esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Compressed Sensing Rates and Video Resolutions on a PoseNet Model in an AIoT System
## 1 Introduction
Therefore, in this paper, we propose an AIoT system with the CS to efficiently provide pose estimation with a PoseNet model, which consists of an IoT-sensing device and an AIoT edge server. Additionally, we analyze the effect of CS rates and video resolutions in the IoT-sensing device on the pose score of the PoseNet model in the AIoT edge server. The main contributions of this paper are summarized as follows:  
* This paper analyzed the effects of CS rates and video resolutions on the PoseNet model in the AIoT system. We used CS among various compression techniques to mitigate data traffic. We chose this because it reduces the amount of data by randomly selecting pixels according to the CS rate and recovers at a rate close to perfection even with a small number of pixels. * In addition, the pose score of the PoseNet model was used to analyze the effect of CS on pose estimation and the effects of 10 CS rates and 3 video resolutions were analyzed. * Through this analysis, we can see that CS can effectively mitigate data traffic, but that the pose score does not significantly decrease. The rest of this paper is organized as follows. Section 2 describes the configuration and operation of the proposed AIoT system and explains the CS for data traffic mitigation and pose estimation with the PoseNet model in detail. Section 3 provides experimental results to analyze the pose scores of the PoseNet model according to CS rates and video resolutions. Finally, concluding remarks are presented in Section 4.
## 3 Results
### Effect of PEF Technology on the Trade Standards for Olive Oil  
None or the parameters used to determine commercial categories of olive oils were significantly affected by PEF treatment. All the values obtained for free acidity, peroxide value, K\({}_{232}\) and K\({}_{270}\) were compatible with the 'Extra virgin' olive oil (EVOO) classification (Table 3). In a similar way, the sensory evaluation of the oils by using a panel test did not detect the appearance of any defect or off-flavor in the oils obtained by PEF treatment. In each extraction trial, both oil samples, control and PEF-treated, obtained the same classification. Thus, all 'Manzanilla' oils were classified as EVOO and both 'Hojiblanca' oils were classified as 'Virgin' with a 'vuiney' defect of 1.4 and 2.20 in control and PEF samples, respectively (Table 4). However, the intensity of positive attributes (fruity, bitter and pungent) was generally higher in PEF oils than in control oils. Other parameters, such as fatty acids, sterols and waxes profiles were also analyzed, but no significant changes were detected either (data not shown). Regarding the oxidative stability measured with Rancimat apparatus, in general the oils obtained with PEF technology showed longer oxidation induction times than those of control oils in most extraction tests (Table 3).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & & **Oil Yield** & **Olive Pomace** \\ \hline \multicolumn{4}{c}{**(\% _v/iv_ Fruit)**} \\ \hline
1 st pilot test & Manzanilla Control & 13.9 & 15.5 \\  & Manzanilla PEF & 12.9 & 16.8 \\
2 nd pilot test & Manzanilla Control & 10.6 & 18.3 \\  & Manzanilla PEF & 11.4 & 18.5 \\
3 rd pilot test & Hojiblanca Control & 9.6 & 21.2 \\  & Hojiblanca PEF & 10.7 & 20.2 \\ Industrial test & Manzanilla Control & - & 16.6 \\  & Manzanilla PEF & - & 12.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Oil extraction yields and oil contents of pomaces obtained from ’Manzanilla’ and ‘Hojiblanca’ olive fruits extracted with a standard protocol (Control) and by applying pulsed electric field (PEF) technology.  
However, differences were not statistically significant. Similar increases (2-10%) were also detected by [PERSON] et al. (2016) using a lab scale PEF assisted extraction system and by [PERSON] et al. (2018) in 'Coratina' oils obtained by applying PEF technology after the malaxation phase.
## 4 Conclusions  
The large adsorption capacity and easily solid-liquid separation property of PPG-nZVI beads make them superior sorbents for practical application in water treatment devices such as stormwater infiltration facility. The adsorption kinetics data could be well fitted by the pseudo-second-order model for all kinds of heavy metals and the adsorption rate decreased followed in the order of Cu\({}^{2+}>\) Pb\({}^{2+}>\) Zn\({}^{2+}\), which is consistent with their reduction potential. Langmuir isotherm fits the data well and the maximum adsorption capacities decreased followed in the order of Pb\({}^{2+}>\) Cu\({}^{2+}>\) Zn\({}^{2+}\) (i.e., 16.02, 5.24, and 2.31 mg\(\cdot\)g\({}^{-1}\)) with the bead dosage of 5 g\(\cdot\)L\({}^{-1}\), respectively. The pH was found to have significant effect on the removal process. The maximum sorption capacity was observed at pH 5.5, indicating that competition occurred between H\({}^{+}\) and metal ions. While the ionic strength did not significantly influence the sorption process. The simulated stormwater infiltration pool with PPG-nZVI beads layer show large removal efficiency on treating the contaminated water containing Pb\({}^{2+}\), Cu\({}^{2+}\), Zn\({}^{2+}\), Cr\({}^{6+}\) and Cd\({}^{2+}\) and the removal rates were more than 65% at least. The results indicated that the PPG-nZVI beads might be effective sorbents as the filling materials in stormwater infiltration facility in purification of heavy metal contaminated water.  
The following are available online at [[http://www.mdpi.com/2076-3417/9/20/4213/s1](http://www.mdpi.com/2076-3417/9/20/4213/s1)]([http://www.mdpi.com/2076-3417/9/20/4213/s1](http://www.mdpi.com/2076-3417/9/20/4213/s1)), Figure S1: Typical wide scan XPS spectra for fresh PPG-nZVI beads (a) and for PPG-nZVI beads reaction with Pb\({}^{2+}\) (b), Cu\({}^{2+}\) (c) and Zn\({}^{2+}\) (d).  
Conceptualization, X.Z.; methodology, X.Z. and L.Y. software, L.Y., X.Z., and J.L.; validation, L.Y.; formal analysis, X.Z. and L.Y.; writing, L.Y. and X.Z.; review, J.L., Z.Z., and C.T.  
This research was funded by the Construction of High Level Teaching Teams in Universities of Beijing-the Youth Top-Notch Talent Cultivation Program (CIT&TCD201804051); National Natural Science Foundation of the China-Youth Project (51508017 and 51708014); the Fundamental Research Funds for Beijing University of Civil Engineering and Architecture (ZF14042 and X19036); the Fundamental Research Funds of Beijing Vocational College of Agriculture (XY-XF-17-13); Pyramid Talent Cultivation Project of Beijing University of Civil Engineering and Architecture.  
The authors have declared no conflict of interest.
# A Survey of Target Detection and Recognition Methods in Underwater Turbid Areas
## 4 Research on Target Detection and Recognition in Turbid Waters
### Underwater Image Restoration and Enhancement Methods
The simulation and real image test results showed that the method restores the image, thus improving the ability of target recognition and feature matching. [PERSON] et al. [42] proposed a method for image fusion based on the Mueller matrix to enhance the quality of underwater degradation images. Each Mueller matrix element image is given a weight and fused to generate a new image. The optimal weights are obtained by searching for values that maximize the image quality. The validity of this method was proved by comparison with the Mueller matrix image and the latest method using objective and subjective analysis. Moreover, the image was enhanced using analog weights. Due to the nature of the Mueller matrix, this method improves the underwater observation distance and image quality, and provides the enhanced images with information that is unavailable when conventional methods are used. Due to the absorption and scattering of light in water, color projection and poor contrast are often present in underwater images. [PERSON] et al. [43] proposed an underwater image restoration method based on a priori underwater features. They first established a powerful model to estimate the background light based on the characteristics of flatness, hue, and brightness, thus effectively mitigating color distortion. The red channel of the color-corrected image was then compensated to correct its transmission map. The rough transmission diagram was refined by combining it with a structure-guided filter.
## 2 Results
### Volatile Compounds in Proso Millet Wine
#### 2.2.4 Benzene Derivatives, Hydrocarbons, and Terpenes  
Twelve benzene derivatives, 12 hydrocarbons, and two terpenes were detected using four fibers, which showed high number and relative concentration in sample. Nine, seven, ten, and ten benzene derivatives, with subtotal concentrations 1403.20 \(\pm\) 60.07 \(\mathrm{\SIUnitSymbolMicro g}\)/L, 2320.13 \(\pm\) 47.47 \(\mathrm{\SIUnitSymbolMicro g}\)/L, 4662.34 \(\pm\) 203.48 \(\mathrm{\SIUnitSymbolMicro g}\)/L, and 11051.34 \(\pm\) 850.84 \(\mathrm{\SIUnitSymbolMicro g}\)/L, respectively, were detected in PA, PDMS, CAR/PDMS, and DVB/CAR/PDMS, respectively. DVB/CAR/PDMS showed a good absorbing effect for benzene derivatives. Two, 12, eight, and nine hydrocarbons, which had a subtotal concentration of 123.17 \(\pm\) 10.38 \(\mathrm{\SIUnitSymbolMicro g}\)/L, 6776.38 \(\pm\) 328.08 \(\mathrm{\SIUnitSymbolMicro g}\)/L, 3366.62 \(\pm\) 206.65 \(\mathrm{\SIUnitSymbolMicro g}\)/L, and 6015.91 \(\pm\) 397.40 \(\mathrm{\SIUnitSymbolMicro g}\)/L, respectively, were identified in PA, PDMS, CAR/PDMS, and DVB/CAR/PDMS, respectively. PDMS showed a good absorbing effect for hydrocarbons in number and concentration. Only two terpen were detected in samples with low concentration. Pinene and limonene were all detected in PDMS. Comparing with rice, abundant benzene derivatives and hydrocarbons were detected in proso millet wine. This could be due to crop differences. Usually, the source of benzene derivatives and hydrocarbons were from the external environment such as water and air [33]. These benzene derivatives and hydrocarbons which were detected in proso millet wine were also detected in proso millet [2]. Thus, these compounds are more likely to come from raw materials. Benzene derivatives were the most important odor component in the sample. Among five odor characteristic benzene derivatives, the OAV of four compounds was greater than 1, especially biphenyl, which had a high OAV in CAR/PDMS and DVB/CAR/PDMS, i.e., OAV reached 254.4 and 355.7. Other benzene derivatives, 1-methyl-naphthalene and 2-methyl-naphthalene, which both had an OAV greater than 1 in each fiber and ranged from 9.7 to 96.2, contributed phenolic aroma to the sample. The OAV of styrene ranged from 10.4 to 25.1 in PDMS, CAR/PDMS, and DVB/CAR/PDMS; styrene generally has a relationship to the sweet and floral characteristics of the wine. Hydrocarbons, which also were detected in the raw material [2], likely have little contribution to the odor of proso millet wine due to odorless feature. However, some hydrocarbons are present in the samples with relatively higher concentration, and may thus play roles in the overall flavor. The PDMS fiber coat was found to have good absorbability for terpen, which belonged to the unipolar compound. Similar to hydrocarbons and benzene derivatives, terpene could also come from proso millet [2]. Limonene generally relates to citrus aroma [25]; however, the OAV was less than 1 for all fibers, and thus there was little contribution to the odor.
# Probing Collisional Plasmas with MCRS: Opportunities and Challenges
## 3 Results and Discussion
### Increased Accuracy in the Permittivity
The data related to \(\Delta f\) is indicated in blue while red is used for \(\Delta(1/Q)\). Squares are used for the parameter study with respect to the number of averages with a fixed frequency step size of 1 kHz, circles are used for the study with respect to the frequency step size with 4096 averaged temporal responses per probing frequency, and triangles are used for the complete data set with the maximum number of averages and frequencies (including temporal averaging with a width of 2 \(\upmu\)s). The total duration of the experiment was approximately 4 h, whereas the top horizontal axis indicates how much shorter the measurement time would have been for the emulated settings. Note, that the reduction factor is idealized by neglecting, e.g., the time it takes to adjust the probing frequency on the microwave generator and to save the measurement data. The used approach was significantly less accurate in determining the \(Q\) of a resonant mode than the resonance frequency. This is not unexpected because \(Q\) contained the error in \(f_{\mathrm{res}}\) in the first order and more importantly the procedure for the determination of FWHM was less accurate. However, this exercise shows that the accuracy could be improved by increasing the number of averages and probing frequencies in an experiment. Other avenues to increase the accuracy will be given in Section 4. Figure 9: The coefficient of variation, i.e., the normalized standard deviation of \(\Delta(1/Q)\) (red) and \(\Delta f\) (blue) as a function of the number of averages (square) and the frequency step size (circle). The results indicated by triangular symbols show the effect of averaging 20 consecutive time steps.
## 2 Preliminaries
### Digital Signatures  
Public key cryptography enables the computation of digital signatures [19]. Given \(M\) (usually the hash digest of the data to be signed), the signer computes a digital signature over \(M\) using their private key. The resulting signature is denoted as \(\text{Sign}_{PK}(M)\), and \(\{M,\text{Sign}_{PK}(M)\}\) is a digest-signature tuple.  
Such a tuple is validated under signer's public key, \(PK\). A positive validation provides integrity, authentication and non-repudiation to the data hashed into \(M\).
## 4 Experiments and Results
### Time Consumption and Processing Speed  
The experiments involved Python 3.7 (TensorFlow and PyTorch deep learning framework) and Matlab (R2022b) on a workstation with an 8-Core CPU @3.70 GHz, 64 GB RAM, and Nvidia Quadro RTX 6000 24 GB GPU. Processing a single frame took \(60.5\pm 5.4\) ms (12.1 \(\pm\) 3.0 ms in object detection, including preprocessing, and \(48.3\pm 4.4\) ms in frame segmentation). Thus, we can analyze 16.5 frames per second (8.3 stereo shots per second), which excludes real-time processing in the current setup, primarily due to the DeepLab v3+ limitations. However, the framework is ready for asynchronous CADS applications and open for reimplementation involving parallel computing and other optimization concepts.
## 6 Experiments
### Performance Evaluation of the Ensemble Classification Algorithm
#### 6.2.1 Classification Precision Evaluation  
To determine the algorithms' performance with respect to gradual concept drift, sudden concept drift, and real data, we verified the classification precision of each of the five algorithms on three datasets. The classification results of the five algorithms on the HyperPlane dataset are shown in Figure 1.  
In Figure 1, three gradual concept drifts occurred at 25,000 instances, 50,000 instances and 75,000 instances (shown as vertical lines). As shown in Figure 1, the classification precision and sensitivity to gradual concept drift of test algorithm 8 are higher than those of the other four test algorithms. Test algorithms 4, 5, and 7 are less sensitive to concept drift than test algorithm 6 because of their fixed data window. However, since test algorithm 6 does not promptly update the weights of the base classifiers according to the classification error rate, its classification precision is lower than that of the other four test algorithms. Experimental results verify the effectiveness of the weight updating method of base classifiers and the variable sliding window method based on double thresholds. It also indicates that the proposed attribute reduction algorithm effectively selects important attributes and reduces the effect of noise on the classification results. Since the classification precision of test algorithm 6 is the lowest one, subsequent experiments were performed using test algorithms 4, 5, 7,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Classification Precision (\%)** & **Runtime (ms)** \\ \hline \(1\) & 98.23 & 269.87 \\ \(2\) & 98.37 & 268.54 \\ \(3\) & 98.45 & 268.39 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test results of different test algorithms (\(\beta=0.9\), \(\varepsilon=\delta=0.95\)) on the Wine dataset.  
of test algorithm 4 are lower than those of test algorithm 5. Test algorithm 8 has a quicker response time to sudden concept drift than the other two test algorithms because it adopts the attribute reduction method and the variable sliding window method based on double thresholds. The higher classification precision also indicates the effectiveness of test algorithm 8. Since the classification precision of test algorithm 4 is the lowest one, subsequent experiments were performed using test algorithms 5, 7, and 8. The average classification precisions of the three test algorithms on the Covertype dataset are shown in Table 3 and these experimental results also demonstrate the effectiveness of test algorithm 8.
# A Transfer Learning and Optimized CNN Based Maritime Vessel Classification System
## 3 Materials and Methods  
### Convolutional Neural Network Structure  
When it comes to problems involving the classification and recognition of images, DL models such as CNN are frequently used [6]. The target classification images can be immediately input into CNN models without requiring additional procedures involving the extraction of features and data reconstruction. A CNN consists of three main layers; convolutional layers, pooling layers, and fully connected layers [6]. By performing convolution operations in convolutional layers, it is possible to retrieve the feature patterns contained within images automatically. The complexity of the training is reduced in pooling layers by using local correlations to prevent overfitting. Local correlations can be used without losing essential features and information. All the components are linked together by fully connected layers, which are also responsible for producing the output.  
Transfer learning (TL), is the process of transferring the weights of a deep neural network (DNN) model that was previously trained on one dataset to another [14]. TL is used for DL models. The TL method has been effectively used in various image processing responsibilities. The reason for using TL is that the feature patterns learned by the bottom layers of CNN models are typically generic patterns that, with a pre-trained model, can be applied to various tasks. In contrast, the features learned by the top layers of CNN models are the only features specific to a given dataset [14]. As a result, the lower layers of CNN models are versatile enough to be immediately applied to various projects.  
It is possible to employ fine-tuning in the TL process of DL models to increase the efficiency of the TL method. During the process of fine-tuning, the majority of the model layers of the pre-trained model are \"frozen,\" which means that their weights are kept, but a few of the top layers are \"unfrozen\" so that the model can be re-trained on a new dataset. The learning model can update the higher-order features in the pre-trained model through fine-tuning [14]--the results better fit the target task and the dataset. The ship dataset is classified using several traditional CNN models in this article.
# Semantic-Aware Low-Light Image Enhancement by Learning from Multiple Color Spaces
## 5 Experiments
### Experimental Settings  
Datasets. The proposed framework was evaluated on two benchmark datasets: LOL dataset, which contains 485 low/normal-light training pairs and 15 test pairs captured in real-world scenarios, and LOL-v2 Dataset (Real Subset), an expanded version with 689 training pairs and 100 test pairs, offering greater diversity in scene types and illumination conditions.  
Evaluation Metrics. Five quantitative metrics were adopted:  
* Peak Signal-to-Noise Ratio (PSNR): Measures pixel-level reconstruction accuracy. Higher values indicate better fidelity.
* Structural Similarity Index (SSIM): Assesses structural preservation in luminance, contrast, and texture. Values range [0, 1], with higher scores denoting superior structural consistency.  
* Learned Perceptual Image Patch Similarity (LPIPS): Quantifies perceptual similarity using deep features. Lower values reflect better perceptual quality.
* Natural Image Quality Evaluator (NIQE): Blind image quality assessment without reference images. Lower scores indicate more natural outputs.
* Peak Signal-to-Noise Ratio under Low Resolution Conditions, LRC PSNR): PSNR computed under low-resolution conditions to evaluate robustness to resolution degradation.  
Comparative Methods. Both the original SKF framework and its modified variant utilize the LLFlow network as the baseline architecture, designated as LLFlow-SKF and LLFlow-SKF+, respectively.  
Implementation Details. For LLFlow-SKF, we conducted testing on GPUs allocated via Google Colab, utilizing the official LLFlow network codebase with identical testing configurations. The Semantic Feature Enhancement (SE) module was strategically integrated into the decoder of the LLFlow network to ensure optimal feature interaction. Notably, the Semantic-Guided Adversarial (SA) loss was excluded during training due to the unavailability of enhanced outputs in this phase. As for LLFlow-SKF+, the training process was fully executed on Google Colab GPUs under the same hardware and software constraints, ensuring consistency in experimental conditions.
## 4 Conclusions and Perspectives  
This study analyzed the temporal signature of SAR X-band signals acquired over irrigated grassland plots over several growing cycles. The objective of this work was to investigate the sensitivity of radar signals to soil moisture and vegetation parameters (LAI, vegetation height, biomass, and vegetation water content).  
Our results show that the radar signal in the X-band at both HH and HV polarizations is always sensitive to soil moisture variations, even with dense vegetation cover (HVE up to 1 m). This sensitivity  
Figure 12: Sensitivity of X-band ((**left panel**) \(\rm HH\), and (**right panel**) \(\rm HV\)) to VWC in three soil moisture (Mv) classes.  
decreases as vegetation density increases (higher sensitivity for biomass lower than 1 kg/m\({}^{2}\)). This result proves that the X-band radar signal penetrates the grassland cover (with vegetation height up to 1 m) and allows for the tracking of irrigation practices. In addition, the X-band at HV polarization is more sensitive to grassland parameters than at HH polarization; however, the potential use of the X-band for the monitoring of vegetation parameters is very limited. The X-band radar signal at HV polarization is useful for the monitoring of HVE up to 50 cm.  
The X-band radar signal is sensitive enough to variations in soil moisture to monitor soil moisture over grasslands. An inversion method based on backscattering models should be developed to analyze the precision of soil moisture estimates using X-band radar images over grassland. The objective of our future work is to develop methodologies based on the coupling of X-band SAR and optical data to estimate soil moisture. The arrival of Sentinel-1 and Sentinel-2 constellations, which have the ability to provide images with high repetition, will allow scientists to combine optical and radar images to estimate soil moisture in agricultural environments.