esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Method
### Overview  
Figure 1 shows the processing flow of the proposed method for classifying the vehicle type and color. The method consists of three steps: the first is a pre-processing step, the second is a vehicle detection step, and the third is a vehicle type and color classification step. The proposed method selects a region of interest (ROI) in road images captured during driving, where the vehicle is located, and applies a vehicle detection method along with type and color classification within the ROI. The pre-processing step enhances the quality of the input road image to improve the accuracy of vehicle detection and classification of types and colors. Image quality enhancement is necessary for images acquired while driving on the road because of the presence of various noises sources.  
In the pre-processing step, the input image is resized to reduce the processing load. Additionally, the RGB color space was converted to the Lab color space and the color contrast was enhanced using contrast-limited adaptive histogram equalization [32]. Fur  
Figure 1: Process flow chart of the proposed method.  
thermore, atmospheric haze is reduced by removing noise, which in turn reduces smoke caused by light scattering or vehicle exhaust in the atmosphere.  
In the vehicle detection step, the pre-trained deep learning model was used to remove noise and reduce the size of the input road image. The YOLO v4 deep learning neural network was then used to detect bounding boxes containing vehicles. To verify that the detected vehicle regions contained actual vehicles, a template matching method was used if the region exceeded the threshold.  
In the vehicle type and color classification step, a ResNet-50 pre-trained CNN [33; 34] was used to classify the detected vehicle bounding boxes [33; 34]. These bounding boxes represent the rear or rear side images of the vehicle in front. The types of colors of the vehicle were then determined by analyzing more than three consecutive frames. To improve the accuracy, vehicle bounding boxes from more than three frames were detected without classifying them using a single image result. Therefore, the input of the proposed method is an image sequence, and the output is bounding boxes containing vehicles in the image sequence, the vehicle types, and colors.
## 4 Proposed Algorithm (NCRA-DP-ACO)
### Pheromone Management
#### 4.2.5 System Parameters  
We employ a weighted-sum method to combine our three optimization objectives--energy consumption, network bandwidth, and carbon cost--into a single heuristic function. The multi-objective weights (\(\theta\), \(\gamma\), \(\delta\)) were determined by grid-search experiments over five representative workload scenarios, resulting in \(\theta\) = 0.3 for energy, \(\gamma\) = 0.4 for network bandwidth, and \(\delta\) = 0.3 for carbon cost.  
To identify appropriate values for the ACO parameters, we conducted a systematic tuning study in which each parameter was varied across a range commonly found in the literature [25; 31; 32; 33; 34; 35; 36; 37]. Specifically, we tested the heuristic influence \(\beta\in[2,\,3,\,5]\), pheromone-evaporation rate \(\rho\) \(\in\) {0.2, 0.3, 0.4}, number of ants \(N^{ ants}\) \(\in\) {10, 20, 50}, and iteration counts \(I\)\(\in\) {1, 2, 5}. For each candidate combination, we ran the ACO algorithm on five representative workloads and recorded the resulting average energy consumption and SLA violation ratio. We then selected the parameter set that delivered the lowest energy usage while maintaining acceptable SLA compliance. This process ensured that our final settings were grounded in empirical performance rather than chosen arbitrarily. Specifically, the pheromone evaporation rate was set to \(\rho\) = 0.3, the heuristic influence coefficient was set to \(\beta\) = 2, and the ant colony comprised \(N^{ ants}\) = 20 agents performing \(I\) = 2 iterations per placement round with an exploitation probability of \(q_{0}\) = 0.7.  
Migration thresholds were likewise determined empirically, with underutilization and overutilization bounds set to 30% and 90%, respectively, to balance the convergence speed against the solution quality. While structured methods such as AHP or TOPSIS could automate weight derivation, our choice of a simple weighted sum supports faster simulation runs; the exploration of adaptive or ML-based weight tuning is left for future work. A full list of parameters appears in Table 2.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline
**Symbol** & \(\rho\) & \(\theta\), \(\gamma\), \(\delta\) & \(\beta\) & \(A\) & \(I\) & \(q_{0}\) & \(Th^{under}\) & \(Th^{over}\) \\ \hline Value & 0.3 & 0.3, 0.4, 0.3 & 2 & 20 & 2 & 0.7 & 30\% & 90\% \\ \hline \end{tabular}
\end{table}
Table 2: System parameters.
## 5 Conclusions  
In conclusion, interference spectral imaging based on LC relaxation is proposed in this paper. A LC cascade composed of a thick LC (100 \(\upmu\)m) and a thin LC (10 \(\upmu\)m) provided an optical path difference of 23 \(\upmu\)m. Calibration using monochromatic light (1053 nm) resulted in a spectral resolution of 262 cm\({}^{-1}\) in the 400-1000 nm spectral range for the system. Spatial interference data could be obtained quickly in a measurement period of 20 s. This method was also used to measure the transmission spectra of resolution plates and optical damage to elements, which verified its feasibility in hyperspectral imaging and optical component damage detection.  
The performance of the system could be further improved by considering the relationship between the refractive index of the LC and the wavelength in the calibration and by increasing the field of view angle. The spectral responsivity of individual components should be considered in the spectral line calibration procedure. Finally, the proposed interferometric spectroscopy imaging technique has several advantages, such as rapid performance, a low cost, high stability, and easy integration. It could be further used for performance improvements in applications such as spectral microscopy and portable spectrometry.  
Conceptualization, J.Y. and W.F.; methodology, J.Y. and D.H.; software, J.Y and T.D.; validation, J.Y.; formal analysis, D.H.; investigation, H.C. and T.D.; resources, W.F. and H.C.; data curation, J.Y. and W.F.; writing--original draft preparation, J.Y. and W.F.; writing--review and editing, W.F. and J.Y.; visualization, W.F.; supervision, W.F.; project administration, W.F.; funding acquisition, W.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA25020303).  
Not applicable.  
The data included in this study are all owned by the research group and will not be made available.  
Figure 10: **(a)** Hyperspectral data cube of K9 glass. **(b)** The spectra of damaged and undamaged points. **(c)** From left to right, one-dimensional (x = 203) and two-dimensional (x = 200–240) spatial information for the entire spectrum. **(d)** From left to right, one-dimensional (x = 203) and two-dimensional (x = 200–240) spatial information at the indicated wavelengths (580 nm).  
**Acknowledgments:** We acknowledge the financial support of Self Deployed Projects of Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Software-Defined Radio Platform for Teaching Beamforming Principles
## 4 Beamforming Platform Validation and Testing
### Beamforming Platform Testing in Indoor and Outdoor Environments
Outdoor measurements revealed that the 90\({}^{\circ}\) beam orientation was in good agreement with the simulation results, as presented by Figure 11. For the 20\({}^{\circ}\) tilt beam orientation, we can once again observe the presence of the two sidelobes in the outdoor measurement results, while only one sidelobe is visible in the indoor setting. Also, for the second beam orientation, one can observe good agreement with the analytically derived solution, supporting a 20\({}^{\circ}\) beam steer compared to the no-phase-difference scenario. These observations validate the beamforming platform's effectiveness in achieving the desired beam orientation while demonstrating the influence of environmental factors on the radiation pattern.
# Intelligent Measurement of Coal Moisture Based on Microwave Spectrum via Distance-Weighted kNN
## 2 Materials and Methods
### Data Analysis
This method is independent of the distribution and linearity of data, and the prediction results are limited only by the range of training samples [33; 34]. The DW-kNN algorithm is weighted by the distance of samples based on the traditional kNN algorithm. In other words, the closer the neighbors are, the greater their distance weight is. Compared with the methods that do not consider the distance weight, the DW-kNN algorithm can overcome several misevaluated limitations and further improve the accuracy of the regression model [35]. The calculation steps of the DW-kNN method are as follows. First, calculate the Euclidean distance \(d\) between the data to be predicted \(\varphi\) and the training data by:  
\[d(\varphi,\delta_{p})=\sqrt{\sum_{q=1}^{n}(\varphi_{q}-\delta_{pq})^{2}}, \tag{12}\]  
where \(\varphi_{q}\) and \(\delta_{pq}\) denote the \(q\)th dimension data corresponding to the data to be predicted and the \(p\)th training spectrum data sample, respectively. Then, according to the Euclidean distance, calculate the distance weight \(\zeta_{p}\) by:  
\[\zeta_{p}=\frac{1}{[d(\varphi,\delta_{p})]^{2}}. \tag{13}\]Finally, obtain the prediction result \(\psi(\varphi)\) as the weighted average of \(k\) nearest samples by:  
\[\psi(\varphi)=\frac{\sum\limits_{p=1}^{k}\zeta_{p}\cdot\psi(\delta_{p})}{ \sum\limits_{p=1}^{k}\zeta_{p}}, \tag{14}\]  
where \(\psi(\delta_{p})\) represents the true moisture value of a training sample \(\delta_{p}\). In this study, the coefficient of determination (\(R^{2}\)), the mean absolute error (MAE) and the root mean square error (RMSE) were used to evaluate the performance of the proposed method.
## 3 Results
### Sustainability Analysis of Land NPP Change  
The Hurst index of land NPP ranged from 0.29 to 0.74 in the Han River Basin from 2001 to 2019, with a multi-year average of 0.59. The continuous series of land NPP (0.5 < \(H\) < 1) accounted for 89.44% of the basin area, indicating that the future trend of land NPP is consistent with the past in most areas of the Han River Basin. The reverse continuous series of land NPP (0 < \(H\) < 0.5) accounted for 10.56% of the basin area and was mainly concentrated in the middle-lower reaches of Nanzhang, Gucheng, and Yicheng and in the upper reaches of junction areas of Shangnan, Yunxi, and Yunxian (Figure 4). The future trend of land NPP in these regions is opposite to the past.  
Spatial overlay analysis of the change trend map and the Hurst index map was performed to obtain the coupled distribution of sustainability pattern of land NPP in the Han River Basin from 2001 to 2019 (Figure 5). In the continuous series of sustainability pattern, regions exhibiting significant and highly significant increases accounted for 58.90% of the basin area, mainly in Hanzhong, northern Ankang, Fangxian, the Shennongjia Forestry District, regions around the Dankiangkou Reservoir, and parts of the Nanyang Basin; areas showing significant and highly significant decreases represented 0.98% of the basin area, primarily in the peri-urban areas. In the reverse continuous series of sustainability pattern, regions displaying significant and highly significant increases accounted for 0.33% of the basin area; areas presenting significant and highly significant decreases only accounted for a small proportion (less than 0.01%). In general, series with continuously significant increases in sustainability pattern of land NPP were far more than those with continuously significant decreases (or reverse continuously significant increases). Therefore, land NPP in the Han River Basin will present sustained growth in the future.  
Figure 4: Spatial distribution of Hurst index of land net primary productivity (NPP) in the Han River Basin of China from 2001 to 2019.
## 5 Parameterisation Studies
### The Area of Support Bars  
The stiffness parameter of the bar depends on the area so in order to see the effect of the area of the bar, the ratio of the area of the bar to the beam is introduced as \(A_{R}\). To determine the \(A_{R}\) value for the support bar, only lead and aluminium materials are used in the calculations for brevity. Figures 8 and 9 show the force transmission response of the lead bar supported and the aluminium bar supported beam in the frequency domain.  
Figure 8: The changing area effects on the force transmissibility of the steel beam supported by lead bars.  
Figure 9: The changing area effects on the force transmissibility of the steel beam supported by aluminium bars.  
As can be seen from Figures 8 and 9, frequency parameters generally increase with the increase in bar area. However, this situation is reversed after the bar effect is seen as a frequency parameter in the system. For example, in Figure 8, the frequency values increase with decreasing area in the peak responses around 15 and 22 Hz. In Figure 9, when the bar effect is seen in the frequency response, the frequency parameter increases with decreasing area around 23 and 28 Hz. At the frequencies where the response of the bar is dominant, the amplitudes of the frequency response decrease with increasing area around 11.31 Hz, 19.73 Hz and 25.45 Hz in Figure 8 and 23.27 Hz in Figure 9. The minimum transmission at these frequencies occurs when the value of \(A_{R}\) is 100. The minimum force transmission occurs at \(A_{R}\) variable 0.1 and 100 values for the aluminium bar-supported beam. This is due to the effective participation of the axial bar in the vibration, as mentioned above. If the beam is dominant in the vibration motion, the force transmission is minimal at 0.1 of \(A_{R}\) (3.13 Hz and 9.21 Hz) and if the bar is dominant, the force transmission is minimal at 100 of \(A_{R}\) (23.27 Hz). One of these values (0.1 or 100) can make the force transmission minimum at one frequency peak and maximum at the other. Therefore, the area for the axial bar is chosen to be 10 times the area of the beam. In force transmission, the first frequency peak value is minimum for the lead and aluminium material used for the axial bar for \(A_{R}\) = 10.
## 2 Background
### SCL Decoding  
To improve the error-correction performance of Polar codes, SCL decoding with list size \(L\) has been introduced in [5]. In the PFT, instead of vectors \(\boldsymbol{\alpha}\) and \(\boldsymbol{\beta}\), lists of \(L\) vectors are passed among the nodes. Therefore, a second index \(l\in[0,L-1]\) is introduced, e.g., the partial-sums of a node \(v\) are denoted by \(\boldsymbol{\beta}^{v,l}\). In layer \(s=0\), at the leaves of the unpruned PFT, both possible values, \(0\) and \(1\), for an information bit estimation are considered. Therefore, the number of decoding paths doubles for every bit estimation. When the number of paths exceeds the list size \(L\), unreliable paths must be discarded by sorting. The reliability of each path is rated by a Path Metric (PM), which is updated at every bit estimation. For LLR-based SCL decoding [17], the PMs are initialized with \(0\). When estimating bit \(i\), each PM of a path with index \(p\in[0,2L-1]\) that proceeds the input path \(l\in[0,L-1]\), is updated by  
\[\text{PM}^{p}_{i}=\begin{cases}\text{PM}^{l}_{i-1}+\Big{|}\pi^{v,l}_{i}\Big{|},&\text{if }\beta^{v,l}_{i}\
eq\text{HDD}(\pi^{v,l}_{i})\\ \text{PM}^{l}_{i-1},&\text{otherwise},\end{cases} \tag{3}\]  
with \(\text{HDD}(\boldsymbol{\alpha}^{v})\) being Hard Decision Decoding (HDD) on \(\boldsymbol{\alpha}^{v}\). Since small PMs belong to probable paths, those paths with low PM values survive the sorting step. After the last bit decision, the path with the smallest PM is chosen as the output of the decoder.  
A CRC included in the input vector \(\boldsymbol{u}\) as an outer code assists the path selection. The output path in SCL decoding is chosen based on a valid CRC, which further improves the error-correction performance.
## 2 Materials and Methods
### Sample Preparation and Analysis of Physicochemical Properties  
The optimal conditions for fermenting CFB with _P. anomalous_ and _L. plantarum_ were identified as follows: pH 7, fermentation temperature of 32 \({}^{\circ}\)C, an inoculation ratio of 7:3, and an inoculum rate of 10%. _P. anomalous_ was introduced on the fourth day of fermentation, while _L. plantarum_ was added on the sixth day. Samples were collected at specific intervals from 0.5 to 15 days and preserved at \(-\)80 \({}^{\circ}\)C for future analysis. Control groups were also prepared without the addition of exogenous strains, with all other fermentation conditions kept constant. The collected samples were analyzed for total esters, total acidity, and alcohol content [24; 25]. The overall ester content was measured as per the official Chinese standard method [26]. Total acidity (titratable acidity) was measured by titrating the samples to a pH of 8.2 using 0.1 M NaOH. The pH was determined with a digital pH meter (STARTER 3100, Ohaus, Shanghai, China) [27]. The alcohol content was gauged through gas chromatography.  
After centrifugation, 1 mL of the supernatant was extracted, diluted to a concentration of 10\({}^{-2}\), and mixed with 100 \(\upmu\)L of 2% acetic acid solution before analysis. The alcohol content was gauged through gas chromatography (model 7820A; Agilent, Agilent Technologies Shanghai Analytical Instruments Co., Ltd., Shanghai, China) with an LZP-950 column (50 m \(\times\) 0.32 mm \(\times\) 1 \(\upmu\)m; Agilent) and flame ionization detector (FID) [28; 29]. The flows of helium (carrier gas), hydrogen, and air were 1 mL/min, and 35 mL/min, and 350 mL/min, respectively. The injector and detector temperatures were set at 230 \({}^{\circ}\)C. A sample volume of 10 \(\upmu\)L was injected with a split proportion of 30:1. The oven temperature was programmed as follows: initially set to 60 \({}^{\circ}\)C for 0.1 min, then raised at a rate of 2 \({}^{\circ}\)C/min to 70 \({}^{\circ}\)C, where it was kept for 1 min. This was followed by a rise at 5 \({}^{\circ}\)C/min to 80 \({}^{\circ}\)C, and subsequently at 11 \({}^{\circ}\)C/min to 110 \({}^{\circ}\)C and held for 18 min. Lastly, the temperature was raised at 8 \({}^{\circ}\)C/min to 250 \({}^{\circ}\)C and kept there for 10 min.  
Following centrifugation, the supernatant was also dissected for organic acids through high-performance liquid chromatography (HPLC) [30]. Chromatographic separation was achieved using a Zorbax SB-Aq column (4.6 \(\times\) 250 mm, 5 \(\upmu\)m; Agilent Technologies Shanghai Analytical Instruments Co., Ltd., Shanghai, China) at a column temperature of 30 \({}^{\circ}\)C. UV detection was performed at a wavelength of 214 nm with a flow rate of 0.8 mL/min. The mobile phase consisted of methanol (A) and a 0.01 mol/L KH\({}_{2}\)PO\({}_{4}\) buffer solution (pH 2.85, B) at a ratio of 3:97. A 10 \(\upmu\)L sample volume was injected, and the total analysis time was 40 min.
# GraphEPN: A Deep Learning Framework for B-Cell Epitope Prediction Leveraging Graph Neural Networks
## 2 Materials and Methods
### Data Preprocessing and Graph Construction
#### 2.2.3 Protein 3D Graph Construction  
After extracting node and edge features, we constructed a graph model based on the 3D structure of the protein [18]. Each residue in the protein is represented as a node in the graph, and interactions between residues are represented as edges. Given the set of residues in a protein \(\{r_{1},r_{2},\cdots,r_{N}\}\), the graph \(G=(V,E)\) is constructed, where \(V\) denotes the set of nodes and where \(E\) denotes the set of edges. An edge \((i,j)\) is added between residues \(i\) and \(j\) if the Euclidean distance \(D(i,j)\) between them is less than or equal to a predefined threshold \(\tau\). The formal representation of the graph is as follows:  
\[G=(V,E),\quad V=\{\mathbf{h}_{i}\mid i=1,2,\cdots,N\},\quad E=\left\{\mathbf{e }_{ij}\mid D(i,j)\leq\tau\right\} \tag{7}\]  
Here, \(N\) represents the number of residues in the protein, and \(\tau\) is the distance threshold. Finally, the complete 3D protein graph was constructed via the DGL framework [18]. In DGL, we construct the protein graph by defining nodes based on the residues and adding edges between residues that satisfy the distance threshold condition. Specifically, we calculate the Euclidean distance between all residue pairs and retain only those that meet the threshold criterion. These edges are then stored as adjacency relationships in the graph. Each edge is further assigned a set of features, including distance-based radial basis function (RBF) encodings and quaternion-based orientation features. The final graph representation is implemented in DGL, where node features, edge indices, and edge attributes are defined accordingly.
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
## 5 Scheme Implementation  
Our proposed scheme comprises five crucial phases: the system initialization phase, user registration and identity verification phase, data storage phase, data query phase and data sharing phase.
## 4 Results and Performance Evaluation
### Continuous Statistical Scores
The isolated storm in the low part of the image around 49\({}^{\circ}\) N 5\({}^{\circ}\) E is also fine detected. The underestimation of the model is evident as well. At peaks of precipitation around and beyond 20 mm/h in various pixel of the radar image correspond values of precipitation around 12 mm/h in the model. Values of FAR of about 0.4 are consistent for instance observing the precipitation area over the North Sea that is overestimated in the NN model. On the other hand, the model shows an output more similar to the nearest in time LEO passage of a PMW sensors (SSMIS Figure 3c). This is not unexpected because of the use of GPM data as truth to train the NN. It should also be considered that the quality of the radar data, according to Figure 4, is quite low (<0.5) over the North Sea. In Figure 3d, the rain rate from PR-OBS3A product is shown. To better understand its behavior, the SEVIRI brightness temperature at 10.8 \(\upmu\)m has been added in Figure 5. The main path of the precipitation is recognized, but the area is overestimated confirming the high values of FAR of Table 11. Comparing the two images it appears clear that PR-OBS3A is mostly driven by the cloud top temperature spreading high precipitation over a larger area. It should be noted that SSMIS product also tend to spread precipitation over a larger area then the radar. Figure 3: Rain Rate data (mm/h). (**a**): NN retrieval—5 June 2015 17:30; (**b**) Radar ground-truth—5 June 2015 17:30; (**c**) SSMIS-Rain Rate—5 June 2015 17:25; (**d**) H03 product—5 June 2015 17:30. Furthermore, during the study it has been noticed that the results significantly benefit choosing a higher threshold of the quality index of radar data (e.g., QI \(\geq\) 0.8 instead of the actual value 0.6). Nonetheless, because of the re-gridding process, the number of radar pixels available for the validation decreases until not to have a valid statistical sample. This is clear in Figure 5 where typical quality index values for OPERA radar composite are shown (they do not change significantly between images through time). Many studies conclude that precipitation measurements from ground (e.g., radar and rain gauge) are subject to large uncertainty, especially when they are used to validate retrievals from satellite data and the results presented in this study confirm this fact [30,47].