esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Measurement of Particle Contact Curvature and Young's Modulus Using X-ray \(\mu\)CT
## 4 Experimental Protocol
### X-ray \(\mu\)CT Instrument  
An in-situ loading apparatus was purposefully built which positions the sample to be scanned by the X-ray instrument. The X-ray instrument consists of a 120 keV transmission X-ray source with a diamond coated W target, a 4 MP flat panel GADOX (Gadolinium Oxysulfide) X-ray camera and an air-bearing rotary table on which the in-situ loading apparatus is mounted. A complete scan is accomplished through 360\({}^{\circ}\) rotation which takes approximately 1 h to collect 1000 radiographs. The in-situ loading apparatus and the sketch of the loading apparatus are shown in Figure 6. As can be seen, the press base of the loading frame is attached to the rotary table, which enables the particle to be rotated during an X-ray scan. The particle is placed between two boron nitride (BN) pistons in the loading frame and can be pressed to a maximum load of 100 N by means of a manual micrometer screw thread. The plastic pillar is made of polycarbonate, which is transparent to X-rays. A recess of the plastic pillar was intentionally built so as to reduce the energy loss of X-rays although the plastic pillar is X-ray transparent. The load is measured by means of a strain gauge load cell situated below the lower BN piston. The load cell was provided by OMEGA's LCMKD series with the loading capacity of 100 N.  
The particle is placed in a small channel of the in-situ loading rig, which levels up with the X-ray source. These particles were provided by CWK Bad Kostriz Gmbh and the specification of tested zeolite particle could be referenced elsewhere [30]. The particle was fed into the channel and increasingly compressed under displacement control loading by screwing the barrel on top of the loading rig. The applied load was recorded by the force transducer placed beneath the bottom plate and the value of the applied load was monitored by the meter reading at every loading stage. In this work, the zeolite particle was first scanned by the X-ray instrument to inspect the initial state of internal structure. Then the zeolite particle was loaded until the loading force of 6 N was reached, where the zeolite particle was scanned again by X-ray \(\mu\)CT. The morphology of the zeolite particle was used to compute the contact curvature by 2D and 3D fitting methods. However, the purpose of X-ray \(\mu\)CT, apart from what is used to measure the contact curvature, serves to examine the microstructure of zeolite particles, and to visualize the progressive failure process subject to compressive loading. More details of the progressive deformation of the zeolite particles can be found in our previous work [34] or the literature work [19] which describes the aid of X-ray \(\mu\)CT.
## 3 Design of Hardware and Software for the Data Monitoring System
### Design of Software for the Data Monitoring  
Computer language C++ [39] has been used for the development of all algorithm programs. All the programs are designed using Arduino commands and Wire library (I\({}^{2}\)C communication) [40]. In this Section we detail libraries created especially for the data-logger.
## 4 Design and Stability Analysis of Surrounding Rock-Supporting Structure of Shaft
### Analysis of Original Supporting Structure Calculation Results  
4.5.1. GRC, SCC, and LDP Curves  
The section 0.5 m from the working face is selected for calculation. The original support plan is calculated using the convergence-constraint method to obtain the surrounding rock GRC, SCC, and LDP curves, as shown in Figure 11 for the two original support schemes. The SCCs of the two schemes do not intersect with the surrounding rock GRCs when using the strain-softening model. This indicates that the designed support structure of the pristine support schemes 1 and 2 cannot meet the shaft support requirements. Field investigation showed that the supporting structure of the rock generated a failure, indicating that the strain-softening model calculation results are consistent with actual breakage conditions in the field. When calculated using the ideal elastic-plastic model, the original scheme of support type 1 reached the yield state, which cannot meet the support requirements. However, the SCC of the original support scheme 2 is confluent with the GRC and the support structure does not reach the yield state. The support structure should be in the safe carrying capacity range, which indicates that the original support can meet the support requirements, but the field support structure had actually undergone a large destruction. There are some differences between the results calculated using the ideal elastic-plastic model and the actual situation in the field.
## 2 Methods
### Ecological Characterization of Trend Shifts  
After having detected a trend shift within the time series, a main challenge is interpreting the changes. One approach is the characterization of the shifts into classes, which are expected to have different driving processes and ecological implications. Six major classes (referred to as _trend types_) are therefore defined as follows: two classes for monotonic change (increasing or decreasing), two for interrupted trends and two for trend reversals (greening to browning or vice versa). In Figure 2, these are represented by the three rows (top to bottom), while the columns separate recent greening from recent browning. In addition to these trend types, individual segments may be stable (_i.e._, in steady-state equilibrium), meaning that no substantial change was detected during the corresponding period. A threshold of 0.25%/year was used, below which trends were regarded to be stable. Cases with stable segments are not shown but automatically imply monotonic change in the other segment.  
Each of the trend types can be induced by a wide range of underlying processes and likely by a combination of more than one process. Some types, however, can be attributed to certain processes with higher likeliness than others. For example, in our case of long observation periods (_i.e._, 30 years), monotonic changes are likely induced by changes in growth-limiting climatologies [3], although persistent land-use change (e.g., forest-crop conversion and urbanization) may also be considered as a candidate driver [36]. In water-limited environments, an abrupt increase in vegetation activity (or _burst_), followed by a gradual decrease, may indicate response of vegetation to a relatively wet period. The wet period may, for instance, be induced by oceanic oscillations, like the El Nino/La Nina Southern Oscillation (ENSO). In higher northern latitudes, the same type of trend change is more likely a result of few anomalous warm years with less snow-covered days. An abrupt negative change (or _setback_), on the other hand, may be attributable to human interventions (e.g., logging followed by regrowth).  
Natural drivers, in such cases, may be wildfire or diseases followed by a period of gradual recovery. These examples include short-lived events to cause a burst or setback in the gradual trend. From a global change perspective, the cases where gradual trends reverse may be most interesting. This type of sustained trend changes may indicate major shifts between greening and browning regimes. Such shifts were suggested in literature [9], but also debated [10, 11].  
Apart from these biophysical candidates, trends shifts may have been induced by data artifacts, including differences between AVHRR-2 and AVHRR-3 or by NOAA platform changes [14, 37]. However, we did not find a relationship with AVHRR sensor changes and there is a sound basis to assume that such events have been effectively corrected [20, 38].
## 3 Results and Discussion
### Sensing Mechanism  
In semiconductor radiation sensor operation, incident radiation creates a charge pulse consisting of electrons and holes within the detector, which are then separated using an applied electric field and the current is sensed by an external circuit. Electromagnetic radiation can interact with a material via different mechanisms [1]: (i) elastic scattering, (ii) photo-electric absorption, (iii) Compton scattering, and (iv) pair production. However, the dominant mechanism and the result of the interaction strongly depend on the incident energy of the interacting X-ray beam and the composition of the sensor [42]. In the case of elastic scattering, the energy of the incident photon is not changed and this process does not contribute to the deposition of energy on the sensor. Photoelectric absorption is the ideal process for sensor operation, where the incident photon loses all of its energy upon incident with the sensor material to one of the orbital electrons of the atoms of the sensor material. Subsequently, this photoelectron loses its kinetic energy through Columbic interactions and creates plenty of electron-hole pairs, which are subsequently separated by the electric field, and a current is produced [41; 43]. During Compton scattering, which is a collision between an incident photon and an orbital electron, both the energy and the direction of the photons change. In fact, some of the energy will be lost to the electrons of the sensing material. These electrons will then lose their energy through the generation of electron-hole pairs. In Compton scattering, a photon does not transfer all of its energy to an electron. Accordingly, the number of electron-hole pairs generated varies remarkably between different Compton events [1]. In the pair production process, a photon with high energy interacts within the Coulomb field of the nucleus and directly produces an electron-positron pair. Generally, the photoelectric effect dominates in the energy range to \(\sim\)200 keV, the Compton effect to a few MeV and pair production above \(\sim\)6 MeV [44].  
The SnO\({}_{2}\) NW is a semiconductor material which can directly convert ionization radiation into an electrical signal. In this study, X-ray irradiation was used to demonstrate the radiation sensing capability of SnO\({}_{2}\) NW. When an X-ray traverses the sensor, it may partly or fully transfer its energy to electrons via the photoelectric effect or Compton scattering [45]. These primary energetic electrons may cause an ionization cascade until their energy is in equilibrium with the energy band gap of the media. This leads to the creation of several electron-hole pairs in the sensor. Signal intensity strongly depends on the collection efficiency of these electron-hole pairs. The collection probability is in competition with electron-hole recombination, which is related to carrier lifetime or defect density.
# Short-Term Traffic Flow Prediction Based on CNN-BILSTM with Multicomponent Information
## 2 Literature Review
[24] determined the spatiotemporal characteristics of traffic flow data using a selection algorithm and converted the data into a two-dimensional matrix to serve as an input for the CNN model for feature learning and prediction. [PERSON] et al. [25] combined CNN and LSTM to extract the spatiotemporal information of the traffic flow and utilized the BILSTM model to analyze the historical traffic flow of the target detection point for obtaining periodic features. When the CNN or LSTM model is used to process the data alone, the characteristics of the CNN model determine its advantages in processing the spatial characteristics of the data. The LSTM model has advantages in processing short-term time series data. Facing long-term time series data, LSTM cannot fully exploit the impact of future data on model training, because it can only perform forward propagation. As CNN has been expanded to the graph convolutional neural network (GCN), numerous types of research have been conducted for learning the characteristics of traffic flow graph structure data on account of spectrogram theory [26]. [PERSON] et al. [27] came up with a hybrid graph convolutional network (HGCN), combined with weather conditions, to forecast the traffic flow of highway detection points,and [PERSON] et al. [28] advanced an LSTM network model on account of graph and attention mechanisms, using GCN to capture the spatial features of traffic flow, employing the LSTM model to extract temporal features, assigning weights to the local correlation size of temporal and spatial features through an attention mechanism, and achieving better performance results in training on actual datasets. Although the GCN model has been the frontier in recent years, it was not suitable for the data prediction of this study, because the location distribution of the data detection points in this study was relatively regular, and it is not suitable for graph structure processing; that is, it does not need to use the Laplace matrix. The adjacency matrix is used to express the direct connection relationship between nodes, so as not to increase the complexity of the model. Compared to the existing traffic flow prediction models, this study uniquely came up with a new CNN and BILSTM hybrid model to forecast traffic flow with the establishment of matrices describing the traffic flow and speed, respectively. In addition, we applied weighted fusion to obtain the periodic law of the traffic flow data based on the training and prediction of weekly and daily periodic data.
# Improved Surprise Adequacy Tools for Corner Case Data Description and Detection
## 4 Experiments and Evaluation
### More Datasets Analysis  
(1) CIFAR10 dataset  
CIFAR 10 is another commonly-used benchmark dataset for image recognition. It contains 60,000 \(32\times 32\) color images in 10 classes [26]. Similarly, a CNN model with 6 convolution layers is trained for CIFAR image classification. Then, as the proposed method above, we can calculate values of four kinds of DSA definitions based on the last layer of the trained DL models. Images with large DSA values are drawn in Figure 10.  
From the results of Figure 10, we can see images with large DSA values have low brightness which may cause misclassification, which means that they have a high risk to be corner cases. To evaluate if these images are real corner cases, we also plot accuracy and corner case coverage performance vs. DSA values, as Figure 11.  
Based on the results of Figure 11, it is also seen that, when taking large DSA values as a threshold, there are a large percentage of misclassified points leading accuracy to be low (as Figure 11a), inversely the coverage of corner cases (misclassified points) to be high (as Figure 11b). Therefore, it implies that DSA can capture corner cases in testing data. Moreover, through the comparison of the 4 kinds of DSA, this phenomenon is more obvious on DSA3, which illustrates that DSA3 has a better performance on corner case detection compared with the other three.  
(2) Real-world engineering dataset  
To further study the feasibility and generalization ability of the proposed method, we also propose to apply the proposed DSA method to detect corner case data in a real-world application. Here, the metal casting product image data is taken for case study, which is applied for industrial quality inspection in the real world [41]. It has a total of 6633 training  
Figure 11: Performance of corner case detection in CIFAR10. (**a**) Accuracy (**b**) Corner case coverage.  
Figure 10: (**a–d**) Images in CIFAR10 with large DSA values.  
From the results of Table 3, we find a similar conclusion as that obtained on MNIST data that the proposed DSA modifications can improve the capability of the original DSA on describing corner case data's behaviors, meanwhile, the DSA3 performs relatively better than others. Moreover, as the layer becomes deeper, the calculated DSA has better performance on corner case data detection, seeing that AUC(Layer3) > AUC(Layer2) > AUC(Layer1).
# Comprehensive Optimization of Air Quality in Kitchen Based on Auxiliary Evaluation Indicators
## 5 Conclusions  
### Study Limitations and Future Perspectives  
While this study achieves significant IAQ improvement through the Q-index framework, two limitations warrant attention:  
1. Experiments and simulations focused on an enclosed kitchen without examining interactions with adjacent spaces (e.g., open-plan kitchens integrated with dining/living areas). Future work should investigate multi-zone ventilation synergy and pollutant migration pathways.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Conditions**} & **100\%-** & **Forehead** & **Subnasal** & **Inhalation** & **Entire** & **Inhalation** & **Entire** & \\  & **ADPI** & **PMV** & **PMV** & **Area CO2** & **Kitchen** & **Area COFP** & **Kitchen** & **Q** \\  & & & **(ppm)** & **CO2 (ppm)** & **(\(\mu\)g/m3)** & **COFP (\(\mu\)g/m3)** & \\ \hline Case 7 & 60.61 & 1.572 & 1.271 & 625.1 & 657.3 & 25.29 & 27.62 & 1.260 \\ Case 8 & 58.89 & 1.591 & 1.390 & 613.1 & 650.7 & 24.43 & 27.49 & 1.257 \\ Case 9 & 52.50 & 1.465 & 1.223 & 594.4 & 651.1 & 19.52 & 26.02 & 1.152 \\ Case 10 & 52.22 & 1.461 & 1.220 & 588.0 & 648.2 & 22.07 & 26.37 & 1.154 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Average air quality indicators and evaluation index for different conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \begin{tabular}{c} **Comparison** \\ **Group** \\ \end{tabular} & **F-Statistic** & \(p\)**-Value** & **Significant** & **Mean Q \(\pm\) SD** \\ \hline Air supply angle & F = 37.2 & \textless{}0.001 & \begin{tabular}{c} Case 4 vs. all \\ others \\ \end{tabular} & \begin{tabular}{c} Case 1: 1.634 \(\pm\) 0.04 \\ Case 4: 1.209 \(\pm\) 0.03 \\ \end{tabular} \\ \hline Airflow rate & F = 29.8 & \textless{}0.001 & \
\begin{tabular}{c} Case 9 vs. 7, 8, \\ 10 \\ \end{tabular} &
\begin{tabular}{c} Case 9: 1.152 \(\pm\) 0.02 \\ Case10:1.154 \(\pm\) 0.03 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 12: ANOVA and Tukey test results for Q-index comparisons.  
Optimization targeted COFP and CO2, excluding volatile organic compounds (VOCs) and ultrafine particles (UFPs) prevalent in cooking emissions [1; 3]. Expanding monitoring to include formaldehyde, PM2,5, and VOCs would enhance Q-index comprehensiveness.
3. The Q-index methodology shows promise for broader application. Future research should explore its adaptation and validation in diverse indoor environments with high air quality demands and vulnerable populations, such as kitchens in elderly care facilities, schools, hospitals, and daycare centers, where pollutant exposure and thermal comfort are critical public health concerns. Investigating the transferability of the weighting scheme and optimization approach in these contexts could significantly extend the societal impact of this work.
## 2 Optical Coherent Detection Technique  
The optical coherent detection techniques consist of a Mach-Zehnder interferometer or Michelson interferometer. In a Mach-Zehnder interferometer, an optical signal is divided into two parts with a beam splitter. These two beams travel a distance of \(L_{1}\) and \(L_{2}\) in two orthogonal arms of the interferometer and are combined at another beam coupler, as shown in Figure 1. The two signals are made to travel exact equal distances, so that the total path difference between these beams is always zero.  
Mirrors M1 and M2 are used to fine tune the total path difference between the two beams. Once the path difference (i.e., \(\Delta L=L_{1}-L_{2}\)) is zero, the phase difference between the two beams becomes constant or zero, as the phase difference \(\Delta\varphi\) is directly proportional to the path difference [26]. The interference pattern is observed at the photo detector. The phase difference (in radians) between the two beams is given by,  
\[\Delta\varphi=\frac{2\pi}{\lambda}\Delta L \tag{1}\]  
Figure 1: Mach–Zehnder Interferometer.  
If the two beams travel an equal path, the path difference between them will be either zero or constant. It will lead to a constant phase difference between the two beams [27]. The condition for constructive interference is given by,  
\[\Delta L=n\lambda\ \ \ for\ n=0,1,2\ldots \tag{2}\]  
The condition of destructive interference is given by,  
\[\Delta L=(n+1)\frac{\lambda}{2}\ \ for\ n=0,1,2\ldots \tag{3}\]
## 2 Background and Related Work
### K-means Cluster Analysis Based on Error Rate  
As shown in Figure 3, 3D-NAND flash has a characteristic that the number of errors is different for each page and layer. The reliability of 3D-NAND flash memory can be improved by applying a clustering algorithm to classify according to this characteristic and using a data protection policy suitable for the characteristics of each cluster.  
There are various types of clustering algorithms, such as k-means, mean shift, Gaussian mixture model, and DBSCAN. Among them, the graph in Figure 3 was used as input data and classified using the k-means algorithm, which is easy and concise, and generally used frequently (see Figure 4) [17]. The \(x\)-axis in Figure 4 represents layers from 0 to 1. A value closer to 0 means the lower layer and a value closer to 1 means the upper layer. The \(y\)-axis indicates the number of error occurrences as Fail bits, and it is expressed as a relative value from 0 to 1 as in Figure 3.  
In the k-means algorithm, the number of clusters is determined according to an arbitrary \(k\) value determined by the user, and similar clusters are classified only by the distance between the input data. The k-means algorithm seeks a set of data that minimize the variance of each cluster. Looking at how the k-means algorithm classifies clusters, the user determines the center point of the clusters as much as the \(k\)  
Figure 4: Number of error occurrences according to the layer that the k-means algorithm is applied.  
Figure 3: Number of error occurrences according to different layers and pages at the end of endurance test [1].  
value based on the first input data (here, the data in Figure 3). The data closest to each centroid are then classified into the same cluster using the Euclidean distance formula. The previously determined \(k\) center points are repositioned in the center of the newly classified cluster. Until the center point cannot be moved any further, the data closest to each center point are newly classified into the same cluster, and the process of moving the center point is repeated.  
We adopt a heuristic way of increasing \(k\) by 1 to find an appropriate \(k\). In this way, we obtain \(k\) values with clear error rate cluster identification. Result of classification, when the \(k\) value was 6 (the number of clusters was 6), areas according to the error rate were clearly identified.