esa-sceva/satcom-chunk-collection huggingface dataset:
# A Blockchain-Based Privacy-Preserving and Fair Data Transaction Model in IoT
###### Abstract  
The rapid development of the Internet of Things (IoT) has resulted in vast amounts of widely distributed data. Sharing these data can spur innovative advancements and enhance service quality. However, conventional data-sharing methods often involve third-party intermediaries, posing risks of single-point failures and privacy leaks. Moreover, these traditional sharing methods lack a secure transaction model to compensate for data sharing, which makes ensuring fair payment between data consumers and providers challenging. Blockchain, as a decentralized, secure, and trustworthy distributed ledger, offers a novel solution for data sharing. Nevertheless, since all nodes on the blockchain can access on-chain data, data privacy is inadequately protected, and traditional privacy-preserving methods like anonymization and generalization are ineffective against attackers with background knowledge. To address these issues, this paper proposes a decentralized, privacy-preserving, and fair data transaction model based on blockchain technology. We designed an adaptive local differential privacy algorithm, MDLDP, to protect the privacy of transaction data. Concurrently, verifiable encrypted signatures are employed to address the issue of fair payment during the data transaction process. This model proposes a committee structure to replace the individual arbitrator commonly seen in traditional verifiable encrypted signatures, thereby reducing potential collusion between dishonest traders and the arbitrator. The arbitration committee leverages threshold signature techniques to manage arbitration private keys. A full arbitration private key can only be collaboratively constructed by any arbitrary \(t\) members, ensuring the key's security. Theoretical analyses and experimental results reveal that, in comparison to existing approaches, our model delivers enhanced transactional security. Moreover, while guaranteeing data availability, MDLDP affords elevated privacy protection.  
blockchain; Internet of Things; data transaction; local differential privacy; verifiable encrypted signature +
Footnote †: journal: Applied sciences
# Wheat Yield Estimation Study Using Hyperspectral Vegetation Indices
## 3 Results
### Multiple Methods to Build Models Based on Vegetation Index
#### 3.3.2 Multiple Stepwise Regression Method  
The Stepwise Multiple Regression method, similar to Multiple Linear Regression, is used to select vegetation indices without multicollinearity. In the final selection, NDWI  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Vegetation Index**} & \multicolumn{2}{c}{**Pearson Correlation**} & \multirow{2}{*}{**Significance**} \\  & **(Taken in Absolute Terms)** & & \\ \hline NDRE & 0.521 & 0.006 \\ GARI & 0.526 & 0.003 \\ PRI & 0.533 & 0.014 \\ DVI-1 & 0.564 & 0.001 \\ DVI-2 & 0.567 & 0.001 \\ DVI-3 & 0.568 & 0.001 \\ EVI-2 & 0.571 & 0.001 \\ TBWI & 0.581 & 0.001 \\ NDMI & 0.602 & 0.002 \\ NDII & 0.676 & 0.001 \\ VDI & 0.678 & 0.001 \\ NDWI & 0.705 & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Correlation between vegetation index and yield.  
and mSRI-2 were the two variables included in the regression model. The results indicate that the first model was built on NDWI and yield, while the second model involved NDWI, mSRI-2, and yield. It's worth noting that the first model, with an R\({}^{2}\) value of 0.497, exhibited a lower accuracy compared to the second model, with an R\({}^{2}\) value of 0.612. In the subsequent evaluation of yield estimation model accuracy, the first model can be excluded, and an accuracy assessment can be performed using the second yield estimation model. This selection process ensures a more accurate estimation of wheat yield.
# Deep Learning-Based End-to-End Carrier Signal Detection in Broadband Power Spectrum
## 4 Experiments
### Model Training  
As can be seen in Table 1, we implement the SCN in PyTorch [34]. Additionally, the parameters of the network are randomly initialized under the default setting of PyTorch with no pre-training on any external dataset. During the training process, we set the input length of the network to 32,768, which leads to an output length of 8192. Moreover, we adopt Dropout [35] prior to RegNet to reduce overfitting.  
Adam optimization method [36] is utilized to optimize the overall training loss. Moreover, we employ a batch size of 32 and the network on 2 NVidia GeForce RTX 3080 Ti graphic process units (GPUs) (Santa Clara, CA, USA) and Intel(R) Bronze 3204 CPU (Santa Clara, CA, USA), Ubuntu 20.04 operation system (London, UK). Furthermore, all the models for 150 epochs are trained with a learning rate strategy of cosine annealing warm restarts [37].  
This paper proposes two different methods to train the proposed SCN. The first method compares the different residual downsample layers with the same training simulation sam  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Implement Library** & PyTorch 1.10.0 \\
**Hardware Platform** & 2 GeForce RTX 3080 Ti GPU, Intel(R) Bronze 3204 CPU \\
**Operation System** & Ubuntu 20.04 \\
**Model Input Length** & 32,768 \\
**Batch Size** & 32 \\
**Training Epochs** & 150 \\
**Dropout Probability** & 0.3 \\
**Optimizer** & Adam \\
**Learning Rate Strategy** & Cosine Annealing Warm Restarts, \\  & initial value \(2\times 10^{5}\), T\_0 = 10, T\_mult = 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: SCN training parameters.  
Figure 5: The carrier IoU.  
ples, while the second method compares the different numbers of training simulation samples with the same residual downsample layers, as shown in Figures 6 and 7, respectively. In Figure 6, 20,000 simulation samples are used to train the networks. The residual downsample layer ranges from 6 to 13 and the results demonstrate that both the PSD and BW losses converge with the training epoch. Subsequently, they increase and decrease with the increasing number of residual downsample layers. In Figure 7, a total of 10,000, 20,000, 40,000, 60,000, 80,000, and 100,000 simulation samples are used to train SCN-11x. Moreover, the results demonstrate that both PSD and BW losses converge with the training epoch. Subsequently, they increase and decrease with the increasing number of training simulation samples. Nevertheless, the specific converged loss values indicate that the scale of residual downsample layers significantly influences the scale of training samples.
## 3 Results
### Assessment of the Bottom Sediment Pollution Level  
[PERSON] et al. developed a total pollution index \(Zc\) to assess the pollution of bottom sediment [68]. The calculation of the total indicator for metals concentration in the bottom sediment was carried out according to the following Equations (1) and (2):  
\[Kc=\frac{Ci}{Cb} \tag{1}\]  
\[Zc=\sum Kc-(n-1) \tag{2}\]  
where \(Kc\) is concentration factor; \(Ci\) is pollutant concentration; \(Cb\) is pollutant concentration at the background point; \(n\) is the number of elements to be determined.  
Background concentrations of pollutants are calculated in consideration of the indicators of the considered metals in the lithosphere [64; 65; 66; 67; 68; 69]. Table 3 shows that the concentrations of metals in the bottom sediment are an order of magnitude lower than their background values, with the exception of cadmium, which depends on the adsorption capacity of mobile forms of this metal in neutral and weakly alkaline environments [64; 62; 46], i.e., Greysems and meadow-boggy soils, which are characterized by high biological activity. The metal concentration is also dependent on the acidity of the environment, with increased solubility and increased concentrations of metals in the bottom sediment occurring with decreasing pH [63; 64]. Low concentrations of zinc, copper and lead indicate their low migration activity in the reservoir sediments and the low intensity of the leaching processes [70].  
The assessment of the degree of contamination of the reservoir water area based on the concentration of chemical elements in the bottom sediment showing a low level of \(Zc=1.26\), which is characterized as \"within the background limits\", and in terms of sanitary and toxicological hazard considered to be acceptable (Tables 3 and 4).
# An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels
## 3 Methodology
### Adaptive Weighted Learning Network  
Inspired by meta-learning [41], we expect the model to be able to learn the relationship between training samples and weighting hyperparameters automatically. At the same time, we aim to achieve this without incurring high computational costs. To accomplish this, we construct an adaptive weight learning network (AWNet) based on a multilayer perceptron. The network consists of an input layer, a hidden layer (with 100 neurons) and an output layer. The hidden and output layers consist of a linear model and an activation function. To avoid the issues of gradient explosion and gradient disappearance, ReLU converts the input gradient into 1 and 0. Therefore, the activation function of the hidden layer is ReLU, and the activation function of the output layer is Sigmoid. Full connectivity is used between the different layers. Specifically, the complexity and noise level of the training samples are expressed quantitatively by two metrics: RES and \(\delta\). The two metrics are then used as the input of AWNet to fit their relationship with \(\alpha\) and \(\beta\), which can automatically weigh each sample. The forward computation procedure of AWNet can be written as  
\[(\alpha,\ \beta)=\mu\big{(}W_{2}^{t}\big{(}\text{ReLU}\big{(}W_{1}^{t}(\text{ RES},\delta)\big{)} \tag{6}\]  
where \(\mu\) and ReLU are the Sigmoid and ReLU activation functions, respectively. \(W_{1}^{T}\)and \(W_{2}^{T}\) are the feature weights between the input layer and the hidden layer and between the hidden layer and the output layer, respectively.  
Algorithm 1 describes the process for updating parameters of our proposed AWNet and the classifier during the training stage. To ensure the reliability of the classifier's output probability, we employ a fixed-weight APL (\(\alpha=1,\ \beta=1\)) for pretraining the classifier in the first \(t_{p}\) epochs. In our experiments, we set the number of pretraining epochs \(t_{p}\) to 3. Detailed information about the setting of \(t_{p}\) will be introduced in Section 4.5. Once the pretraining is completed, the classifier proceeds to the formal training stage. In this stage, we calculate the RES and \(\delta\) for each sample by using the output probabilities of the classifier. These two metrics are then input into AWNet to obtain \(\alpha\) and \(\beta\) for each sample. Subsequently, we update the parameters of the classifier by Equation (1) and then input the same samples into the updated classifier to update the AWNet parameters with the new loss. The classifier and AWNet parameters are iteratively updated until training is complete. Our method can be trained directly on noisy data \(D_{n}\) without the need for additional clean data as a guide.
## 2 Materials and Methods
### Stationary Scenario Field Works  
Six Trimble R10 GNSS receivers mounted on tribrachs were attached to tripods. The re-creation of the geometrical configuration of GNSS receivers from the railway mobile platform was obtained using the tachymetric method. Trimble S7 robotic total station was set up on the _Rybina_ point of the POLREF control network. Coordinates of the adjacent lower-order network control points for the instrument station tying (connecting) were  
Figure 1: Configuration of six GNSS receivers on the railway platform.  
obtained from the local department of cadaster and land surveying. Total stations' 1'' angular and 1.0 mm + 2.0 ppm linear errors produced millimeter stake-out errors. Also, the shape of the receivers' distribution frame was controlled with certified linear measures, as was practiced in the railway campaign measurements.  
Receivers were staked-out in two parallel lines with a distance of 7.000 m, representing along-track configuration on the platform. Cross-line offsets of receivers equaled 0.750 m. Also, the right angles between receivers' positions were maintained in the final staked-out configuration consisting of front-line (left-front-LF, central-front-CF, and right-front-RF) and back-line (left-back-LB, central-back-CB, and right-back-RB) subgroups. Devices used in the field works are presented in Figure 2.  
The bottom panel of Figure 2 presents a time series of GNSS static observations in six Trimble R10 receivers on 20 January 2021. The first session, Stat1, lasted for approx. 20 min. Three other sessions, Stat2, Stat3, and Stat4, lasted for approx. 30 min each. The synchronous recording of satellite observations with a frequency of 20 Hz was performed without major interference. A short break in the recording occurred in the RB receiver during the last 10 min of session Stat2.  
The GNSS observations were post-processed using the Leica Infinity software. The calculation used synchronous satellite observations from the reference stations of geodetic active geodetic networks ASG-EUPOS and VRSnet.pl located in Gdansk (GDSK), Elblag (ELBL), and Braniewo (BRWO). The locations of the survey site (Rybina) and the reference stations are presented in Figure 3. Precise final orbits in SP3 files were downloaded from the IGS server.
# Research on the Pavement Performance of Slag/Fly Ash-Based Geopolymer-Stabilized Macadam
## 3 Results and Discussion
### Microscopic Test
#### 3.5.2 FTIR Analysis  
Figure 7 shows FTIR images of slag/fly-ash-based geopolymer-stabilized macadam (GFA-4) after curing for 7 d and 28 d. The two characteristic peaks appeared near 1500 cm\({}^{-1}\) and 3500 cm\({}^{-1}\) represent the absorption bands of water molecules. The characteristic peak near 1500 cm\({}^{-1}\) is vibration peak of H-O-H, and the characteristic peak near 3500 cm\({}^{-1}\) is the vibration peak of O-H. The former is mainly related to free water, while the latter is related to crystal water and free water in the test piece. Therefore, both adsorbed water and crystal water exist in the test piece [31].  
The characteristic peak near the wavelength of 1000 cm\({}^{-1}\) is the symmetric stretching vibration absorption peak of Si-O-Al in the geopolymer. It is found that the strength of the 28 d test piece is stronger than that of the 7 d test piece because the hydration reaction is relatively adequate along with the formation of more aluminosilicates as the curing age prolonged. At the same time, glass phases are constantly dissolved in the alkaline solution to generate more silica-alumina tetrahedral network structures, thus improving the strength [32,33].  
Figure 5: SEM images of slag/fly-ash-based geopolymer stabilized macadam (**a**) 100 \(\upmu\)m. (**b**) 40 \(\upmu\)m.  
Figure 6: ITZ images of slag/fly-ash-based geopolymer-stabilized macadam. (**a**) 100 \(\upmu\)m. (**b**) 40 \(\upmu\)m.
# Coupled Model of Heat and Power Flow in Unventilated PV/PCM Wall-Validation in a Component Scale
## 3 Results and Discussions  
### Measurements  
Measurements used for the numerical model validation were planned and conducted in such a way as to be adequate for future real-scale application of test panels as facade-mounted components. Thus, the mock-up was mounted vertically, south-oriented. To comprehensively analyze the performance of components, local climatic data was recorded during the tests. As the most important parameter influencing power generation is solar irradiance, it was measured directly on the surface of the PV panel, using a second class pyranometer LP02 Hukseflux. In addition, air temperature, wind direction and velocity were also monitored with a 1-min time step. Measurements were carried out for two selected days characterized by clear sky conditions between 9 a.m. and 6 p.m. The measured values of solar irradiance, air temperature, and wind velocity are presented in Figure 3.  
In order to ensure the optimal work of PV panels, MPPT (maximum power point tracking) technology was applied to the charge controller, which served two purposes. It optimized the current parameters for battery charging but also transferred the current to the receiver--LED lamps. Moreover, for the panel performance assessment and its comparison with the simulation results a Nemo D4-DC power meter for monitoring DC power supply with the accuracy of class 1 was used. The last parameter measured that was used for model validation was the temperature on the back side of the PV panel (between PV and PCM). It was measured by three PT-1000 sensors located in the central part of the panel. The accuracy of the sensor was \(\pm\)0.5 \({}^{\circ}\)C. The schematic representation of the experimental system is presented in Figure 4.  
Measured values of the power generated by PV and temperature on its backside were presented in Figure 5. It can be observed that, despite slightly higher irradiance recorded during the second day of the measurements, the power generation was higher during the first day. It should also be noted, based on the data presented in Figure 3b, that due to higher wind velocity, the temperature on the back side of the PV panel decreased, which resulted in an increase in the efficiency of the PV panel. Observation of such phenomena, the positive influence of temperature on power generation, confirmed the need for the development of the temperature control.  
Figure 3: Measured weather data: (**a**) external air temperature and irradiance at vertical surface, (**b**) wind velocity.
## 6 Personalized Recommendation System
### Key Frame Detection with Differential Evolution
#### 6.2.3 Detecting Key Frame Using Differential Evolution Algorithm  
In the past decade, evolutionary algorithms have been extensively used in various problem domains and have successfully and effectively found a solution that is nearly optimal [39]. In this paper, differential evolution algorithm [40] is utilized to detect the key frame. As shown in Figure 7, it performs iterative execution of mutation-crossover-selection process cycle over the set of candidates in the present population.  
The differential evolution algorithm starts its search by a population initialization that randomly initializes a set of problems dependent on candidate solutions. Then, the fitness of the candidates is computed by the problem-specific objective function. After that, the child of each parent in the initial population is generated. The generation process included three steps--mutation process, crossover process, and selection process--will be executed iteratively until the user-defined stop criteria is met. Thus, at the end of each generation, a new group of survivors will be generated. The framework is designed in such a way as to generate 10 key frames for each video. Parameters used in differential evolution algorithm along with their values are described below.  
In differential evolution algorithm, there are many hyper parameters to be determined before the execution. The size of each vector \(\mathbf{D}\) that decides the number of key frames required for the video processing application is set to 10 constantly in our experiment. The population size \(NP\) decides the number of candidates in the population. In our experiment, \(NP\) is set to 10. The scaling factor \(F\) is the step size used for differential mutation. In our experiment, \(F\) is set to 0.9. The crossover weight \(Cr\)  
Figure 6: Inception model with dimension reductions.  
Figure 7: Algorithmic structure of classical differential evolution algorithm.  
is the crossover probability used in the crossover operation to determine whether the components of the trial vectors are from the parent vector or mutant vector. In our experiment, \(Cr\) is set to 0.6. The maximum number of generations _MaxGen_ is set to 40, for each run. The population is a set of _NP_ candidate vectors with dimension of **D**. The initial population is initialized by randomly selecting some frames in the video as the key frames. The average Euclidean distance is used as the fitness function that evaluates the quality of each candidate solution in the population.  
Figure 8 shows part of the detection results of several mobile videos, in which five frames are detected at equal intervals from all video frames and it displays them in the first column of the image. The second row is the detected key frame; it can be seen that the key frame can fully summarize the video content so as to help the user previewing the video content in general.
## 2 Materials and Methods
### Biomethane Potential Test  
To investigate the effect of the pre-operations on CH\({}_{4}\) yield from CS, a biomethane potential (BMP) test was conducted in triplicate bottles with a total volume of 500 mL and a working volume of 400 mL. The parameter settings of the BMP test referred to some suggestions from [PERSON] et al. [25] and the results of some pre-experiments (Supplementary Data Figures S2 and S3). In particular, the inoculum injection was operated in the ratio of 1.6 (inoculum/substrate) on the basis of VS (volatile solids), and the substrate concentration was 12.5 gVS/L. No additional nutrients or alkalinity was added. The headspace gas of each reactor was replaced with pure N\({}_{2}\) before starting the test. The BMP test was additionally performed under the thermophilic conditions (55 \({}^{\circ}\)C) and intermittent stirring (30 s clockwise, 30 s counterclockwise, and then 60 s rest in each cycle) for dozens of days until the daily methane yield was less than 1% of the total methane production. Microcrystalline cellulose was used as a positive control.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameters** & **Corn Stover** & **Inoculum** & **Liquid Digestate** \\ \hline Total solids (\%) & 91.48 \(\pm\) 0.10 & 13.54 \(\pm\) 0.05 & N/D \\ Volatile solids (\%) & 84.55 \(\pm\) 0.19 & 6.51 \(\pm\) 0.04 & N/D \\ Total organic carbon (\%) & 43.11 \(\pm\) 0.77 & 25.77 \(\pm\) 0.36 & N/D \\ Total nitrogen (\%) & 1.02 \(\pm\) 0.03 & 2.61 \(\pm\) 0.07 & 0.15 \(\pm\) 0.00 \\ C/N ratio & 42.28 \(\pm\) 0.74 & 9.89 \(\pm\) 0.29 & N/D \\ Alkalinity (g CaCO\({}_{3}\)/kg) & 8.54 \(\pm\) 0.02 & 10.23 \(\pm\) 0.35 & 1.87 \(\pm\) 0.11 \\ Cellulose (\%) 1  & 33.83 \(\pm\) 0.36 & N/D & N/D \\ Hemicellulose (\%) 1  & 26.10 \(\pm\) 0.33 & N/D & N/D \\ Lignin (\%) 1  & 19.10 \(\pm\) 0.89 & N/D & N/D \\ MLSS (g/L) & N/D & N/D & 3.72 \(\pm\) 0.13 \\ MLVSS (g/L) & N/D & N/D & 2.64 \(\pm\) 0.14 \\ pH & N/D & 7.87 \(\pm\) 0.08 & 7.53 \(\pm\) 0.09 \\ \hline \end{tabular}
\end{table}
Table 1: Characteristics of materials.