esa-sceva/satcom-chunk-collection huggingface dataset:
# Development Trend of Electronic Nose Technology in Closed Cabins Gas Detection: A Review
## 3 Concept and Principle of the Electronic Nose
### Comparison of Electronic Nose and Other Methods  
Early detection of volatile compounds in closed cabins relied mostly on gas chromatography [55; 56] and sensory analysis [57], which are traditional methods. These methods, which require much labor and material resources, have many technical and evaluation limitations. Therefore, electronic nose technology provides an electronic identification technique and reliable information, mainly for the identification of different gases in closed cabins. The main advantage of the electronic nose is that it requires only an upfront investment in calibration, after which it can be continuously detected at minimum cost. Sensor drift, detection sensitivity, and system stability are the main problems in industrial e-nose applications [58]. The main limitation of the electronic nose is that it does not provide detailed information on volatile compounds [15]. However, the main need for gas detection in closed cabins is a timely response to the detected gas, so the electronic nose can be promoted in the field of closed cabins.  
In general, electronic nose sensors require different recognition modes to cope with unused gases. Although sensory analysis is performed by a team of professionals, manual measurements still cannot eliminate subjective influences. Gas chromatography methods require significant financial resources and cannot achieve real-time detection [59]. Both of these methods do not apply to the interior of closed cabins. Therefore, the electronic nose as a reliable detection technology can be vigorously developed in closed cabins. Because of the complex composition of volatile compounds in closed cabins, the electronic nose needs to be appropriately adjusted to different sensor arrays and recognition modes in the face of different needs. The electronic nose incorporates human perception into the detection criteria [60]. This interaction is achieved by the use of sensors. E-nose has great potential in closed cabins and has obvious advantages over other methods.
## 4 Discussion
### Parameters Analysis  
CTS can distinguish well between shadows and dark ground objects in images, avoiding the shortcomings of MC3, which only utilizes spectral features. However, there are still some factors that may lead to inaccurate detection results:  
1. The number of over-segmented superpixels \(n_{o}\). The multi-scale segmentation of images ensures the continuity and integrity of shadows, but it may still face a situation where shadows and non-shadows are mixed within the same object, especially for fine shadows and shadow edges. When the \(a_{m}\) is certain, this situation is related to \(n_{o}\). If \(n_{o}\) is too small, the resulting superpixels are more likely to contain both shadows and non-shadows; if \(n_{o}\) is too large, the difference between shadow superpixels and non-shadow superpixels is not significant enough, meaning that they are more likely to be merged into the same object. Therefore, a suitable \(n_{o}\) can avoid the problem as much as possible. Incorporating shadow features into superpixel segmentation similarity metrics may be able to fundamentally solve the problem, which will be studied in our future work.
2. The weight of the DEM rough shadow mask \(w_{\text{dem}}\). The fusion weights of the SPM and the MC3 mean map are also particularly important to the detection results. The overall accuracy \(\tau\) changes in Images 7-12 under different \(w_{\text{dem}}\) are illustrated in Figure 14. The overall accuracy for all six images reaches its highest at \(w_{\text{dem}}=0.2\), which basically stays around 95%. When \(w_{\text{dem}}\) tends to 0 or 1, i.e., only the MC3 mean map or only the SPM is considered, the overall accuracy decreases. It falls below 75% at \(w_{\text{dem}}=0\) in Images 7, 11, and 12, and Image 8 also falls to 75% at \(w_{\text{dem}}=1\). Therefore, it can be seen that adding the terrain factor has a positive enhancement effect on image shadow detection, confirming the good performance of CTS.
3. The number of multi-thresholds \(c\). When performing histogram thresholding segmentation, the setting of the number of multi-thresholds \(c\) is also an important factor that affects the detection accuracy. If \(c\) is too small, it can result in dark objects, such as vegetation, water bodies, and soil, close to shadows being classified as shadows; if \(c\) is too large, it can lead to true shadows being missed. Figure 15 shows the overall accuracy changes of the six images under different \(c\). When \(c=1\), the \(\tau\) is very low for all six images, whereas when \(c=2\), the \(\tau\) improves drastically. For Image 8, when \(c>3\), the overall accuracy decreases, indicating that a threshold that is too high leads to partial shadow
## Results
### VIS/NIR Spectrometer for TSS Detection of Orange
#### 3.1.3 Feature Wavelength Extraction
As shown in Figure 5, the R\({}^{2}\), RMSE, and slope for the calibration set of PLSR detection are 0.8627, 0.3839, and 0.8627, respectively, for the validation set of PLSR detection are and 0.6807, 0.6920, 0.9212, respectively. Thus, it is feasible to detect orange TSS based on based on VIS/NIR reflection spectrometer, however, the detection accuracy needs to be further improved.
# Optimal Resource Allocation Policies for Multi-User Backscatter Communication Systems
## 2 System Model
### Tag Configuration
#### 2.2.2 Semi-Passive Tag  
For a semi-passive tag, we assume that the battery does not have energy at the beginning. First, the \(i\)-th semi-passive tag enters the sleep state to harvest energy while consuming negligible circuit power over sleep time \(t_{i}^{s}\)[8]. For symbol simplification, the definitions of the semi-passive tags case are the same ones as those of the passive tags case. The stored energy of the \(i\)-th semi-passive tag in the sleep state (denoted by \(E_{i}^{s}\)) is given as \(E_{i}^{s}=\eta_{i}^{Prec}t_{i}^{s}\). Then, the \(i\)-th semi-passive tag operates in the active state over the remaining time \(t_{i}^{a}\), satisfying \(t_{i}^{a}=T-t_{i}^{s}\), which is shown in Figure 2. The other definitions of the \(i\)-th semi-passive tag during the active state can refer to those of the passive tags case.
## 2 Preliminary Work
### Theoretical Foundation
Therefore, the VTW of the payload to point \(L\) is [\(T_{a}\), \(T_{b}\)] [33]. The specific calculation steps can be divided into three parts: the coordinate transformation, calculation of angle \(\beta\), and determination of the critical angle \(\beta_{0}\) and corresponding time \(T_{i}\). Firstly, to maintain consistency in the coordinate systems and facilitate the calculation of the angle \(\varepsilon\) and \(\beta\), the latitude and longitude of the ground points need to be converted into three-dimensional coordinates in the Earth-centered inertial coordinate system, according to the conversion formula shown in Formula (1) [34]. \[ \begin{array}{c}x_{L}=r\cos(lat_{L})\cos(lon_{L})\\ y_{L}=r\cos(lat_{L})\sin(lon_{L})\\ z_{L}=r\sin(lat_{L})\end{array}  \tag{1}\]  
where \(r\) represents the radius of the Earth, and \(lat_{L}\) and \(lon_{L}\), respectively, denote the latitude and longitude of the ground point \(L\). Based on the unified coordinates of the two points, the angle \(\varepsilon\) and \(\beta\) can be calculated using Formula (2). \[ \begin{array}{c}\varepsilon=\arccos(\frac{\mathbf{u}\cdot\mathbf{v}}{\|\mathbf{u}\|-\| \mathbf{v}\|})\\ b^{2}=(h+r)^{2}+r^{2}-2\cdot(h+r)\cdot r\cdot\cos(\varepsilon)\\ \beta=\arcsin(\frac{r}{\beta}\cdot\sin(\varepsilon))\end{array}  \tag{2}\]  
Here, \(\mathbf{u}=(x_{T}\), \(y_{T}\), \(z_{T}\)) denotes the geocentric coordinates of the sub-point, \(\mathbf{v}=(x_{L},y_{L},z_{L})\) represents the geocentric coordinates of the ground point, \"\(\cdot\)\" denotes the dot product operation, and \(\|\cdot\|\) represents the magnitude of the vectors. \(h\) and \(r\), respectively, stand for the orbit altitude and the radius of the Earth. Based on this, critical angle determination is performed. Starting from the initial moment, the angle \(\beta\) is computed continuously at each moment, recording the time when each orbit's \(\beta_{Ti}=\alpha\)  
Figure 2: Schematic diagram of VTW calculation principle. as shown in Formula (3). The \(T_{i}\) moments are merged, and the earliest and latest \(T_{i}\) are selected to obtain the VTW [\(T_{is}\), \(T_{ie}\)] for each orbit regarding region N.  
\[\beta_{Ti}=\alpha \tag{3}\] \[VTW_{n}=[T_{is},T_{ie}]\]  
By employing the previously described angle and orbit calculations, the VTW of the payload for the target can be computed, utilizing the input target position and spacecraft orbit information. The subsequent models are all constructed on the foundation of this theoretical calculation.
## 2 Materials and Methods  
### Datasets  
The SAR image utilized in this study was sourced from Sentinel-1, presenting a single polarization intensity image acquired in IW mode with geographic coding, registration, and denoising processing applied. The images boast a resolution of 5 m \(\times\) 20 m. The study area encompasses water bodies such as lakes, rivers, streams, and ponds, as well as landform features including plains, mountains, forests, and shrublands, providing abundant data support for research on water body extraction. Given the scarcity of publicly accessible high-resolution SAR datasets, a custom PAS-CAL VOC 2012 dataset was crafted for this research. Following validation by SAR experts, the water areas were delineated as water bodies and other regions as background using Labelme software (version 5.2.0). Initially, a sliding window technique was employed to partition the large-scale SAR image into \(512\times 512\) pixels segments. To augment the dataset adequately, data enhancement strategies such as horizontal and 90-degree clockwise rotations were implemented to enhance the sample quality, yielding a total of 10,200 samples. Figure 1 shows two samples of SAR images and their ground truth (GT) in diverse scenes. The dataset was randomly split into training and testing sets at an 8:2 ratio, comprising 8816 training samples and 2204 testing samples. Each SAR image in the dataset is associated with a binary label, with blue denoting water and black representing background. Furthermore, three SAR images with a resolution of 2000 \(\times\) 2000 pixels were earmarked for independent testing purposes and excluded from the dataset.  
Figure 1: The sample of datasets. (**a,b**) SAR images; (**c,d**) ground truth.
# Spectrum Allocation Using Integer Linear Programming and Kerr Optical Frequency Combs
## 2 Materials and Methods
\[\max f_{o}=\sum_{k}\sum_{m}\sum_{\omega}x_{km\omega}\cdot pp_{\omega}+\sum_{ \omega}t_{k\omega}\cdot 100 \tag{4}\]  
where \(k\) represents the required requests, \(m\) represents the available channels for allocation, and \(\omega\) denotes the modulation for transmission and reception (QAM, 8-QAM, and 16-QAM). \(pp_{\omega}\) is a prioritization variable determining the weight of each request depending on the modulation. The binary variable \(x_{km\omega}\) controls the allocation of channels to a specific client, while the binary control variable \(t_{k\omega}\), identifies active users in the allocation process. To prioritize the maximum number of requests effectively, a weight of 100 is assigned to \(t_{k\omega}\). This approach ensures that the system prioritizes user requests over bandwidth in the allocation process. The number 100 was chosen because it is sufficiently large to assign significant weight to each user in the cost function. A smaller number might not effectively prioritize users, potentially reducing the number of clients allocated, while a  
Figure 2: Photonic structure for OFC generation. larger number would have a similar effect on the cost function. Essentially, when a client is not allocated, they receive a penalty of 100 to reinforce the prioritization in allocation. The model includes nine constraints regulating the system's operations. These constraints aim to assign one channel for each request (Equation (5)), ensure only one modulation for a channel (Equation (6)), ensure only one modulation for a request (Equation (7)), guarantee the required bandwidth for modulation (Equation (8)), and ensure the channel meets the required distance \(d_{k}\) for the request and \(P_{\omega m}\). It ensures that the allocation of a channel and a modulation does not exceed the maximum capacity of distance for each modulation (Equation (9)), ensures the required lines \(n_{k}\) for a request are obtained (Equation (10)), checks for consecutively assigned channels (Equation (11)), limits one modulation per user (Equation (12)), and ensures continuity in the assignment for a request requiring multiple channels (Equation (13)). Here, \(y_{p}\) and \(y_{n}\) represent the rising and falling edges, respectively. If the channel is contiguous, it should have only two edges. Please refer to Table 1 for the constraint equations.
## 3 Model Architecture
### Error Evaluation  
The normalized Root Mean Square Error (\(nRMSE\)) and Mean Absolute Percent Error (\(MAPE\)) are used to evaluate the prediction methods, which are calculated as follows:  
\[nRMSE=\frac{1}{P_{rated}}\sqrt{\frac{1}{n}{\sum_{i=1}^{n}{\left(P_{mi}-P_{pi} \right)^{2}}}} \tag{14}\]  
\[MAPE=100\times\frac{1}{n}{\sum_{i=1}^{n}{\frac{\left|P_{mi}-P_{pi}\right|}{P_{ mi}}}} \tag{15}\]  
where \(n\) is the number of time periods for power generation; \(P_{rated}\) is the rated power; \(P_{pi}\) is the predicted power in the \(i^{th}\) time period; and \(P_{mi}\) is the measured power in the \(i^{th}\) time period. In order to avoid the nighttime data, we disregarded the data set where both the solar radiation and PV generated power data are equal to zero.
# Smart Roads for Autonomous Accident Detection and Warnings
## 3 Proposed Accident Alert Light and Sound System
### A Way to Smart Roads (SRs)
First, it blinks the light continuously until the system is reset; secondly, it sounds the siren to alert drivers of approaching vehicles so they can take measures to avoid an MVC; and third, it sends a message through the HC-12 transmitter to the immediately adjacent node. A message contains three types of information: the pre-saved location of the detecting node, the direction of traffic on the road, and fire information (If recorded). Communication between nodes is one way, i.e., back toward oncoming vehicles to make their drivers aware of the incident ahead. Every node does the same job in case of accident detection until the message reaches the EOC. When the EOC receives an accident message, it sends an ambulance to the incident location and the fire brigade in case of fire. A rescue operation will be performed to save the lives by giving treatment and hospitalizing the injured peoples if necessary. After the road is cleared by the rescue team, the first communication node will be reset via the hardware push button. Four functions activate when the reset button is pressed: first is to switch off the blinking light; second is to stop the siren; third is to send the RESET message to its adjacent node; and fourth is to reset the controller to its initial state. The RESET message is also sent backwards and will be relayed until received by the EOC. This message indicates to the EOC that the road is now clear, and the rescue operation completed successfully.
# Conformal Titanyl Phosphate Surface Passivation for Enhancing Photocatalytic Activity
###### Abstract  
A conformal titanyl phosphate passivation with the thickness of ca. 5 nm on the surface of TiO\({}_{2}\) nanoparticles for enhancing the photocatalytic degradation of organic pollutants and hydrogen production is described. The phosphate anion species bound to the surface of TiO\({}_{2}\) promote the favorable kinetics of photocatalytic activity and influence the catalytic reaction pathway. By using a facile surfactant-assisted sol-gel process, the surface defects of TiO\({}_{2}\) associated with deep traps was reduced and passivated by the phosphate anion species to form the titanyl phosphate. The strong bonds between the titanyl phosphate shell and TiO\({}_{2}\) core provided a long-term photochemical stability in aqueous electrolytes with enhanced photocatalytic activities. The titanyl phosphate contributed to the production and stabilization of hydroxyl radicals on the surface of photocatalyst, which facilitated the efficient photooxidation of the organic pollutants. Further, enhancing the photocatalytic hydrogen production was achieved by the titanyl phosphate modified TiO\({}_{2}\) (TP-TiO\({}_{2}\)). Consequently, the conformal titanyl phosphate passivation enhanced photocatalytic activity of TiO\({}_{2}\). Comparing to the bare TiO\({}_{2}\) nanoparticles, approximately two-fold higher photocatalytic H\({}_{2}\) production rate was achieved by the TP-TiO\({}_{2}\).  
titanyl phosphate; passivation of defects; stability; hydrogen production; photodegradation of pollutant +
Footnote †: journal: Applied Physics Letters
# A Method of Defect Depth Recognition in Active Infrared Thermography Based on GRU Networks
## 6 Conclusions  
In this paper, we combined the active infrared thermography with the GRU model and PCA to achieve the recognition of the defect depths. The raw datasets obtained by an infrared camera and the datasets processed by PCA were used to verify the performance of the GRU model. PCA is a useful technique for processing thermal sequences by reducing the cost of calculation and eliminating the redundant information. In terms of recall rates, the GRU model trained by principal component datasets had a better performance than the one trained by raw datasets, besides, the former had a faster training speed.  
In order to verify the application, defects of two different diameters were set, and the model we proposed proved that it possessed the fairly desirable ability to recognize the defect depths under the condition of the two diameters, respectively. The performance of the GRU model was evaluated by a comparison with the BP neural network; the former performed better than the latter, or in other words, the GRU model was able to learn more defect depth information than the BP model. For the same material, in using AIRT we found that the different defect depths determined the surface thermal response of the defect area. When inspecting a specimen with different plate thicknesses, it is necessary to prepare datasets for the different sound areas.  
Improving the quantitative evaluation of defects in non-destructive testing (NDT) is an urgent need, and our work provided a new method for further promoting the application of deep learning in NDT to improve the accuracy of quantitative evaluation. Therefore, the proposed method is a potential candidate for recognizing defect depth.  
[PERSON] designed and conducted the experiments, proposed the methodology, collected and analyzed the datasets, and wrote the original manuscript; [PERSON] and [PERSON] reviewed and edited the manuscript; [PERSON] provided supervision. Both authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (No. 51975117), and Key Research and Development Program of Jiangsu Province (BE2019086).  
Not applicable.  
The data are part of an ongoing project.  
We would like to thank the school of Mechanical Engineering of the Southeast University. We also thank the Research Center of Condition Monitoring and Fault Diagnosis for academic support.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### EEG Data Acquisition and Processing  
A 40-channel wireless EEG system [29] with star-shaped dry gold sensors was used to record electroencephalographic (EEG) signals with a sampling rate of 250 Hz. Electrodes were positioned according to the 10-10 and 10-20 systems (ref. Figure S1; 10/10: AF3-AF4, F7-F8, FT9-F10, FC3-FC4, FC5-FC6, C1-C2, C5-c6, CP1- CP2, CP3 -CP4, TP7-TP8, P7-P8, PO3-PO4, PO7-PO8: Fz, F3-F4, C3-C4, Cz, T7-T8, P3-P4, Pz, O1-O2, Oz; [30,31]), with reference sensors on the process mastoidei and ground electrode placed on the forehead. Skin impedance was kept below 5 k\(\Omega\) to ensure signal quality. Individual trials were segmented into 800-ms windows, with stimulus onset. EEG signals were filtered (bandpass: 1-70 Hz; cutoff filter for 50 Hz) and artifacts rejected above \(\pm\)200 uV. To evaluate the quality of the signal, the signal-to-noise ratio (SNR) was calculated according to the following formula: SNR = A/(2 \(\times\) SDnoise) [32]. The amplitude A was the peak-to-peak potential of the mean ERP, and the SD of the noise (\(\varepsilon\)) was obtained by subtracting the mean from each ERP. Only trials with good SNR and correct responses were included in the analysis. After preprocessing, the average number of realizations per condition and group was 525 for controls and 772 for children with DD artifact-free data epochs. The frequency bands in which the EEG transforms were filtered were \(\delta\)(1.5-4), \(\theta\)(4-8), \(\alpha\)(8-13), \(\beta\)1(13-20), \(\beta\)2(20-30), \(\gamma\)1(30-48), and \(\gamma\)2(52-70) Hz.
###### Abstract  
This study addresses the growing demand for scalable data encryption by evaluating the performance of AES (Advanced Encryption Standard) encryption and decryption using CBC (Cipher Block Chaining) and CTR (Counter Mode) modes across various CPU (Central Processing Unit) and GPU (Graphics Processing Unit) hardware models. The objective is to highlight GPU acceleration benefits and propose an optimized hybrid CPU-GPU workflow for large-scale data security. Methods include benchmarking encryption performance with provided data, mathematical models, and computational analysis. The results indicate significant performance gains with GPU acceleration, particularly for large datasets, and demonstrate that the hybrid CPU-GPU approach balances speed and resource utilization efficiently.  
AES encryption; GPU acceleration; CPU-GPU hybrid workflow; data security; performance analysis Article
# Measuring Identification and Quantification Errors in Spectral CT Material Decomposition
## 3 Results
Protocol-2 underestimates gadolinium concentration in comparison to Protocol-1 in the phantom (Figure 12a), by a similar amount. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Known Material (mg mL\({}^{-1}\))** & \begin{tabular}{c} **Protocol-1** \\ **Measured Material** \\ **(mg mL\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **Protocol-2** \\ **Measured Material** \\ **(mg mL\({}^{-1}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Protocol-1** \\ **Measured Material** \\ **(mg mL\({}^{-1}\))** \\ \end{tabular} &
\begin{tabular}{c} **Protocol-2** \\ **Measured** \\ **Misidentification (\%)** \\ \end{tabular} \\ \hline Gd\({}_{08}\) & 8.19 & 3.15 & 0 & 7.22 \\ \hline Gd\({}_{04}\) & 4.35 & 1.29 & 1.52 & 31.68 \\ \hline Gd\({}_{02}\) & 2.31 & 0.59 & 8.93 & 41.45 \\ \hline Gd\({}_{01}\) & 1.18 & 0 & 37.57 & 57.66 \\ \hline HA\({}_{808.5}\) & 818.9 & 852.9 & 0 & 0 \\ \hline HA\({}_{211.7}\) & 215.4 & 246.4 & 0 & 0 \\ \hline HA\({}_{54.3}\) & 63.78 & 68.09 & 4.66 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Qualitative and quantitative results summary of the Gd/HA calibration phantoms with two imaging protocols. Figure 12: Relationship between known and measured concentrations of Gd (**a**), and HA (**b**). Dotted line shows the trend line. Error bars show the standard deviation of measured concentrations from their mean value i.e., 1.51 to 3.14 mg Gd/mL for gadolinium (**a**) and 23.6 to 134.6 mg HA/mL for hydroxyapatite (**b**).