esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Results and Analysis
### Apparent Morphology
#### 3.1.1 The Ignition Composition of Mg/TeO\({}_{2}\)  
Figure 1 shows the particle appearance morphology photos of three kinds of Mg/TeO\({}_{2}\) ignition compositions, magnified by an optical microscope, before and after the high-temperature test at 180 \({}^{\circ}\)C for 2-10 days. The results showed that after exposure to 180 \({}^{\circ}\)C for 2 days, the color changed slightly, and the color changed less after 2-10 days. There were no appearance changes such as breakages, cracks, expansions and looseness, pits, shrinkage, melting, and surplus materials of the agent particles, and a little more metallic luster appeared.  
It can be seen from Figure 2 that due to the granulation and the use of adhesive, the SEM images of sample B and sample C are different from those of sample A. Sample A presented irregular lumps, while sample B and sample C presented larger size agglomerates. Compared with the samples tested at normal temperature and high temperature, there was no change in the morphology and surface details of the composition.  
\begin{table}
\begin{tabular}{c c c} \hline
**Test Materials** & **Storage Conditions** & **Test Items** \\ \hline Magnesium powder; & 60 \({}^{\circ}\)C, 4 h; & Apparent morphology; \\ TeO\({}_{2}\); viton & 180 \({}^{\circ}\)C, 2 days & weight loss ratio \\ Sample A & 60 \({}^{\circ}\)C, 4 h; 180 \({}^{\circ}\)C, 2 days, 5 days, 10 days & apparent morphology; XRD; DSC; \\ Sample B & 60 \({}^{\circ}\)C, 4 h; 180 \({}^{\circ}\)C, 2 days, 5 days, 10 days & weight loss ratio; content analysis \\ Sample C & 60 \({}^{\circ}\)C, 4 h; 180 \({}^{\circ}\)C, 2 days, 5 days, 10 days & Apparent morphology; DSC; weight loss ratio; content analysis; _P-t_ test \\ \hline \end{tabular}
\end{table}
Table 1: Thermal stability analysis test items and storage conditions corresponding to different test materials.
###### Abstract  
Polarimetric synthetic aperture radar (SAR) remote sensing provides an outstanding tool in oil spill detection and classification, for its advantages in distinguishing mineral oil and biogenic lookalikes. Various features can be extracted from polarimetric SAR data. The large number and correlated nature of polarimetric SAR features make the selection and optimization of these features impact on the performance of oil spill classification algorithms. In this paper, deep learning algorithms such as the stacked autoencoder (SAE) and deep belief network (DBN) are applied to optimize the polarimetric feature sets and reduce the feature dimension through layer-wise unsupervised pre-training. An experiment was conducted on RADARSAT-2 quad-polarimetric SAR image acquired during the Norwegian oil-on-water exercise of 2011, in which verified mineral, emulsions, and biogenic slicks were analyzed. The results show that oil spill classification achieved by deep networks outperformed both support vector machine (SVM) and traditional artificial neural networks (ANN) with similar parameter settings, especially when the number of training data samples is limited.  
oil spill; polarimetric synthetic aperture radar (SAR); deep belief network; autoencoder; remote sensing +
Footnote †: journal: Applied  
Article
## 3 Results and Discussion
### Heat Resistance Performance Testing  
Figure 5 illustrates the compressive strength of the 3 DPFCs at different calcination temperatures. It is evident from Figure 5 that the compressive strength of the 3 DPFCs underwent significant changes with increasing calcination temperatures. Following calcination at 200 \({}^{\circ}\)C, the compressive strength of all 3 DPFCs noticeably increased. Specifically, sample EP80S1 exhibits a 31.8% increase in compressive strength after calcination at 200 \({}^{\circ}\)C, while sample EP40S2, with a similar density, only experienced a 19.4% increase. Similarly, the compressive strength of sample EP100S1 increases by 20.3%, while sample EP80S2 shows a modest increase of 1.5%. Evidently, samples with higher replacement rates of expanded perlite demonstrate greater improvements in compressive strength after calcination at 200 \({}^{\circ}\)C. This may be due to the expanded perlite reducing the foam content, which decreases the porosity and the number of large pores. Additionally, expanded perlite has better temperature stability than foam voids, resulting in less structural damage after high-temperature calcination [43; 44]. As the calcination temperature rose to 400 \({}^{\circ}\)C, the compressive strength of all samples significantly decreased, stabilizing after calcination at 800 \({}^{\circ}\)C, which is consistent with previous research findings.  
Figure 5: Compressive strength of 3 DPFCs after burning at different temperatures.
# Development of a Multi-Sensor GNSS-IoT System for Precise Water Surface Elevation Measurement
## 5 Discussion
### Advantages and Limitations of Integrated IoT Sensors  
Integrated IoT sensors, particularly the ultrasonic sensor, provide both benefits and limitations to the overall monitoring system. The ultrasonic sensor readings have an STD of 0.024 m for raw data and 0.019 m for the smoothed data in this experiment. The ultrasonic sensor on the pontoon collects data at 15 min intervals, whereas the GNSS data are recorded every 15 s. This mismatch means that the ultrasonic sensor cannot accurately capture the true distance between the system device and the water surface, introducing a variable with 2 cm uncertainty that affects the final WSE estimation accuracy. One potential solution is to increase the frequency of ultrasonic  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**“orig”**} & \multicolumn{4}{c}{**“high”**} \\ \hline
**Data Type** & **Mean** & **STD** & **Range** & **Max** & **Mean** & **STD** & **Range** & **Max** \\ \hline Epoch & \(-0.005\) & \(0.027\) & \(0.317\) & \(0.161\) & \(-0.007\) & \(0.020\) & \(0.167\) & \(0.086\) \\
10 min & \(-0.006\) & \(0.021\) & \(0.168\) & \(0.084\) & \(-0.006\) & \(0.020\) & \(0.166\) & \(0.084\) \\
1 h & \(-0.005\) & \(0.015\) & \(0.101\) & \(0.052\) & \(-0.006\) & \(0.014\) & \(0.090\) & \(0.052\) \\
6 h & \(0.001\) & \(0.007\) & \(0.038\) & \(0.020\) & \(0.000\) & \(0.007\) & \(0.030\) & \(0.019\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: System’s overall accuracy during testing period (unit: m).  
measurement; however, we surmise that the main application of the ultrasonic sensor at this stage is to detect blunder errors or significant pontoon movements, rather than to improve accuracy.
# Automatic Identification of Peanut-Leaf Diseases Based on Stack Ensemble
## 2 Materials and Methods
### Deep Learning
#### 2.2.4 DenseNet  
In deep-learning networks, as the depth of the network increases, the problem of gradient disappearance becomes more obvious. At present, many papers have proposed solutions to this problem. The core is to create a \"shortcut\" from the front layer to the back layer (such as a residual network). The idea of DenseNet is to directly connect all the layers on the premise of ensuring the maximum transmission of information between the middle layers of the network [30]. DenseNet is formed by connecting DenseBlock, convolutional layer, and pooling layer, with each DenseBlock containing convolution kernels of sizes 1 \(\times\) 1 and 3 \(\times\) 3. One advantage of DenseNet is that the network is narrower and there are fewer parameters. A large part of the reason is the design of DenseBlock. The dimension of output feature maps of each convolutional layer in DenseBlock is very small (less than 100). Other networks are as wide as hundreds of thousands. At the same time, this connection method makes the transfer of features and gradients more effective, and the network is easier to train.
# Deep Learning-Based Super-Resolution Reconstruction and Segmentation of Photoacoustic Images
###### Abstract  
Photoacoustic imaging (PAI) is an emerging imaging technique that offers real-time, non-invasive, and radiation-free measurements of optical tissue properties. However, image quality degradation due to factors such as non-ideal signal detection hampers its clinical applicability. To address this challenge, this paper proposes an algorithm for super-resolution reconstruction and segmentation based on deep learning. The proposed enhanced deep super-resolution minimalistic network (EDSR-M) not only mitigates the shortcomings of the original algorithm regarding computational complexity and parameter count but also employs residual learning and attention mechanisms to extract image features and enhance image details, thereby achieving high-quality reconstruction of PAI. DeepLabV3+ is used to segment the images before and after reconstruction to verify the network reconstruction performance. The experimental results demonstrate average improvements of 19.76% in peak-signal-to-noise ratio (PSNR) and 4.80% in structural similarity index (SSIM) for the reconstructed images compared to those of their pre-reconstructed counterparts. Additionally, mean accuracy, mean intersection and union ratio (IoU), and mean boundary F1 score (BFScore) for segmentation showed enhancements of 8.27%, 6.20%, and 6.28%, respectively. The proposed algorithm enhances the effect and texture features of PAI and makes the overall structure of the image restoration more complete.  
photoacoustic imaging; deep learning; super-resolution reconstruction; medical image segmentation +
Footnote †: journal: Medical Image Computing  
**Keywords:** photoacoustic imaging; deep learning; super-resolution reconstruction; medical image segmentation
## 4 Proposed Watermarking Scheme
### Watermark detection  
The following steps describe the detection algorithm:  
1. Apply the first three steps of the embedding on the possible watermarked and attacked image, \(I_{wa}\), by means of the secret key \(K\).
2. Construct the feature set \(F\)' with angles computed using the same procedure presented in step 5 of the embedding process. The angle \(\theta\)'\({}_{b}\) is determined using vectors \(v\)'\({}_{i}\) and \(v\)'\({}_{j}\) and their relative components \(x\)' = \(v\)'\({}_{k}\), \(y\)' = \(v\)'\({}_{l}\), \(z\)' = \(v\)'\({}_{j}\) and \(w\)' = \(v\)'\({}_{l}\), according to the following relation: \[\theta\text{'}_{b}=\frac{\arctan(y\text{'},x\text{'})}{\delta}\text{.}\] (11)
3. To detect the watermark in the extracted feature set \(F\)' we construct the following hypothesis test [53]: \[H_{0}: F^{\prime}=F+Z\] (12) \[H_{1}: F^{\prime}=F+\beta\cdot W+Z\]where \(F\) and \(F\)' are, respectively, the initial and the extracted feature sets, \(W\) is the watermark sequence and \(Z\) is the noise signal. Under the null hypothesis \(H_{0}\) the original image is unmarked, whereas under the alternative hypothesis \(H_{I}\) the watermark sequence \(W\) is embedded into the original image.
4. Compute the test statistic \(q\) with the following relation: \[q\ =\frac{Mean_{Y}\cdot\sqrt{L}}{Var_{Y}}\] (13) where \(Mean_{Y}\) and \(Var_{Y}\) are, respectively, the sample mean and the Root Mean Square Error (RMSE) of \(Y=\sum_{i=1}^{n^{2}}F_{i}^{\prime}\cdot W_{i}^{\prime}\), whilst \(L=n^{2}\) is the size of the feature set \(F\)'. According to [53], since the watermark strength factor \(\beta\) is a constant value, then it is possible to use \(W_{i}^{\prime}=W_{i}\), that is the sequence generated from the seed contained in the key. Intuitively, the value \(q\) measures the correlation between the watermark sequence and the extracted feature set.
5. The value of \(q\) computed above is compared with the acceptance threshold \(T\) which is calculated, under the hypothesis \(H_{I}\) and for large \(L\), with the relation: \[T=\frac{m_{H_{1}}}{2}\cong\frac{1}{2}\cdot\frac{\beta\cdot\sum_{i=1}^{n^{2}}W _{i}\cdot W_{i}^{\prime}+\sum_{i=1}^{n^{2}}Z_{i}\cdot W_{i}^{\prime}}{Var_{Y} \cdot\sqrt{L}}\,.\] (14) where \(m_{H_{1}}\) is the mean of the distribution of the output statistic \(q\) under the hypothesis \(H_{I}\). As proposed by [PERSON] _et al_. [53], in the above relation we disregard the noise factor \(Z\); then, if \(q\geq T\), we consider that the watermark sequence \(W\) is present in the feature set \(F\)'.
## 5 Conclusions  
In order to deal with the environmental noises from sources such as adjacent construction activities or traffic, an innovative gradual noise reduction method is proposedin this study, combining the I-SOBI and FFT with the A-CFS. The function of the first de-noising stage, as performed by the proposed I-SOBI method, can not only preserve the contributions of useful information from the separated signals during the reconstruction process, but also achieve the goal of reducing the impacts of noise components on the reconstructed signal. And in the second de-noising stage, the FFT with the A-CFS method is adopted to further deal with the residual high-frequency noises while preserving as much useful information as possible. This model demonstrates its effectiveness across various conditions and contributes to the accuracy of infrastructure assessment. However, the method also has its limitations. For instance, although the method has shown promising performance under the specific conditions evaluated, further validation is required to test its generalizability to other scenarios or signal types. Additionally, the computational complexity and the need for real-time processing are other aspects that could be addressed in future work to enhance its practical applicability.  
Conceptualization, R.W., H.W. and S.Z.; methodology, R.W., H.W. and S.Z.; software R.W. and H.W.; validation, R.W. and H.W.; formal analysis, R.W., H.W. and S.Z.; writing--original draft preparation, R.W. and H.W.; writing--review and editing, R.W., H.W. and S.Z.; supervision, R.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China, grant number 42201488; and the Beijing Key Laboratory of Urban Spatial Information Engineering, grant number 20230103.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.
# The Importance of Modeling Path Choice Behavior in the Vehicle Routing Problem
## 3 Path Choice Problem and Assignment (Behavioral Constraint)  
Two nodes of the pick-up and delivery route are connected by one or more paths. Between two consecutive nodes one or more paths can be perceived and one of these paths is chosen with a behavioral approach. The behavioral constraints are modelled by the analyst taking into account some elements, i.e., the decision of the users and the congestion.
## 5 Proposed Framework
### Training Time Compliance and Inference Latency Analysis
#### 5.3.2 Inference Latency Impact on Deployment  
The inference latency of the LSTM scheduler was evaluated, yielding a mean latency of 1.2 ms (95% confidence interval: [1.0, 1.4 ms]). The 99 th percentile latency reaches 2.8 ms, remaining compliant with the sub-5 ms budget required by URLLC services. For the zero-trust authentication module, token verification incurs a latency of 0.8 ms per API call, while behavioral analysis based on LSTM sequence validation adds 8.5 ms. Consequently, the overall end-to-end control loop, including training, inference, and security verification, maintains a total latency below 10 ms, fully meeting the Near-RT window of 10 ms to 1 s defined by O-RAN. All measured latencies and their compliance status are consolidated in Table 10.
## 4 Traditional MPPT Techniques
### Hill Climbing Algorithm  
Hill Climbing (HC) MPPT is another traditional technique used in solar power systems to optimize energy output by continuously adjusting the operating point to find the maximum power available from a PV module. In this algorithm, the duty cycle is adjusted directly, unlike P&O, which changes the current or voltage. Thus, the power output changes are observed to find the direction that maximizes module power until reaching a peak [52; 53]. The main advantages of HC MPPT include its simplicity and effectiveness in variable sunlight conditions. However, it can be less efficient in rapidly changing weather conditions and may suffer from oscillations around the MPP, potentially leading to reduced performance in comparison to more advanced MPPT algorithms [54]. A comparison of the traditional MPPT algorithms is shown in Table 2, where \(V\) is voltage, \(I\) is current, \(G\) is solar radiation, \(T\) is temperature sensor, \(A\) is analog, and \(D\) is digital.
# Label-Setting Algorithm for Multi-Destination \(K\) Simple Shortest Paths Problem and Application
## Appendix A
Pseudocode for the implementation of the heuristic using the proposed two-level organization of temporary labels```
Input: The network instance \(G(N,A)\) and origin \(s\) Output: The set of \(k\) simple shortest path labels \(P\)
1//Create a label \(d_{s}^{1}\) corresponding to the first path to the origin \(s\)
2\(d_{s,1}^{1}\gets s\)
3\(d_{s,2}^{1}\gets NULL\)
4\(d_{s,3}^{1}\gets 0\)
5//Create a single global temporary set \(T_{global}\) (min heap) and move \(d_{s}^{1}\) into \(T_{global}\)
6\(T_{global}\leftarrow\emptyset\)
7\(T_{global}\gets T_{global}\cup\{d_{s}^{1}\}\)
8//Create a local temporary set (min-max heap) for each node
9\(T_{i}\leftarrow\emptyset,\forall\;i\in N\)
10//Create an empty permanent set \(P\)
11\(P\leftarrow\emptyset\)
12//Initialize the label index and permanent label index for each node
13\(l_{s}\gets 1\)
14\(l_{t}\gets 0,\forall\;i\in N\setminus\{s\}\)
15\(p_{i}\gets 0,\forall\;i\in N\)
16 Repeat
17//Pick the minimum cost label \(min\big{(}T_{global}\big{)}=d_{u}^{p_{u}+1}\) from \(T_{global}\) and move it to \(P\), making it permanent
18\(\text{\emph{Since }}p_{u}\) labels are permanent for node \(u\), \(p_{u}+1\) is the next smallest temporary label index of \(u\)
19\(T_{global}\gets T_{global}\setminus\big{\{}\min\big{(}T_{global}\big{)}\big{\}}\)
20\(P\gets P\cup\big{\{}\min\big{(}T_{global}\big{)}\big{\}}\)
21//Increment the permanent label index of node \(u\)
22\(p_{u}\gets p_{u}+1\)
23//Move the next smallest temporary label of node \(u\) from its local level to the global level
24\(T_{u}\gets T_{u}\setminus\big{(}\min(T_{u})\big{)}\)
25\(T_{global}\gets T_{global}\cup\big{\{}\min(T_{u})\big{\}}\)
26 For each node \(v\) adjacent to \(u\)do
27 If\(l_{v}<k\)then
28 IfCycleCheck(\(s,\;u,\;p_
# Optimal Shape Design of Direct-Drive Permanent Magnet Generator for 1 kW-Class Wind Turbines
###### Abstract  
Direct-drive permanent magnet generators are becoming an attractive option for highly efficient small-scale wind turbines due to their high-power density and size reduction capabilities. In this study, the optimal shape design of a direct-drive permanent magnet generator for 1 kW-class wind turbines was conducted while considering power generation and weight. Half of the geometry of a single stage in the generator was considered for a electromagnetic analysis under given electrical parameters. In order to construct a response surface model, a sensitivity analysis was conducted on seven design parameters of the proposed generator. The desirability function was used to minimize the weight of the generator while meeting a requirement of the target specification. The results indicated that the optimized design parameters for the generator met the target specification while maintaining the generator's weight at the same level as the initial design model. From the comparisons with other research, the optimized generator exhibited a higher power generation/weight ratio than the generator with a rated capacity under 3 kW.  
desirability function; direct-drive permanent magnet generator; electromagnetic analysis; response surface method; shape optimal design; sensitivity analysis Article
## 3 Vehicle Activity Dataset  
In this part, we present a dataset called Vehicle Activity Dataset (VAD) that combines vehicle data and road scene information, aiming to drive advancements in eco-mobility. By integrating these two distinct sources of data, we provide a valuable resource for studying the intricate relationship between vehicle emissions and road conditions.  
Figure 1 shows the entire process of the VAD creation journey. The street scene imagery is first carefully captured along with associated Portable Emission Measurement System (PEMS) data. The PEMS is a pivotal tool for acquiring genuine emissions data from vehicles. Consisting of sensors and specialized equipment, PEMS accurately gauges the pollutants emitted by vehicles in real-world road conditions. The brain behind this innovation is none other than the CERTAM Regional Innovation Center. PEMS not only captures real-world emissions data but also provides comprehensive vehicle information, including GPS coordinates and speed. An important next step is the dataset extraction process, which carefully extracts complex road information from the collected images. These intrinsic street insights formed the basis for subsequent synchronization efforts to establish a harmonious alignment between the extracted images and the corresponding PEMS data. This synchronization seamlessly connects visual context with emissions-related information, resulting in a cohesive and meaningful dataset with enormous potential for transformative insights and actionable results.  
Figure 1: Overview of VAD construction.
## Materials and Methods
### Dependence of PRI on Sunlit Fraction  
The dependence of the remotely measured PRI on the sunlit fraction \(\alpha_{S}\) (assuming all other factors, including leaf optical properties, constant) is given by Equation (9) by [PERSON] [PERSON] and [PERSON] (1958) as:  
\[\mathrm{PRI}=\mathrm{PRI}_{leaf}-\frac{1}{2}\ln\left[\frac{\phi_{570}E_{531}}{ \phi_{531}\ E_{570}}\right]=\mathrm{PRI}_{leaf}-\frac{1}{2}\ln\left[\frac{ \phi_{570}}{\phi_{531}}\right]-\frac{1}{2}\ln\left[\frac{E_{531}}{E_{570}} \right], \tag{6}\]  
where \(\phi_{\lambda}\) is the spectral irradiance on leaf surface at the wavelength \(\lambda\), and \(E_{\lambda}\) is the TOC spectral irradiance on a horizontal surface. We can divide the spectral irradiance into the diffuse and direct components denoted with the superscripts \(\mathrm{dif}\) and \(\mathrm{dif}\), respectively, i.e., \(E_{\lambda}=E_{\lambda}^{dif}+E_{\lambda}^{dif}\). We used the approximation of the direct and diffuse irradiances on needle surfaces proposed by [PERSON] et al. (2019) (Equation (11)):  
\[\phi_{\lambda}=V_{dif}E_{\lambda}^{dif}+\alpha_{S}\frac{E_{\lambda}^{dif}P( \phi_{0})}{\cos\phi_{0}}, \tag{7}\]  
where \(P(\theta_{0})\) is the ratio of average projected area of the element in the direction given by the solar zenith angle \(\phi_{0}\), and \(V_{dif}\) is the average fraction of diffuse sky radiation reaching the visible leaves. For broadleaves, \(P(\theta)=\frac{1}{2}G(\theta)\); for an isotropic distribution of leaf (or needle) normals, \(P\equiv\frac{1}{4}\). Hence, we get:  
\[\mathrm{PRI}=\mathrm{PRI}_{leaf}-\frac{1}{2}\ln\left[\frac{E_{531}}{E_{570}} \right]-\frac{1}{2}\ln\left[\frac{V_{dif}E_{570}^{dif}+2\  
We used the non-linear regression line fitting function prediction in the R statistical software environment (version 1.0.136), to make a prediction between the Scots pine canopy sunlit fraction and the Scots pine canopy PRI for each scatter angle. We used the equation:  
\[\text{PRI}(\text{PRI}_{0},Q_{570},Q_{531},\alpha_{S})=\text{PRI}_{0}-\frac{1} {2}\,\ln\frac{1+0.2\alpha_{S}Q_{570}}{1+0.2\alpha_{S}Q_{531}}, \tag{11}\]  
where \(\text{PRI}_{0}\) is the value of PRI at \(\alpha_{S}=0\); and \(Q_{531}\) and \(Q_{570}\) are curve parameters that depend on the irradiance ratios, \(V_{dif}\), and the solar angle.