paper has presented a blockchain and IoT-enabled framework for enhancing traceability, accountability, and transparency across the coffee supply chain. By integrating simulated IoT data with Ethereum-based smart contracts, the proposed system captures and automates key supply chain events such as batch registration, weight verification, environmental monitoring, and ownership transfer. A structured dataset was designed to reflect realistic sensor outputs, allowing the system to be tested under conditions that closely resemble actual field deployments. Our system architecture accommodates RFID registration, GPS tracking, and mobile validation workflows, thereby laying the groundwork for seamless integration with real-world hardware platforms.  
The proposed smart contract was deployed and evaluated on the Sepolia Ethereum testnet and transaction data were analyzed across multiple performance dimensions, including gas cost, execution time, and administrative overhead. The results demonstrate that the proposed system operates within low transaction cost bounds while maintaining functional correctness and reliability, which is our main priority for this work. Similarly, our interaction model enforces role-specific permissions and immutably records all transactions in the decentralized ledger, ensuring verifiability across all stakeholder actions in the coffee supply chain.  
While the current implementation relies on simulated inputs, our framework is modular and extensible, offering a feasible path toward physical deployment using platforms such as Node-RED and Raspberry Pi. The novelty of this work lies in its detailed mapping of real-world coffee logistics into smart contract logic as well as in its simulation-based validation strategy, which provides a replicable approach for similar supply chain scenarios. Our future extensions will focus on real sensor integration, blockchain oracle support for off-chain data, and broader scalability testing under multi-batch parallel conditions.  
**Author Contributions:** The authors declare that they have equally contributed to the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy and commercial restrictions.  
**Acknowledgments:** We want to thank the authors of the literature cited in this paper for contributing useful ideas to this study.  
**Conflicts of Interest:** The authors declare no conflicts of interest; the funders had no role in the design of the study, in the collection, analysis, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.
## 3 Results
### HE Activates AKT/GSK3\(\beta\) Signaling Pathways in HDP Cells  
Several studies have reported that cell proliferation in dermal papilla is enhanced through the phosphoinositide 3-kinase (PI3K)/AKT pathway and mitogen-activated protein kinase (MAPK) signaling [58; 59; 60; 61]. These signaling pathways also play essential roles in hair follicle inductivity [62; 63; 64; 65]. Furthermore, several bioactive compounds with hair growth effects have been shown in prior studies to modulate hair follicle inductivity by activating the AKT/\(\beta\)-catenin pathway [66]. Given these points, HE might also have an activating effect of the AKT signaling pathway. Thus, it is determined whether or not HE indeed activates the AKT signaling pathway. After HE treatment, the expression levels of proteins related to AKT and MAPK pathways and the phosphorylation levels were analyzed using Western blot assays with their specific antibodies. As expected, HE increased the phosphorylation level of AKT, but the total protein expression was not changed (Figure 4a). Following this, the phosphorylation level of GSK3\(\beta\) at Serine 9 was also increased in HE-treated HDP cells. Still, MAPK-associated signaling molecules including p38 MAPK, extracellular signal-regulated kinase (ERK), and c-Jun N-terminal kinase (JNK) were not changed by HE treatment (Figure 4b). In conclusion, these results indicate that the increased proliferation and activated \(\beta\)-catenin signaling pathway in HDP cells were facilitated by HE via the AKT/GSK3\(\beta\) signaling pathway.  
Figure 3: Effect of HE on \(\beta\)-catenin signaling activation. \(\beta\)-catenin stabilization (**a**) with dose dependence and (**b**) with the passage of time and (**c**) translocation were analyzed using Western blotting with specific antibodies after treating with the indicated concentration of HE. \(\beta\)-actin, \(\beta\)-tubulin, and Lamin A/C served as loading controls for total protein, cytoplasmic fraction, and nuclear fraction, individually. Quantification of the protein levels was performed using ImageJ software v. 1.52a. Data are presented as the mean \(\pm\) SD from three independent experiments. Statistical significance was analyzed using one-way ANOVA followed by [PERSON]’s post hoc test. ***\(p<0.001\) compared to the vehicle-treated group. HE, heterotrophic-cultured _S. deserticola_ JD052 extract.
## 4 Performance Analysis
### Energy Efficiency  
Energy efficiency is the ratio of the total number of bits that were transmitted to the terminal network (or, in the case of collecting techniques, the core network) to the total amount of energy used by the network to send these messages. The energy efficiency offered by the suggested and existing approaches is shown in Figure 6 and Table 5, demonstrating that the suggested strategy uses less energy for transmission.