esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis of Co-Occurrence Networks and Functional Prediction in Different Displacement Blocks
A connection stands for a strong correlation (Spearman’s |r| > 0.6) and significant (_p_ < 0.05) of bacterial communities in ASP and WF blocks. Correlation Analysis between Microbial and Environmental Factor of Oil Reservoirs in Different Displacement Blocks  
The produced fluid from the production well was analysed for the presence of ionic composition and total salinity (Table S1). The results showed that the produced fluid from the production well mainly contained HCO3\({}^{-}\), Cl-, SO4\({}^{2-}\), Na+, Mg2+, Ca2+, and CO3\({}^{2-}\). The total salinity of different production wells ranged from 1963.41 to 19599.92 mg/L. In order to better implement microbial enhanced oil recovery technology, we performed a redundancy analysis (RDA) on the physicochemical parameters and microbial dominant genus. Physicochemical parameters were used as explanatory variables and dominant genus populations as response variables (Figure 6a). Among them, salinity (r2 = 0.71, \(p\) = 0.001) was a significant factor influencing the microbial community structure of oil reservoirs. In addition, Na+ (r2 = 0.72, \(p\) = 0.001), HCO3\({}^{-}\) (r2 = 0.66, \(p\) = 0.001), Cl- (r2 = 0.61, \(p\) = 0.001), and SO4\({}^{2-}\) (r2 = 0.59, \(p\) = 0.001) were also important factors influencing the microbial community structure of oil reservoirs. To further explore the positive and negative correlations between these environmental factors and dominant genus populations, a correlation heatmap between dominant genus and environmental factors was generated (Figure 6b). This showed that _Ralstonia_ sp. and _Halomonas_ sp. had a significant positive correlation with Cl-, khd, SO4\({}^{2-}\), Na+, and CO3\({}^{2-}\), whereas _Desulfomicrobium_ sp., _Hydrogenophaga_ sp., and _Desulforhudus_ sp. had a significant positive correlation with HCO3\({}^{-}\), Mg2+, and Ca2+. Figure 5: Prediction of the functionality of ASP and WF blocks. The percentage of ASV with the same characteristics can reflect the degree of functional redundancy in microbial communities. P11 and P7 are WF block samples, while the rest are ASP block samples.
## 4 Results
### Correlation between IPLFCS and FC  
Figure 4 shows the correlations between average FC strength and average IPLFCS in the SCD1, MCI1, AD1, SCD2, MCI2, and AD2 groups. In different disease groups, the results demonstrated that the correlations between FC strength and IPLFCS were strongly significant (\(p<0.001\)) in SCD1 (r = 0.956, Figure 4a), MCI1 (r = 0.944, Figure 4b), AD1 (r = 0.980, Figure 4c), SCD2 (r = 0.946, Figure 4d), MCI2 (r = 0.856, Figure 4e), and AD2 (r = 0.853, Figure 4f). It was noted the IPLFCS and FC strength were positively correlated, indicating that the IPLFCS could perfectly characterize FC strength. Supplementary Table S2 provides the results of the Granger causality analysis for the average FC strength and average IPLFCS. No causal relationship was found between the average FC strength and average IPLFCS in SCD1, MCI1, AD1, SCD2, MCI2, and AD2 groups. While we found a correlation between IPLFCS and FC, we did not observe a direct causal relationship. We conclude that these two features are interrelated, but they are not causally related.  
Figure 3: The IPLFCS in AD continuum. (**a**) The IPLFCS of LMTG in groups of cohort 1. (**b**) The IPLFCS of LMTG in groups of cohort 2.
## 5 Testing and Navigation
### Testing Strategy
Furthermore, a 2-day endurance test was run off the coast of Halifax. A total of four months were required for the re-design, testing, and launch of the Breizh Tigress. Following the launch of the Breizh Tigress, almost two years were required to design and launch the SeaLeon. Note that it was important to time the launch of the sailboat to avoid hurricanes that are frequent in North America in the fall season. A similar testing procedure as for the Breizh Tigresse was adopted. Note that during dry land tests, the entire boat with sails was mounted in the back of a truck navigating a set of pre-programmed waypoints located around Dalhousie University. It was possible to monitor the performance from inside the truck through the radio to confirm waypoint validation as well as navigation, wind, and rudder information. Following these these tests, similar tests as those that were run with the Breizh Tigresse on the Northwest Arm were run. A few tests were run in stronger wind conditions and various sea states in the open sea to analyze the boat's behaviour. These tests were encouraging, with the hull remaining stable on the waves and the only downside being unavoidable: wind shadow from upwind waves in steeper chop. A comparison of the routes taken by the Breizh Tigresse and SeaLeon is shown in Figure 4 and will be analyzed in the next section. An important difference between the Breizh Tigresse and the SeaLeon was the motor design. For the SeaLeon, a clutch was used to prevent the motors from being overpowered. During initial testing, it was observed that the sail control clutch actuated frequently. Sudden wind gusts activated the clutch, and in these conditions, the motor could pull in the sail to the desired position after a delay. This limited the peak loads that the sail sheeting system could experience. To reduce the sensitivity of the clutch, the winch drum diameter was reduced from 7.6 cm to 4.8 cm. As a consequence, this increased the wind loads that were required to decouple the motor and sail control. In addition to the reduction in power consumption, there was a gain in resolution for sensing the sail position. This was measured on the drum shaft using a potentiometer. Figure 4: Route comparison with waypoints and tracks; Breizh Tigresse (yellow); SeaLeon (orange); Starting line in red.
# A Real-Time On-Orbit Signal Tracking Algorithm for GNSS Surface Observations
## 4 Method, Part 2: Open Loop GNSS-R Delay and Doppler Tracking Algorithms
### Estimating the Reflected Signal Doppler Frequency  
The Doppler frequency of a reflected signal can be calculated using the following terms,  
\(\vec{R}\) = The receiver position, \(\vec{R}_{p}\) = the receiver velocity, \(\vec{T}\) = the transmitter position, \(\vec{T}_{p}\) = the transmitter velocity, \(\vec{S}\) = the best estimate of the specular point position, \(D_{clk}\) = the Doppler frequency contribution due to the GPS receiver clock, \(f\) = The GNSS signal transmit frequency, for GPS L1 = 1575.42 \(\times\) 10\({}^{6}\) Hz, and \(c\) is the speed of light, 299,792,458 m/s.  
Using these parameters and estimates of the unit vectors from the specular point to the transmitter and receiver calculated using Equations (11) and (12), the individual Doppler components can be calculated as (from [27]),  
\[D_{R}=-1(\vec{R}_{v}\cdot\hat{D}_{RS})(\frac{f}{c}) \tag{19}\]  
\[D_{T}=-1(\vec{T}_{v}\cdot\hat{D}_{TS})(\frac{f}{c}). \tag{20}\]  
The open loop tracking reflected signal Doppler frequency is the sum of the two geometric components, and the contribution from the GPS receiver clock, expressed as follows,  
\[D=D_{R}+D_{T}+D_{clk}. \tag{21}\]  
The receiver clock error \(D_{clk}\) is output regularly from the GPS navigation receiver position, velocity and timing estimation.
## 3 Research Results  
The program used to build the network, learn it and run tests was the GeNIe Academic Version 2.3.3828.0 [79]. The first step was the automatic separate learning of each network \"AM\" and \"IF\" by implementing the corresponding the knowledge base--data set (training set). The GeNIe program teaches the network by using EM (Expectation--Maximisation). The advantage of EM is teaching with missing values in the training set [80]. In every experiment, the parameter initialization group was: Uniformize. This triggers the algorithm with all network parameters taken from the uniform distribution with the existing parameters describing the network ignored. Taught networks were then connected with the result node \"P\", which was taught manually by inputting the appropriate Equations (2)-(5), to filled in CPT.  
Therefore, three research experiments were carried out, which differed in nodes building the network. Representation of BN is presented at Figure 2, where \"IF\" nodes are blue color, P9.2 nodes--green, P10.1 nodes--violet, or P10.3 nodes--red. The following relationship was defined, viz. the number of elements to choose from in questionnaires in order to maximise the number of possible choices, according to the assumption: the more times choice appears, the more popular, available, and known it is.  
Network constructions and rules for each experiments are presented below. Always, probability for \"P\" node for State0 was defined as Equation (2):  
\[\mathrm{P}_{(\mathrm{P=State0})}=1-\mathrm{P}_{(\mathrm{P=State1})}, \tag{2}\]  
where:  
P(P = State0)--the probability of the occurrence of State 0 of \"P\" node,  
P(P = State1)--the probability of the occurrence of State 1 of \"P\" node.  
The proposed \"two-parts\" model, firstly, using resources from literature (52 records) and then using data from empirical research (99 records), yields 85.00% and 92.00% accuracy, respectively.  
Figure 2: The scheme of Bayesian network with “IF” nodes and all “AM” nodes.
## 4 Experiments
### Experiments on Benchmark Datasets  
#### 4.1.1 Experiments Description  
We compare the performances of the introduced classifiers in Section 1 with 10 real-world datasets, selected from the UCR Time Series Classification Archive [42] randomly, which contains different application domains datasets, and is widely used as a benchmark for classification comparisons. To contrast with multiple types of classifiers, we have made an effort to consider some classical and effective methods, including (1) distance-based methods:1-NN with Euclidean distance (1 NN-ED) [9] and 1-NN with DTW (1 NN-DTW) [10], (2) feature-based methods: shapelets [11] and random forests based on features (Features-RF) [12], (3) ANN-based methods: feedforward neural networks (FNNs) [43], RNNs [17], LSTMs [20] and our BICORN-RNNs. It should be noted that the experimental results of type (1) and (2) were collected by [44; 45], for an authoritative comparison. As for type (3), we use the default training and testing set splits provided by UCR for fairness concerning.  
Without loss of generality, all neural network classifiers are set as a single layer hidden layer structure, and the number of neurons in hidden layer is 24. All weights in matrices are initialized in the interval \([-\sqrt{\frac{6}{r+c}},+\sqrt{\frac{6}{r+c}}]\) drawn from the uniform distribution, where \(r\) and \(c\) are the number of rows and columns in the structure [46].  
Generally, larger training sets can have a better generalization performance. However, considering the fact that it may suffer from the imbalanced percentages of training and testing examples in practical application, we improve the generalization performance with the limited datasets size through an early stopping method in the validation set, which avoids overfitting in the testing set. In this paper, both the validation error and training error are evaluated at regular intervals with a typical decrease. If the validation error appears to start increasing, the training will terminate at once.
# Nomological Deductive Reasoning for Trustworthy, Human-Readable, and Actionable AI Outputs
## 5 Nomological Deductive Reasoning (NDR): A Proposed Solution
### NDR Framework Architecture and Operational Integration with Machine Learning
#### 5.2.1 System Architecture Overview  
To operationalize the Nomological Deductive Reasoning (NDR) framework, we propose a multi-layered architecture that integrates symbolic knowledge representation, statistical learning, and deductive inference into a unified explanation pipeline. Figure 7 illustrates the overall system design, detailing how each component--from domain knowledge encoding to user interaction--contributes to generating transparent, norm-compliant, and causally grounded explanations. This architecture supports the core objective of aligning machine learning outputs with structured, law-based reasoning to enhance trust and accountability in AI decision-making.  
The **Knowledge Representation Layer** encodes structured domain-specific knowledge --such as legal statutes, clinical guidelines, or financial regulations, etc.--into a semantic ontology using symbolic rules and causal relationships. This layer forms the epistemic foundation for deductive reasoning. The **Machine Learning Layer or AI Model Layer** performs statistical learning using traditional [78; 142] or deep learning models [143]. It extracts salient features from input data and provides probabilistic or deterministic predictions that are then evaluated by the **Reasoning and Deductive Inference Engine** against the symbolic rules encoded in the knowledge base. Using logical inference and causal reasoning, it verifies whether the AI's output aligns with normative expectations and domain constraints. The inferred decision is then passed onto the **Explanation Generation Layer** to produce human-readable, law-based explanations grounded in domain-specific knowledge as encoded in the knowledge base. These explanations are crafted to be both comprehensible and traceable. Finally, the generated explanation is sent to the **User Interaction and Trust Evaluation Layer**, which interfaces with end-users, supporting transparent decision communication. Each component contributes to ensuring explainability, traceability, and robustness in high-stakes decision-making scenarios such as in healthcare and medical diagnosis, credit-risk assessment, criminal justice, etc.
## 3 Results
### ODSS Analysis  
The mean ODSS was 15.15 \(\pm\) 6.81 (range 4-29) and 13/44 (29%) patients had ODSS values above 20 points, indicating moderate-severe oral involvement.  
The ODSS in patients with values of BP180 ELISA > 20 U/mL was found to be statistically greater compared to patients with lower titers of circulating anti-BP180 (19.47 \(\pm\) 6.70 vs. 12.44 \(\pm\) 5.40 \(p\) = 0.001). (Figure 2).  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Clinical Variable** & \multicolumn{2}{c}{**ODSS**} & \multicolumn{2}{c}{**Univariate Analysis**} & \multicolumn{2}{c}{**Multivariable Analysis**} \\ \hline  & **N.** & **(Mean \(\pm\) SD)** & \(p\)**-Value** & \(p\)**-Value** \\ \hline Sex & & & 0.86 & \\ Male & 10/44 & 14.5 \(\pm\) 6.16 & & \\ Female & 34/44 & 15.35 \(\pm\) 7.06 & & \\ Age (mean 64.36 \(\pm\) 15.44 yrs) & & & & \\ \(<\)65 yrs & 21/44 & 16.28 \(\pm\) 7.05 & & \\ \(>\)65 yrs & 23/44 & 14.13 \(\pm\) 6.56 & & \\ Eosinophils & & & 0.74 & \\ Absent & 31/44 & 15.41 \(\pm\) 6.98 & & \\ Detectable & 13/44 & 14.53 \(\pm\) 6.60 & & \\ Band-like infiltrate & & & 0.12 & 0.17 \\ Absent & 40/44 & 14.62 \(\pm\) 6.44 & & \\ Detectable & 4/44 & 20.5 \(\pm\) 9.11 & & \\ IgA deposit & & & 0.001 \({}^{\star}\) & 0.035 \({}^{\star}\) \\ Absent & 35/44 & 16.48 \(\pm\) 6.96 & & \\ Detectable & 9/44 & 10 \(\pm\) 2.39 & & \\ IgM deposit & & & & \\ Absent & 40/44 & 15.61 \(\pm\) 7.05 & 0.30 & \\ Detectable & 4/44 & 11.5 \(\pm\) 3.41 & & \\ C3 deposit & & & & \\ Absent & 4/44 & 16.5 \(\pm\) 5.74 & 0.55 & \\ Detectable & 40/44 & 15.02 \(\pm\) 6.95 & & \\ BP180 ELISA (mean 25.81 \(\pm\) 29.29 U/mL) & & & 0.001 \({}^{\star}\) & 0.002 \({}^{\star}\) \\ BP180 \(<\) 20 & 27/44 & 12.44 \(\pm\) 5.40 & & \\ BP180 \(>\) 20 & 17/44 & 19.47 \(\pm\) 6.70 & & \\ Total & 44 & 15.15 \(\pm\) 6.81 & & \\ \hline \end{tabular} ODSS—oral disease severity score; SD—standard deviation. An asterisk (*) denotes a statistically significant association.  
\end{table}
Table 1: ODSS values according to the studied clinical variables in the studied population.  
Figure 2: Boxplot showing levels of ODSS in patients with circulating levels of anti-BP180 NC16A ELISA below and above the cut-off of 20 U/mL.  
By contrast, the presence of deposits of IgA was associated with lower values of ODSS (10 \(\pm\) 2.39 \(p\) = 0.001).  
BP180 ELISA > 20 and IgA both emerged in the multivariable analysis as variables statistically related to the ODSS (\(p\) = 0.002 and 0.0035 respectively) (see Table 1).
## 4 Experiments and Results
### Evaluation Metrics
#### 4.3.4 Entropy  
The entropy is a statistical measure of data randomness [46]. In random data, the information entropy should be around 8. The entropy of the encrypted standard images was calculated and displayed in Table 5. According to the findings, the encrypted photos contain the required entropy.
## 3 Results
### TC Adsorption Isotherms
These findings are consistent with previous studies in the literature, reinforcing the significance of surface modification through acid treatment to enhance adsorption capacity [33; 34]. The Temkin model, which assumes that adsorption energy decreases as sites become occupied, further supports these observations. In this model, the constant B reflects the distribution of adsorption energy, and the parameter A\({}_{\rm T}\) represents the binding energy of the adsorbent. The Temkin model suggests that initial adsorption interactions are strong, gradually weakening as more sites are occupied. For BA1 (Figure 11C), these parameters indicate that the adsorption process is energetically favorable in the early stages, reinforcing the role of both physisorption and chemisorption in the adsorption of tetracycline. However, for BN (Figure 11A,B), the Temkin model did not fit as well as the other models, likely due to the assumption that adsorption energy decreases uniformly across all adsorption sites, which may not fully capture the heterogeneity of BN. Taken together, the isotherm analysis confirms that BA1 exhibits a heterogeneous but highly efficient adsorption surface, making it an ideal candidate for applications involving tetracycline removal, with significant contributions from both physical and chemical adsorption mechanisms. Meanwhile, BN demonstrates concentration-dependent adsorption behavior, with heterogeneity playing a more prominent role at lower concentrations and monolayer adsorption becoming more dominant at higher concentrations. A comparison of various studies on tetracycline adsorption reported in the literature, emphasizing factors such as isotherm and kinetic models applied, type of adsorbent, removal efficiency, and time required to reach adsorption equilibrium, is summarized in Table 4. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Clay Mineral** & **Isotherm** & **Kinetics** & **Time (min)** & **Removal (\%)** & **Author** \\ \hline BN & [PERSON] & Pseudo-second order & 30 & 97 & This study \\ BA1 & [PERSON] & Pseudo-second order & 30 & 99 & This Study \\ Nano montmorillonite & Langmuir & Pseudo-first order & 60 & 90 & [4] \\
20\% In\({}_{2}\)O\({}_{3}\)/[PERSON] & Langmuir & Pseudo-second order & 60 & 98 & [35] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative analysis of tetracycline hydrochloride adsorption.
# Interplay Between Wear and Thermal Expansion in 6082 Aluminum: A Simulation and Experimental Study
## 3 Results and Discussion
### Interface Temperature  
Figure 11 shows the temperature on the pin and disc, comparing scenarios with and without wear. Note that in the scenario without wear, thermal expansion and material softening are still considered. The highest temperature occurs at the contact center, forming a comet-shaped pattern on the disc that decreases along the direction of friction. This observation aligns with the findings in [32; 33]. On the pin, the temperature spreads in a circular pattern, decreasing uniformly in all directions.  
Under identical conditions, the maximum temperature on the pin and disc shows a similar fluctuating pattern, with the pin slightly hotter. The rise in maximum temperature varies between scenarios with and without wear. For the first 0.1 min, the maximum temperatures in both scenarios are identical, as wear has not yet significantly affected contact behavior. As wear progresses and further impacts contact behavior, temperature differences become apparent. The simulation without wear shows a higher, steadily increasing temperature during sliding. In contrast, the simulation with wear exhibits fluctuations due to more complex contact behavior. The measured temperatures align more closely with the simulation, which includes wear. However, discrepancies remain, possibly due to the initial temperature differences between the simulations and laboratory conditions.  
Figure 12 compares the temperature distribution evolution on the pin. An interesting observation emerges: with wear, the heat source expands along the friction direction due to sliding and shifts toward the center of the disc. The heat source then shifts in the opposite direction, eventually positioning itself within the nominal contact center. In contrast, without wear, the heat source remains nearly stationary at the pin center but still expands along the friction direction.  
Figure 11: Comparison of the maximum temperature between the experiments and simulations on the (**a**) pin and (**b**) disc under 15 N and 300 RPM.
## 3 Experiments and Results
### Evaluation Metrics  
In the test phase, the local CHarts with holes are selected as the ground truth on each of the testing models, and the size of the input point cloud is unfixed. We randomly select \(\left\{\frac{1}{2}N,\frac{1}{4}N,\frac{1}{8}N\right\}\) points from a hole region patches as testing input. Due to the disorder of discrete point cloud, and to evaluate the completion results against the ground truth, we adopt two standard metrics introduced in [35]: Chamfer distance (CD) and Earth movers distance (EMD).  
Figure 5: (**a**) Point cloud of points directly acquired by the 3D scanner; (**b**) point cloud after the downsampling procedure with a sampling coefficient of 60%.  
Figure 6: Point cloud patches with different scales.  
The CD denotes the distance between the generated point cloud \(P_{d}\) and the ground truth \(P_{gt}\). The formula is symmetric, and the first part guarantees the minimum distance between the generated point cloud and the real point cloud, and the latter part ensures that the real point cloud is covered by the output point cloud.  
\[\text{CD}\big{(}P_{d},P_{gt}\big{)}=\frac{1}{|P_{d}|}\underset{i\in P_{d}}{ \text{min}}\underset{j\in P_{gt}}{\text{min}}\|i-j\|_{2}+\frac{1}{|P_{gt}|} \underset{i\in P_{gt}}{\text{min}}\underset{j\in P_{d}}{\text{min}}\|j-i\|_{2} \tag{5}\]  
EMD is defined as follows:  
\[\text{EMD}\big{(}P_{d},P_{gt}\big{)}=\underset{\varphi:P_{d}\to P_{gt}}{ \text{min}}\frac{1}{|P_{d}|}\underset{i\in P_{d}}{\text{min}}\|i-\varphi(i)\| _{2} \tag{6}\]  
where \(\varphi\) is the mapping generated point cloud to ground truth point cloud. EMD minimizes the average distance between the corresponding point. EMD will be higher than CD, because EMD is the point-to-point correspondence, whereas CD can be one to many.
# Service Interface Translation. An Interoperability Approach
## 2 Related Work
### Middleware and Mediators  
In an attempt to overcome interoperability problems, new middleware frameworks and mediators have been proposed. In [15], an automated synthesis of mediators for middleware-layer protocol interoperability in the IoT was presented. The interoperability between heterogeneous middleware protocols is challenging due to the difference in semantics and technology diversity. This approach relies on the identification of common abstract interaction types according to the interaction paradigms and their modeling into a DeX API and a connector model. Another powerful example is the GeoNis framework for interoperability [16; 17], which is focused on electric power supply companies and acts as a core component of the interaction of distributed entities. Middleware is often applied to overcome mismatches in terms of computational devices, communication networks, and operating systems. This approach is usually tailored to specific technologies and scenarios that lack scalability and flexibility.