esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results Analysis and Discussion
### Advantages and Limitations of the Ensemble Learning Framework  
Our experimental results demonstrate both the advantages and limitations of the ensemble learning framework in urban road travel time prediction:  
Advantages:  
(1) Selective performance improvement: In specific prediction scenarios, ensemble learning models can significantly enhance prediction performance. For example, LSTM-DNN(2) ensemble learning achieves an MAPE of 8.63% for 2 min scale prediction, outperforming single models; linear regression ensemble learning achieves an RMSE of 5.82 for 2 min scale prediction, also superior to single models.  
(2) Enhanced stability: By combining prediction results from multiple base learners, ensemble learning models can reduce the volatility of single model predictions and provide more stable prediction results.  
(3) Complementarity utilization: The ensemble learning framework can effectively combine the computational efficiency of shallow learning models (such as linear regression) with the performance capabilities of deep learning models (such as LSTM-DNN), achieving complementary advantages to some extent.  
Limitations:  
(1) Inconsistent improvement: Ensemble learning does not provide improvements across all prediction scenarios and evaluation metrics. For example, for MAPE at 20 min and 30 min prediction scales, the single LSTM-DNN model still performs best; for RMSE across multiple prediction scales, the advantages of the single random forest model are not surpassed by ensemble models.  
(2) Increased computational cost: The ensemble learning framework requires training multiple base learners and meta-learners, significantly increasing computational costs, which may be a challenge in resource-constrained environments.  
(3) Meta-learner dependency: The performance of ensemble learning largely depends on the selection and training of meta-learners. We found that different meta-learners perform differently across various prediction scenarios, with no single meta-learner performing best in all scenarios.  
(4) Data dependency: The effectiveness of ensemble learning depends on the quality and quantity of training data. If training data is insufficient or of low quality, ensemble learning may not be able to leverage its advantages and may even experience performance degradation due to overfitting.  
Based on these observations, we recommend that in practical applications, the decision to adopt an ensemble learning framework should be made according to specific prediction requirements and resource constraints. For scenarios that demand high accuracy with sufficient resources, ensemble learning is a worthwhile consideration; for resource-constrained scenarios or those requiring high computational speed, single models may still be the better choice.
## 5 Conclusions  
Despite its wide application, the conventional subset-based DIC method exhibits a fundamental defect in boundary deformation calculation. Boundary deformation information is of paramount importance in many mechanical problems because damage or yielding generally initiates from the boundary, particularly on curved edges as a result of stress concentration. To resolve this issue, the capability of the novel Q8-DIC in deformation near curved edges was explored and highlighted in this work. The effectiveness and efficiency of Q8-DIC in the boundary deformation calculation were compared with those of two conventional methods, namely the extrapolation and half-subset methods, by means of simulated and real experiments. The results revealed that, owing to the continuity constraint over the entire displacement field, Q8-DIC provided significantly higher accuracy in boundary deformation measurement and a substantially smoother displacement field than the other two methods. A precise whole field (including the boundary) deformation is very useful for material property characterization or failure prediction. For example, mesh-based DIC and finite element model updating methods can be combined to inversely obtain the damage evolution law or yielding criterion of material under complex loading. Further studies will be performed in these fields.  
Conceptualization, G.L. and S.M.; methodology, L.W., T.X. and H.Z.; writing--original draft, L.W.; writing--review and editing, G.L. and S.M.; funding acquisition, G.L. and S.M. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Natural Science Foundation of China (Grant Nos. 11872115, U1837602, 11602022, 11727801, and 11772053).  
The raw / processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.  
The authors declare no conflict of interest.
# Floral Scent Evaluation of Three Cut Flowers Through Sensorial and Gas Chromatography Analysis
## 3 Results
### VOCs Analysis

Table 2: List of volatile organic compounds (VOCs) detected through GC-MS from the headspace collection ofresia, oriental lilly, and chrysanthhemum flowers, indicating the retention time (min), aromatic description (www.flavournet.com), and relative areas (a5 TDEV, \(n=3\)) normalized to 4-methyl-2-pentanol. ID: identification reliability. A, mass spectrum and Linear Retention Index (LRI) agreed with standards; B, mass spectrum agreed with mass spectral data base and LRI agreed with the literature data (Flavornet and Pherobase); C, tentatively identified, mass spectrum agreed with mass spectral database. Figure 3: Chromatograms obtained from the analysis through GC-MS to detect the floral scent of (**A**) chrysanhemum, (**B**) oriental lily, and (**C**) frescia, showing the highest peak in each case (\(\beta\)-pinene, \(\beta\)-cis-cime, and linalool, respectively). The lowest concentration of the major VOC was found in chrysanthemum (\(\beta\)-pinene) at \(40.7\pm 1.8\)\(\mu\)g L\({}^{-1}\) while higher concentrations were observed for \(\beta\) cis-ocimene (5552 \(\pm\) 990 \(\mu\)g L\({}^{-1}\)) and linalool (11,800 \(\pm\) 220 \(\mu\)g L\({}^{-1}\)), the major VOCs present in lilium and freesia, respectively (Table 3). The odor activity value (OAV) can be calculated as the ratio between the concentration of an individual compound and its odor detection threshold (ODT). Thus, an OAV above 1 means that this is an aroma active compound and therefore it contributes to the overall aroma of that particular sample. As shown in Table 3, the OAV is clearly higher than 1 in the case of ocimene and linalool, suggesting that these may be the main impact odorants of lilium and freesia flowers, respectively. However, in the case of chrysanthemum, the concentration of \(\beta\)-pinene is below the ODT and therefore it would not be an impact odorant of this flower. Following terpen, benzenoid compounds (3) were numerically the next most abundant class of VOCs detected in all three species evaluated while volatile compounds belonging to the groups of ketones (2), aldehydes (1), and esters (1) were exclusively detected in freesia, chrysanthemum, and oriental lily, respectively (Figure 4).
## 4 Simulation Results
### Economic Assessment
Domestic natural gas and CHP have good benefits compared to traditional systems from an annual operating costs point of view, as shown in Figure 5. The annual operating costs of domestic gas heater and CHP consists of O&M costs and total energy costs, which are respectively \(USD2354.43\) and \(USD2133.15\). Compared with traditional DHW systems, gas-fired heater is saved annually \(USD1201.22\) for simulated residential building, and CHP saved annually is \(USD1493.13\). However, it is impossible to identify which of the CHP and domestic gas heater is more advantageous without the initial investment. To eliminate the impact of the initial investment of the DHW system for economy, the following further economic analysis between GB and CHP in the traditional DHW system with the instantaneous domestic gas-fired heater with distributed DHW is undertaken. It is assumed that the hot water and heating temperature range between gas heater and CHP are the same. The payback and CSR are calculated in Table 3, and NPV is shown in Figure 6. The payback of CHP with engine lifetime of 20 is calculated is 9.43 and domestic gas heater with engine lifetime of 8 is 3.55. There is not much benefit of using the CHP from an economic point of view. The main reason is the large heat loss, and the CHP is in standy mode most of the time. AHUE is 31.28%, which means the hot water using time on CHP is too low. Therefore, the economics of micro-CHP are highly dependent on AHUE and are not solely dependent on NPV and payback periods as described in the literature [3]. The heat energy produced in the heating pipes cannot be entirely consumed by the users. When there is no heat demand, some heat energy stored in the pipeline will be released into the air. It causes pipe heat loss in the hot water systems. The influence of the AHUE on CHP payback is discussed below. Figure 7 shows that CHP payback is better than a domestic gas heater when the AHUE reaches 55.35%, which means the average daily operation of CHP units is more than 5.11 h. The payback of is dependent on the sizing of CHP, and the capacity of hot water demand. It is a reference for sizing and designing a CHP residential installation. The ratio will be shorter if the lifetime of each engine is dimensionless.
# SATFuzz: A Stateful Network Protocol Fuzzing Framework from a Novel Perspective
## 3 Architecture
### Simplified Finite-State Machine  
We use a simplified finite state machine (SFSM) as a formal description model for stateful network protocol interactions. The SFSM can be defined as a triple M = (S,I,T). \(S=\{s_{0},s_{1},\ldots,s_{n}\}\) is a finite set of state symbols, and \(s_{0}\) represents the initial state of M. At any time, M can only be in a specific state \(s_{i}\), and M begins to receive inputs from \(s_{0}\); \(I=\{i_{1},i_{2},\ldots,i_{m}\}\) is a finite set of input symbols; T: S \(\times\) I\(\rightarrow\)S is a state transition function, which is a one-to-one mapping. When the SFSM is applied to the server, S represents the set of status codes in the response messages, and I represents the set of request message types that can be received and processed normally.  
Figure 1: The structure of SATFuzz.  
Taking the SFSM of the FTP as an example [24], the specific values corresponding to its state symbols and input symbols are shown in Table 1. M, shown in Figure 2, only contains part of the state transition relationships, where the transition from \(s_{0}\) to \(s_{1}\) indicates that, when the server is in state 220 (abstracted by \(s_{0}\)), if a request message of the USER type (abstracted by \(i_{1}\)) is received and processed normally, then the server transitions to state 331 (abstracted by \(s_{1}\)).
## 3 Coherent \(\pi\)-Electron Angular Momentum and Current of a Molecule Having Two Aromatic Rings
### Two Ring Components of \(\pi\)-Electron Angular Momentum and Current
#### 3.1.4 Application to (P)-2,2\({}^{\prime}\)-Biphenyl  
For generation of coherent angular momentum and ring current of (\(P\))-2,2\({}^{\prime}\)-biphenyl, we focus on the three optically allowed excited states (\(a\), \(b_{1}\), and \(b_{2}\)) whose energies range from 6.67 to 6.84 eV as shown in Figure 9a [87]. The geometry of (\(P\))-2,2\({}^{\prime}\)-biphenyl in the ground state was optimized by using the density functional theory (DFT) at the B3 LYP level of theory in the GAUSSIAN09 program [89]. (\(P\))-2,2\({}^{\prime}\)-biphenyl was assumed to be fixed on a surface by a non-conjugated chemical bond or in a space by laser molecular orientation techniques. The laboratory-fixed \(Y\)-axis is set to be parallel to the single bond bridging the two phenol groups, and the rotation axis of point group C\({}_{2}\) is placed along the laboratory-fixed \(Z\)-axis parallel to the surface normal. The dihedral angle between the two phenol groups, \(\Theta_{d}\), was found to be 108.9 degrees from our DFT calculations [86].  
The transition energies to the \(a\) (A), \(b_{1}\) (B), and \(b_{2}\) (B) states, which were calculated at the optimized ground state geometry using the time-dependent DFT at the B3 LYP level of theory, are 6.67, 6.78, and 6.84 eV, respectively [86]. Figure 9b shows the directions of transition dipole moments. The three optically allowed excited states associated with the present laser-induced electron dynamics have one-electronic excitation configurations since they basically consist of optically allowed \(\pi\pi^{\star}\) excited states of the two phenol rings. The transition dipole moment from the ground state to excited state \(a\) (A), \(\mu_{ga}\) [\(=(0,\,0,\,-0.77)\) in atomic units], is parallel to the \(Z\)-axis, and the others, \(\mu_{gb1}\) [\(=(0.08,\,1.93,\,0)\)] and \(\mu_{gb2}\) [\(=(1.24,\,-0.34,\,0)\)], are nearly orthogonal to each other in the \(XY\)-plane as shown in Figure 9.
## 2 LEA Detection and Tracking
### Perspective Projection for Correcting Image Distortion  
If LEA detection and tracking are successfully implemented, we must then make a symbol (color) decision for each LED. Because the camera is used as a receiver, the received image can be distorted, which adversely affects the SER performance. The distorted shape of the LEA is not suitable for determining the symbol for each LED inside the square LEA using image processing.  
Figure 8: Four corners of LEA used as the variables of the Kalman filter.  
Figure 7: Discrete-time Kalman filter loop.  
To correct the distorted shape of the LEA, perspective projection is used in the final step of our proposed method, as shown in Figure 2. Geometrical distortions such as scaling, rotation, skewing, and perspective distortion are very common transformation effects. Each distortion is represented by a linear transformation, which is well-investigated in linear algebra [19]. These transformations can be performed using simple multiplication, as shown in Equation (6):  
\[\left(\begin{array}{ccc}a_{1}&a_{2}&b_{1}\\ a_{3}&a_{4}&b_{2}\\ c_{1}&c_{2}&1\end{array}\right)\times\left(\begin{array}{c}x\\ y\\ 1\end{array}\right)=\left(\begin{array}{c}x^{\prime}\\ y^{\prime}\\ 1\end{array}\right) \tag{6}\]  
where \(\left(\begin{array}{cc}a_{1}&a_{2}\\ a_{3}&a_{4}\end{array}\right)\) is a rotation matrix. The above matrix defines the kind of transformations that will be performed: scaling, rotation, and so on. \(\left(\begin{array}{c}b_{1}\\ b_{2}\end{array}\right)\) is the translation vector; it simply moves the points in the \(x\)-\(y\) plane. \(\left(\begin{array}{cc}c_{1}&c_{2}\end{array}\right)\) is the projection vector. Here, \(x\) and \(y\) are the source points on the received image. And \(x^{\prime}\) and \(y^{\prime}\) are the destination points of the transformed coordinates.  
Finally, given the four corner points of the LEA in the image, the perspective projection can be applied to correct the distorted LEA, as shown in Figure 9.
## 3 Results
### Volatile Organic Compounds Produced by Bacillus Bacteria and F. oxysporum
#### 3.2.1 Volatile Organic Compounds Produced by _Bacillus_ Bacteria
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Group of Compounds** & \(\mathbf{RI}_{\textit{exp.}}\) & **Relative Content [\% TIC]** \\ \hline
**Sesquiterpenenes**, & & **8.49 \(\pm\) 0.62** \\ including the following: & & \\ \hline Cyclosativene & 1370 & 0.10 \(\pm\) 0.01 \\ Spiroaxa-5,7-diene & 1391 & 0.33 \(\pm\) 0.03 \\ \(\beta\)-Elemene & 1391 & 0.10 \(\pm\) 0.01 \\ \(\beta\)-Copaene & 1391 & 0.20 \(\pm\) 0.01 \\ Sesquiterpene (C\({}_{15}\)H\({}_{22}\)) & 1435 & 0.11 \(\pm\) 0.01 \\ \(\alpha\)-Mudrene & 1487 & 3.09 \(\pm\) 0.07 \\ \(\delta\)-Cadinene & 1505 & 0.10 \(\pm\) 0.01 \\ Zonarene & 1529 & 0.68 \(\pm\) 0.03 \\ \(\beta\)-Vatirenene & 1530 & 0.20 \(\pm\) 0.02 \\ Sesquiterpene (C\({}_{15}\)H\({}_{24}\)) & 1556 & 2.44 \(\pm\) 0.31 \\ Glenol & 1574 & 0.53 \(\pm\) 0.07 \\
1-epi-Cubenol & 1590 & 0.12 \(\pm\) 0.01 \\ Cubenol & 1635 & 0.27 \(\pm\) 0.03 \\
**Alcohols**, & & \\ including the following: & & **14.53 \(\pm\) 0.42** \\ \hline Ethanol & 450 & 4.62 \(\pm\) 0.01 \\ Propan-1-ol & 530 & 0.57 \(\pm\) 0.04 \\ Isobutanol & 594 & 0.32 \(\pm\) 0.03 \\
2-Methylbutan-1-ol & 728 & 0.84 \(\pm\) 0.07 \\ Benzyl alcohol & 1030 & 8.18 \(\pm\) 0.27 \\ \hline
**Carbonyl compounds**, & & **26.69 \(\pm\) 1.67** \\ including the following: & & \\ \hline Isopentanal & 626 & 0.54 \(\pm\) 0.04 \\ Benzaldehyde & 959 & 25.83 \(\pm\) 1.61 \\ Acetofenone & 1065 & 0.11 \(\pm\) 0.01 \\ Anisaldehyde & 1247 & 0.09 \(\pm\) 0.01 \\ Undecan-2-one & 1294 & 0.11 \(\pm\) 0.01 \\ \hline
**Carboxylic acids**, & & **0.58 \(\pm\) 0.06** \\ including the following: & & \\ \hline Acetic acid & 612 & 0.10 \(\pm\) 0.01 \\ Isovaleric acid & 830 & 0.35 \(\pm\) 0.03 \\
2-Methylbutanoic acid & 835 & 0.13 \(\pm\) 0.03 \\ \hline
**Esters**, & & **0.72 \(\pm\) 0.05** \\ including the following: & & \\ \hline Methyl isopentanoate & 770 & 0.38 \(\pm\) 0.02 \\ Methyl benzoate & 1095 & 0.26 \(\pm\) 0.01 \\ Ethyl benzoate & 1172 & 0.08 \(\pm\) 0.01 \\ \hline
**Chlorinated benzene derivatives and chlorinated benzaldehyde derivatives**, & & **48.45 \(\pm\) 1.46** \\ including the following: & & \\ \hline
2-Chloro-1,4-dimethoxybenzene & 1353 & 2.55 \(\pm\) 0.17 \\
1,4-Dichloro-2,5-dimethoxybenzene & 1444 & 13.07 \(\pm\) 0.43 \\
3-Chloro-4-(methyloxy)benzaldehyde & 1453 & 2.04 \(\pm\) 0.02 \\
3,5-Dichloro-4-methoxybenzaldehyde & 1465 & 30.79 \(\pm\) 0.83 \\ \hline
**Other compounds**, & & **0.54 \(\pm\) 0.04** \\ including the following: & & \\ \hline Methyl N-hydroxybenzencarboximidoate & 903 & 0.19 \(\pm\) 0.02 \\ \(\gamma\)-Decalactone & 1471 & 0.36 \(\pm\) 0.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Chemical composition of volatile organic compounds emitted by _F. oxysporum_. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Group of Compounds** & \(RI_{\text{exp.
# Detecting SARS-CoV-2 in CT Scans Using Vision Transformer and Graph Neural Network
## 3 Materials and Methods
### Integration of CNN-GNN-ViT  
Integrating CNN, GNN, and ViT into a unified model called ViTGNN involves structuring the data flow and potentially training in multiple stages. The process begins with the CNN extracting spatial features from the input image, producing a feature map where each spatial location corresponds to a feature vector. These feature vectors are then used as node features in a graph, with edges strictly defined based on four-connected spatial adjacency. The adjacency matrix is constructed by connecting each node to its immediate top, bottom, left, and right neighbors, ensuring that local spatial relationships are preserved. In addition, self-loops are included to retain individual node features during the message passage. The GNN processes these node features by applying graph convolution layers, updating each node's representation based on its neighbors. The refined node features are then flattened into a sequence, positional encodings are added, and a CLS token is prepended to prepare the data for the transformer. The ViT processes this sequence using self-attention layers, producing a final representation where the CLS token embedding serves as a global descriptor of the image. This representation is then passed through a fully connected layer to generate class scores, with a softmax function applied to obtain class probabilities. Training the entire model from scratch, especially the Vision Transformer (ViT), is computationally expensive. In order to mitigate this, we adopt a staged training approach where the ViT is initialized with pre-trained weights (ViT-B/16 model) from ImageNet and fine-tuned on our dataset with a freezing ratio of 0.5. That is, 50% of the ViT architecture layers are kept frozen so their weights do not change during training. Meanwhile, the CNN and GCN components are trained from scratch to extract local and relational features. This integration allows CNNs to capture local spatial patterns, GCNs to model relational structures, and ViTs to process global dependencies, creating a powerful framework for medical image analysis.
# UAV-Based Airport Lights Inspection Without GNSS Positioning Support
## 3 Materials and Methods
### Extraction and Description of Observed Lights  
The video stream from the UAV camera is cut into frames--images--constituting the input of the next processing task. The task executed by the function _olamp_ is to extract lights and describe their parameters on the current image. If it is the first image, the light data base is initiated. It contains lights numbers, their colour, and pixel positions on the image. The task is divided into operations: light detection and light description.  
Light detection in the image is based on morphological operations in grey scale. Morphology was chosen as previous works proved its high effectiveness in extracting objects [34; 35]. The raw camera image is in the RGB colour space; it is converted to grey scale to speed up the processing. In order to eliminate noise, the image is \"smoothed\" using a Gauss filter. A rectangular filter is used to remove noise potentially linking neighbouring lights. The size of the filter kernel was selected by taking into account the horizontal pixel distances between the lights on the image. One tenth of the distance set as the width and one fifth set as the height of the kernel proved effective for eliminating noise and for preserving light edges. Next, dilation is applied, and the morphological gradient is calculated. The result is binarized using locally calculated thresholds and a set of objects--lights and contours--is obtained.  
The set of contours is filtered; only objects larger than a prescribed size and positioned in the image segment significant for detection are extracted.  
Each light is described using image pixel coordinates of the light centre, size, and colour. The size is expressed by the number of pixels in the light contour. The number of overexposed pixels is subtracted from the total number of light pixels limited by a rectangle circumscribed around the contour of the light as shown in Figure 3. This operation prevents a gross distortion of the lights colour as the colour is calculated as the mean value of the light pixels. It is coded as ((256R + G)256) + B, where RGB are the red, green, and blue colour constituents.
## 2 Conditions of Conducted Tests
### Test Conditions  
The tests were carried out in an anechoic chamber (Figure 8). The measurement system FSWT26 receiver from Rohde & Schwarz with a set of measurement antennas (a vertical active rod antenna (100 Hz up to 50 MHz), a biconical active antenna (20 MHz up to 200 MHz), and a dipole active antenna (200 MHz up to 1000 MHz)) were used in the tests. The distance between antenna and the PDA-1000 8-bit analogue-to-digital converter card was used to sample of the revealing emission signals (Signatec PDA100 Scope Application software, version 1.19). The card offers a signal sampling rate of 1 GS/s. The sampling rate can be reduced by using a card clock frequency division in the range from 2 to 1024.  
Figure 7: Test images used in the analysis of the effectiveness of the proposed measures in the process of determining the correct number of lines of the reproduced image: (**a**) a photo showing two vehicles (HDMI standard), (**b**) a three-column text (HDMI standard), (**c**) three words “protection” written in secure font (VGA standard), (**d**) menu of multifunctional device.  
The tests were carried out using a computer set (Figure 8a) and a laser printer--HP Color Laser Jet M477 fdn (Figure 8b). During testing of revealing emissions from a computer set, the monitor was operated at 1280 \(\times\) 1024/60 Hz (HDMI standard) or 1024 \(\times\) 768/60 Hz (VGA standard). The printer was not connected to the computer. In case of text data, black-letter characters were displayed on a white background. Images were presented in greyscale.