esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Correlation Coefficient and Principal Component Analyses  
The results of the heat diagram of the correlation coefficient between sample pairs are shown in Figure S3. Positive correlations were shown between pairs of rhizosphere microbiome samples, while negative correlations were shown between any given rhizosphere microbiome versus any given bulk soil microbiome, and vice versa. These results support our claim that microbiome structures of the two soil types should differ. The strength of correlations involving samples of group B (P1S2 or P1R2) was lower than correlations involving none of the group B samples. This observation might refer to the higher influence of watering on microbiomes collected at the 24 h time point than on microbiomes collected at the 0 or 48 h time points (Figure S3).  
Principal component analyses (PCA) based on the number of genes at phylum, genus and species levels are shown in Figure 1. The results indicate a discernible separation among samples of bulk and rhizosphere soil microbiomes across the three taxonomic levels. Interestingly, the PCA1 (or PC1) axis completely separated samples based on soil type, while the PCA2 (or PC2) axis completely separated samples based on watering time point. At both the phylum and species levels, bulk soil microbiomes are located on the positive side of the PC1 axis, while rhizosphere microbiomes are located on the negative side. At genus level, microbiomes of the two types of soils switched their sides. Across the three taxonomic levels, group A microbiomes were located on the negative side of the PC2 axis, group B microbiomes were located on the positive side of the PC2 axis, while those of group C were located in the middle (or around 0 position of the PC2 axis) (Figure 1).  
Figure 1: Principal component analysis (PCA) based on the number of genes at phylum (**a**), genus (**b**) and species (**c**) levels of metagenomes collected from surrounding bulk (S) and rhizosphere (R) soils of _Dipterygium glaucum_ after 0 (group A, or P1S1 and P1R1, respectively), 24 (group B, or P1S2 and P1R2, respectively) and 48 h (group C, or P1S3 and P1R3, respectively) of watering. Orange circles = bulk soil microbiomes, blue circles = rhizosphere microbiomes, black circles = group A, red circles = group B, green circles = group C.  
Overall results of the correlation coefficient and principal component analyses reflect the reliability of the experiment and the rationality of grouping microbiomes based on soil type (e.g., bulk and rhizosphere) and time after watering (e.g., 0, 24 and 48 h).
# Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
## 4 PIDL for UQ
### Real-World Data Validation
In Figure 13e, we summarize the \(RE\) (x-axis), i.e., the relative difference between the predicted and ground-truth mean, and \(KL\) (y-axis), i.e., the statistical difference between the predicted and ground-truth distribution, of all of the models with different training datasets that are shown in Figure 13a-d. Each point represents a combination of metrics by applying one model type to one training dataset. We interpret these points by assigning them into four regions:  
* Region A (optimal RE and KL): Most points in this region belong to the TrafficFlowGAN model type (stars), which shows that the combination of PI-GAN and PI-Flow helps to achieve the best performance in terms of both RE and KL. * Region B (low RE and high KL): Most points in this region belong to GAN (inverted triangles) and PI-GAN (dots), which is a sign that the GAN-based models are prone to mode-collapse. * Region C (balanced RE and KL): Most points in this region belong to the PI-Flow model type, indicating that explicit estimation of the data likelihood helps to balance RE and KL. * Region D (high RE and low KL): Most points in this region belong to the EKF (triangles) and PI-GAN-FDL (squares), showing that these two types of model can better capture the uncertainty than the mean. **Transition from pure physics-driven to data-driven TSE models**: Figure 14 shows the optimal \(\beta/\alpha\) ratio of the TrafficFlowGAN under different numbers of loop detectors. The optimal \(\beta/\alpha\) has a similar decreasing trend as in the deterministic TSE problem shown in Figure 10.
# An Innovative System of Deep In Situ Environment Reconstruction and Core Transfer
## 5 Fuzzy-PID Dual Mode Pressure Control  
The structure diagram of pressure control is shown in Figure 6. In Figure 6, Inlet is the entrance of water. Drive Entrance is the pneumatic source that drives the piston to pressurize the water under the control of the pressurization valves. High Pressure Outlet is the exit of high-pressure water leading to the chamber. Unloading Port is the pressure relief outlet. The system collects pressure signals through 1 and 2, and obtains control variables for the pressurization valves and pressure relief valves by pressure control algorithm. The pressure is then controlled by PLC to pressurize through 5 and 6 or reduce pressure through 3 and 4. The traditional PID control is prone to overshoot. In the research, a fuzzy-PID dual mode method is adopted to reduce the response time and the overshoot.  
As shown in Figure 7, pressure error \(e\) and its change rate \(ec\) are the input variables, and the control quantity \(u\) of the valve is the output variable. When \(e\) is small, \(u\) is mainly decided by the PID algorithm to ensure high control accuracy, while when \(e\) is large, \(u\) is mainly given by the fuzzy control algorithm to ensure quick dynamic response. The final value of \(u\) is obtained by the weighted sum of the outputs by the PID control and the fuzzy control. The weight coefficient \(w_{2}\) of the fuzzy control is determined by Equation (10), and the weight coefficient of PID control is decided accordingly by \(w_{1}=1-w_{2}\).  
\[\left\{\begin{array}{c}w_{2}=1\The method of the fuzzy control is as follows:  
1. Define \(E\) in the domain of discourse of fuzzy discrete set to represent the deviation, \(EC\) to represent the deviation change, and \(U\) to represent the output;
2. Set the domain of discourse of \(E\) and \(EC\) as \(\{-6\), \(-5\), \(-4\), \(-3\), \(-2\), \(-1\), \(0\), \(1\), \(2\), \(3\), \(4\), \(5\), \(6\)], and the domain of discourse of \(U\) as \(\{-3\), \(-2.5\), \(-2\), \(-1.5\), \(-1.0\), \(-0.5\), \(0\), \(0.5\), \(1.0\), \(1.5\), \(2.0\), \(2.5\), \(3\)];
3. Seven fuzzy linguistic variables are used, namely PB (positive big), PM (positive middle), PS (positive small), ZO (zero), NS (negative small), NM (negative middle) and NB (negative big);
4. Triangular membership function is adopted, as shown in Figure 8;
5. The main principles for determining \(U\) according to \(E\) and \(EC\) are as follows: 1. When \(E\) is big, \(U\) should be big to speed up the system response; 2. When \(E\) is medium and \(EC\) is small, \(U\) should be medium to slow down the control; 3. When \(E\) and \(EC\) are both small, \(U\) should be small.
6. Center-of-gravity defuzzification is used on \(U\) to get its precise output.  
Figure 8: Triangular membership function.  
Figure 6: The structure diagram of the pressure control.  
Figure 7: Fuzzy–PID dual mode pressure control.  
The inference rules are shown in Table 1.
# Impact of Cooking Processes on Volatile Flavor Compounds and Free Amino Acids in Fish Sauce
## 3 Results and Discussion
### Qualitative and Semi-Quantification Analysis of Volatile Compounds in the Fish Sauces  
The relative quantification of flavor compounds in fish sauce samples subjected to different cooking processes is shown as a heatmap (Figure 3). A total of 38 volatile compounds were detected across all fish sauce samples, including 1 amine, 1 sulfide, 7 esters, 2 alcohols, 5 aldehydes, 7 acids, 6 ketones, 3 phenols, and 6 others. The compounds identified by mass spectrometry (MS) were positively and negatively matched with a degree > 700. In addition, 32 compounds were confirmed by RI, with a deviation of less than 30 [22]. Figure 3 shows the identification of 33, 35, 35, 32, 34, 27, and 24 odorants in the U, B10, B30, B60, F10, F30, and F60 samples, respectively. The number of volatile compounds initially increased and then decreased with extended cooking times. The total volatile compound content (Figure 4) also reflects the effect of cooking processes on the flavor of fish sauce. The total volatile compound concentration in B30 was significantly higher than in the control group. Minimal variation was observed in total volatile compound concentration between U and B60, while no significant difference was observed between B10 and U. In the stir-fried samples, the intensity of volatiles in F10 increased significantly, while the intensities in F30 and F60 decreased slightly, remaining consistent with their respective volatile compound numbers. These results indicate that fish sauce exhibited higher concentrations of volatile compounds at moderate cooking times. This may occur because heating triggers the Maillard reaction, which generates flavor compounds. However, prolonged heating leads to the degradation of these compounds. Stir-frying involves higher temperatures, which accelerate the Maillard reaction, driving it more rapidly toward its final stage. Hierarchical clustering analysis (HCA) clustered the 38 volatile compounds and distinguished the fish sauce samples (Figure 3). The samples were divided into three clusters: F30 and F60, U, and B10, and F10, B30, and B60. This indicates that F30 and F60 shared similar aroma profiles, U resembled B10, and F10, B30, and B60 were like one another. The results of this study show that hot cooking significantly changes the flavor characteristics of fish sauce.  
Figure 2: Radar plot based on Electronic-nose (E-nose) data and representing the overall aroma profiles of fish sauce subjected to boiling (**A**) and stir-frying (**B**). U (unheated); B10, B30, and B60 (heated at 100 \({}^{\circ}\)C for 10, 30, and 60 min, respectively); F10, F30, and F60 (heated at 220 \({}^{\circ}\)C for 10, 30, and 60 s, respectively).
## 5 Passenger Vehicle Demonstrator for Low Speed Autonomy (PVLSA)
### Experimental Setup and Testing Methodology  
In order to validate the developed RRT-based trajectory planning algorithms, an automated driving demonstrator vehicle seen in Figure 12 was utilized. The vehicle is a stock Ford Fusion MY2018 test vehicle that is equipped with several additional sensors and computational hardware as well as custom software components. The basis hardware  
Figure 11: Architecture of the path planning for parking pipeline of the PVLSA Demonstrator.  
is the Dataspeed ADASKit [37], which enables the basic steering, braking, and acceleration controls of the vehicle along with access to all onboard sensors. Additionally, the vehicle has a dual-antenna Novatel Propak6 RTK-GPS system, a Continental AR5408 long-range RADAR, a Mobileye 630 intelligent camera, a Blackfly S GIGGE RGB camera, and an Ouster OS1-64 LIDAR sensor. The mounting positions of the perception sensors are indicated in Figure 12. For the implementation, the Autoware AI software stack [38] based on the ROS-1 middleware [39] was utilised. The components and their respective wiring scheme are shown in Figure 13.  
The validation of the RRT-based trajectory planning algorithms for parking was made using a set of scenario-based real-life tests. The test scenario is shown in Figure 14 as a typical parking situation in a static environment setting. The static environment in this context contains 10 parking spots, each one with dimensions \(3\times 5\) m and a distance of 8.2 m across the two rows.  
The KPIs were mainly chosen to analyze the trade-off between quality of results and computation time. In this respect, the KPIs are selected and grouped mainly to measure path quality and computation time. The selected Path-Quality KPIs are success rate, length of the planned path (in meters) and length of the planned path in reverse (in meters). The selected computation-time KPIs are execution time for the path planning step (in seconds), the number of nodes in the tree, and the number of collision checks performed.  
Figure 12: The PVLSA demonstrator vehicle (**a**) with its perception sensor equipment including LIDAR (**b**); RGB camera (**c**); intelligent camera (**c**), RADAR (**d**), and dual-antenna GPS with RTK (**e**).  
Figure 13: E/E Architecture and wiring scheme of the individual components of the PVLSA Demonstrator.
## 4 Experiments  
The performance of the proposed algorithm is verified using series of experiments, Note that only some representative experimental results are presented. MATLAB R2019b and Python VScode are used in this study as the experimental platforms.  
Imperceptibility and robustness are two important evaluation criteria of the watermarking algorithm. As mentioned in Section 3.6, PSNR and SSIM are used to evaluate the quality difference between the original and watermarked images. A large PSNR or SSIM corresponds to enhanced perception quality. The robustness of the algorithm is measured by calculating bit error rate (BER) and NC values. A small BER value or large NC value indicates high robustness. The size of the original image in our experiment is \(512\times 512\), and the size of the watermark image is \(32\times 32\), that is, 1024 bits of binary data.
## 2 TCAPLL Architecture
### IMU MEMS Error Modeling  
Generally, IMU sensor errors can be modeled by stochastic processes. Depending on the nature of the error (e.g., random or systematic, correlated over time or uncorrelated), different types of stochastic processes such as random constant, random walk, or first-order Gauss-Markov (GM) can be used to model it. Typical remaining uncompensated errors (after calibration) of an accelerometer and gyroscope include a turn-on to turn-on residual bias, axis misalignment, a residual scale-factor error, correlated noise and white noise. For high-grade IMUs, the manufacturer calibrates the INS extensively and compensates for these errors in its processor; therefore only small random errors remain. However, for low-cost MEMS inertial sensors, deterministic errors persist and can be roughly estimated by lab calibrations or manufacturer specifications [14]. Therefore, a solution to this problem is to include these errors as part of the functional model and to estimate them in a Kalman filter.  
Figure 1: TCAPLL architecture.  
In this paper, the MEMS sensor errors on each axis of the accelerometer and the gyroscope are modeled as [14]:  
\[\delta\omega_{ib}^{b}=b_{g}+\delta b_{g}+\omega_{b}SF_{g}+\eta_{g} \tag{1}\]  
\[\delta f_{ib}^{b}=b_{a}+\delta b_{a}+f_{b}SF_{a}+\eta_{a} \tag{2}\]  
where \(\delta\omega_{a}^{b}\) and \(\delta f_{a}^{b}\) are the total angular rate and force error in the body frame, \(\eta_{g}\) and \(\eta_{a}\) are white noise errors with spectral densities \(\sigma_{g}\) and \(\sigma_{a}\), respectively, and \(b_{g}\) and \(b_{a}\) are turn-on-to-turn-on biases, and are modeled as a random constant process, given by:  
\[\dot{b}_{i}=0 \tag{3}\]  
where '\(i\)' stands for '\(g\)' for gyroscope or '\(a\)' for accelerometer. \(\delta b_{g}\), \(\delta b_{a}\), \(SF_{g}\), and \(SF_{a}\) are the bias drifts and scale factor errors, and are modeled as first-order GM processes, given by:  
\[\delta\dot{b}_{i}=-\beta_{d_{i}}\delta b_{i}+\eta_{d_{i}} \tag{4}\]  
\[S\dot{F}_{i}=-\beta_{SF_{i}}SF_{i}+\eta_{SF_{i}} \tag{5}\]  
where \(\beta_{d_{i}}\) and \(\beta_{SF_{i}}\) are the inverse of the process correlation times, and \(\eta_{d_{i}}\) and \(\eta_{SF_{i}}\) are the GM process driving noises with spectral densities \(\sigma_{\delta b_{i}}\) and \(\sigma_{SF_{i}}\), respectively.
## 4 Cross Layer Adaptation of Check Intervals
### Linear Topologies  
In this topology the forwarding nodes are connected to exactly two neighbors: one child and one parent node. Each node receives packets from its child and forwards them to its parent to reach the destination node. Since the communication delays due to medium contention are related to the number of neighbors, for this particular topology we can estimate the value of \(\gamma\) to be approximately constant  
Figure 5: Behavior of CLAC in two different scenarios: on the left, the ideal case with no contention delays. On the right, a realistic case where delays are introduced at each hop. CLAC adjusts the next check interval in the receivers. In the figure \(\tau\) stands for the time of transmission/reception of a packet of length \(s\) on the medium.  
for each node on a path. We define \(\gamma\) in terms of the check interval and a parameter \(p\) that expresses the fraction of \(\tau_{check}\) by which the check interval should be shifted (to bring it forward or backward):  
\[\gamma=p\times\tau_{check}/100 \tag{6}\]  
For instance, given a check interval of \(200\) milliseconds, if \(p\) takes the value of 5, then the delay introduced by each packet at each node will be of 10 units of time (expressed in the same units that the check interval). The delay introduced by a packet at node \(i_{k}\) depends on its position within the path and increases linearly with the number of hops. This approach makes sense for linear topologies where each node redirects the data to one single neighbor.
###### Abstract  
Automated document processing and circulation systems face critical challenges in achieving reliable retrieval accuracy and robust classification performance, particularly in security-critical organizational environments. Traditional approaches suffer from fundamental limitations, including fixed fusion strategies in hybrid retrieval systems, inability to model inter-document relationships in classification tasks, and lack of confidence estimation for result reliability. This paper introduces AttenFlow, a novel context-aware architecture that revolutionizes document management through two core technical innovations. First, we propose the retriever consensus confidence fusion (RCCF) method, which addresses the limitations of conventional hybrid retrieval approaches by introducing consensus-based fusion strategies that dynamically adapt to retriever agreement levels while providing confidence estimates for results. RCCF measures the consensus between different retrievers through sophisticated ranking and scoring consistency metrics, enabling adaptive weight assignment that amplifies high-consensus results while adopting conservative approaches for uncertain cases. Second, we develop adversarial mutual-attention hybrid-dimensional graph attention network (AM-HDGAT) for text, which transforms document classification by modeling inter-document relationships through graph structures while integrating high-dimensional semantic features and low-dimensional statistical features through mutual-attention mechanisms. The approach incorporates adversarial training to enhance robustness against potential security threats, making it particularly suitable for critical document processing applications. Comprehensive experimental evaluation across multiple benchmark datasets demonstrates the substantial effectiveness of our innovations. RCCF achieves improvements of up to 16.9% in retrieval performance metrics compared to traditional fusion methods while providing reliable confidence estimates. AM-HDGAT for text demonstrates superior classification performance with an average F1-score improvement of 2.23% compared to state-of-the-art methods, maintaining 82.4% performance retention under adversarial attack scenarios. Real-world deployment validation shows a 34.5% reduction in manual processing time and 95.7% user satisfaction scores, establishing AttenFlow as a significant advancement in intelligent document management technology.  
document processing; consensus-based retrieval fusion; graph attention networks; adversarial training; context-aware architecture
## 7 Conclusions and Future Work  
We present a novel real-time and robust routing mechanism that is designed to be adaptive to emergency applications such as building fire hazards. The probability of end-to-end real-time communication is achieved by maintaining a desired delay based on a message propagation estimate and power level adaptation. The design is be adaptive to realistic hazard application characteristics including fires expanding, shrinking and diminishing. Our routing mechanism is designed as a localized protocol that makes decisions based solely on one-hop neighborhood information. Our ns-2 simulation results prove that the EAR routing mechanism achieves a good real-time packet delivery adaptive to fire emergency when compared with other related works. We have implemented our protocol into a 4-node TinyOS testbed. Future work will include implementation on a 100-node testbed we have deployed at our university to monitor and help to handle building fires.
Many kinds of instruments can obtain these measurements based on two principles of operation: light scattering and electric field. The differential mobility analyzer (DMA) used in conjunction with a particle counting system (condensation particle counter (CPC) or electrometers), called the Scanning Mobility Particle Sizer (SMPS), is the most common technique employed for long-term characterization of the atmospheric sub-micrometric aerosol fraction [1]. With this equipment, the total number of particles (Nt) in the size range measured is obtained, the daily averages ranging between a few hundred and over 50,000 cm\({}^{-3}\)[3]. In order to assess the source apportionment of eBC, the absorption Angstrom exponent (AAE) can be used as a source specific parameter to distinguish between wood smoke and diesel exhaust aerosols. Wood smoke contains aerosolized substances that strongly absorb in the blue and ultraviolet (UV) part of the light spectrum and may not absorb in the infrared (IR) parts of the spectrum; that is, brown carbon (BrC) [22]. High aerosol absorption at low wavelengths leads to high AAE values [7]. For wood smoke or smoke resulting from the combustion of biomass, an AAE of around 2 is expected [8], although much higher AAEs have been observed, for example, for the smoldering combustion of peat [23]. Fresh diesel exhaust has an AAE close to 1 [24]. The west of Spain lacks studies on eBC, on eBC in combination with Nt, and on source apportionment of eBC. However, there is a traditional use of wood in this region as an energy source for domestic heating in single family homes outside urban areas. In fact, the Galician Autonomous Government (in the northwest (NW) of Spain) has encouraged the use of biomass for this end. Taking all the previous information into account, the aim of this study was (i) to evaluate eBC levels and their temporal variability in a residential area in the NW of Spain, (ii) to assess the previous information with data related to Nt, and (iii) to identify eBC primary local and regional sources of emissions from fossil fuel combustion and biomass burning. For this purpose, (i) we carried out a sampling campaign from January 2015 to December 2016, and (ii) we developed new software features/functionalities for the SCALA(r) software application, which was the atmospheric aerosol data management software used to carry out this work.