esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Virtual Image Generation Results
#### 4.1.2 Prediction Performance for Sentinel-2-Based Feature Combinations
CGANRESWR utilized resampled input images to consider the spatial resolution difference between Sentinel-1 and -2 images. Hence, it has a limitation to properly learn the complex spatial patterns from the pair images. Furthermore, the Sentinel-1 SAR imagery with a spatial resolution of 10 m and speckle noise filtering may be responsible for the failure to depict the spatial patterns of small objects. On the other hand, CGAN2S could overcome such limitations by considering the fine-scale VNIR bands as additional features, which yielded much better prediction results for both subareas with different land-cover types. As the actual Sentinel-2 imagery is available, the errors per pixel for the NIR and red-edge3 bands were calculated and further analyzed using the CDL (Figure 12). For both spectral bands, the magnitude of errors of CGAN2S was relatively smaller than those of CGANVIR or CGANRESWR. As expected, the superiority of CGAN2S over CGANRESWR was substantial in relation to the red-edge3 band. The overestimation (red color) and underestimation (purple color) of spectral reflectance were significantly reduced for CGAN2S. However, despite the significant reduction in errors when using CGAN2S, there still exist relatively significant errors in the western, northwestern, and northeastern parts of the study area. The land-cover types in the western part include developed areas. Thus, the land-cover distributions in this area are relatively more complex than the other crop parcel areas, which resulted in scattered error distributions in the NIR band prediction. The clustered errors in the northwestern and northeastern parts indicate the parcel-unit errors,  
Figure 11: Prediction results of NIR and red-edge3 bands for different CGAN models with the actual Sentinel-2 imagery. The two subareas marked with white boxes in the Sentinel-2 imagery are enlarged. mainly due to the learning process in a patch unit for all of the CGAN models. The different spatial distributions and data ranges of the SAR-based features within each patch unit may be responsible for the failure to restore the actual spectral reflectance values. These effects were relatively severe for the urban areas, including complex land-cover types and objects of different sizes. Since more than 70% of the pixels in the training patch are composed of crops, large errors may occur in the developed areas and roads. The errors at road and parcel boundaries were also more significant than those in the interior of the crop parcels, mainly due to the use of the features generated by a convolution operation in the generator. Despite these intrinsic errors, CGAN\({}_{\text{2S}}\) showed the best prediction performance.
## 4 Experimental Validation
### Metrics and Implementation Details  
#### 4.3.1 Evaluation Metrics  
In semantic segmentation, the intersection over union is a standard metric to assess segmentation models. It calculates the intersection over union ratio of ground truth and predicted binary masks for the foreground. Formally, it is defined as:  
\[\text{IoU}=\frac{|G\cap P|}{|G\cup P|} \tag{1}\]  
with \(G\) denoting the ground truth, and \(P\) the model prediction associated to the foreground.  
Another metric used is pixel accuracy, which evaluates the model by calculating the proportion of correctly classified pixels to the total number of pixels in the image as  
\[\text{PA}=\frac{\sum_{i=0}^{1}p_{ii}}{\sum_{i=0}^{1}\sum_{j=0}^{1}p_{ij}} \tag{2}\]  
where \(p_{ii}\) is the number of pixels whose prediction and ground truth are both \(i\). \(p_{ij}\) is the number of pixels whose prediction is \(i\) and label is \(j\).
## 5 Measurements and Experimental Results
### Experimental Results of BOW with Machine-Learning Classifiers  
Table 2 shows the time spent on obtaining the SIFT features, calculating K-means and generating histograms for the training data and obtaining the SIFT features and calculating the histograms for the testing data. This discrepancy is because the training data used the K-means results for the training data to create its own histogram.  
The experimental results show that the time spent obtaining the SIFT features from the training data does not increase as the number of centers chosen for the K-means method increases. However, the time spent on the other operations increases as the number of centers increases.  
Table 3 shows the accuracies based on different numbers of centers, including 10, 50, 100, 300 and 500. As shown in Table 3, the best accuracy of all scenarios was 80.5% and was achieved with SVM. In Table 3, five machine-learning methods, decision tree, LDA, SVM, K-NN and ensemble learning, are implemented using the features resulting from BOW based on different types of centers selected when using K-means. In Table 3, for those five machine-learning methods, the accuracies increased when selecting 10 centers, 50 centers and 100 centers when using the K-means method. The highest accuracy was achieved when using SVM.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Different Centers** & **Time to Obtain** & **Time to Obtain** & **Time to Calculate** & **Time to Calculate** \\
**(Using K-Means** & **SIFT Features for** & **SIFT Features for** & **Histograms for** & **Histograms for** \\
**Method** & **Training Data** & **Training Data** & **Training Data** & **Training Data** \\ \hline
10 centers & 472,358.918 & 2157.486 & 4253.557 & 834.538 \\
50 centers & 440,101.268 & 2415.997 & 4533.5 & 908.185 \\
100 centers & 526,735.405 & 2854.442 & 4851.46 & 963.334 \\
300 centers & 481,344.417 & 3307.601 & 4791.243 & 917.323 \\
500 centers & 444,823.379 & 4040.969 & 4748.997 & 874.902 \\ \hline \end{tabular}
\end{table}
Table 2: Time for BOW with the training and testing data (in seconds).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Classification Learners** & **10 Centers** & **50 Centers** & **100 Centers** & **300 Centers** & **500 Centers** \\ \hline Decision Tree & 53.8\% & 59.7\% & 62.1\% & 60.7\% & 60.9\% \\ Linear Discriminant Analysis (LDA) & 55.0\% & 64.2\% & 67.0\% & 59.7\% & 33.9\% \\ Support vector machine (SVM) & 68.9\% & 75.9\% & 77.4\% & 80.5\% & 80.0\% \\ K-Nearest Neighbor (K-NN) & 67.9\% & 74.9\% & 76.3\% & 74.7\% & 76.4\% \\ Ensemble Learning & 67.9\% & 75.4\% & 78.1\% & 75.9\% & 77.3\% \\ \hline \end{tabular}
\end{table}
Table 3: Accuracy of BOW based on different machine-learning classifiers.  
Figure 21: (a) Learning rate is \(1\times 10^{-4}\); (b) learning rate is \(1\times 10^{-5}\); (c) learning rate is \(1\times 10^{-6}\).
## 4 Decision Fusion Rule with Channel Errors Characterized by BER
Here, we focus on the channel-aware fusion rule, and the fusion statistic with the \(0<P_{e}\leq 1/2\) setup is considered. Taking the logarithm of Equation (15), we obtain the log-likelihood ratio based on BER (LRT-BER), shown as:  
\[\Lambda_{\text{BER}}=\sum_{i=1}^{N}\log\left[\frac{P_{d_{i}}+(P_{e}/(1-P_{e}))^{ 2y_{i}-1}(1-P_{d_{i}})}{P_{fa_{i}}+(P_{e}/(1-P_{e}))^{2y_{i}-1}(1-P_{fa_{i}})}\right] \tag{16}\]  
Let \(S_{0}=\{i:y_{i}=0\}\) and \(S_{1}=\{i:y_{i}=1\}\). Alternatively, Equation (16) can be further expressed as:  
\[\Lambda_{\text{BER}}= \sum_{i\in S_{1}}\log\frac{P_{d_{i}}+(P_{e}/(1-P_{e}))(1-P_{d_{i} })}{P_{fa_{i}}+(P_{e}/(1-P_{e}))(1-P_{fa_{i}})}\] \[+\sum_{i\in S_{0}}\log\frac{P_{d_{i}}+((1-P_{e})/P_{e})(1-P_{d_{i }})}{P_{fa_{i}}+((1-P_{e})/P_{e})(1-P_{fa_{i}})} \tag{17}\]  
The BER-based fusion statistic \(\Lambda_{\text{BER}}\) requires the knowledge of the sensor detection probability \(P_{d_{i}}\), the false alarm probability \(P_{fa_{i}}\) and the BER of the communication channel between each sensor and the FC. The fusion statistic is based on the assumption that the transmitted symbol is \(\{0,1\}\). It is straightforward to show that the symbol \(\{+1,-1\}\) leads to an identical fusion statistic with the symbol \(\{0,1\}\). For the proposed BER-based fusion rule, the modulation mode is reflected in the BER rather than the transmission symbol. By contrast, for the flat and slow fading channel characterized by the channel envelope \(h_{i}\), the modulation is reflected in the transmission symbols (\(\{+1,-1\}\) and \(\{0,1\}\)) [21, 22]. Notice that, for the decode-then-fuse strategy Equation (17), the communication channel between sensor \(i\) and the FC is modeled as the probability \(P(y_{i}|u_{i})\) for binary \(u_{i}\) and \(y_{i}\). Such a probability can completely characterize the uncertainties due to modulation/demodulation, reception mode, as well as the channel flat fading statistics' characteristics. As such, we can focus on the fusion process, and the communication errors between sensors and the fusion center are taken into account by the probability \(P(y_{i}|u_{i})\), which can be expressed as the function of \(P_{e}\). \(P_{e}\) in Equations (7), (9) and (10) is a function of the received SNR \(\bar{\gamma}_{b}\), which can be estimated by transmitting training symbols before fusing data transmission [29, 30, 31].
## 6 Results and Discussion
### Overview of Performance Measurement
#### 6.2.2 Sensitivity  
Sensitivity calculates the ratio of true positive predictions (TP) to the total number of actual positive instances, including both true positives and false negatives (FN). It measures the model's ability to avoid missing positive instances.  
\[Sensitivity=\frac{True\;Positives}{True\;Positives+False\;Negatives}\]
# An Intelligent Error Correction Algorithm for Elderly Care Robots
## 3 Intelligent Error Correction Algorithm  
In human-computer interaction, naturalness is an important influencing factor of interaction comfort, but naturalness will affect the recognition rate of gestures, so gesture recognition in a daily life environment will inevitably make mistakes, especially for the special group of the elderly who need to be accompanied. Therefore, in order to improve the gesture recognition rate of the elderly escort robot and improve the escort effect of the elderly escort robot, this study analyzes gesture recognition from two levels and establishes an error correction mechanism. On the one hand, we should count the recognition probability of each gesture based on the behavior of the elderly and establish a misread-gesture database and probability matrix. Finally, it is concluded that the recognition error is caused by the non-standard gestures made by the elderly. On the other hand, we deeply discuss the differences between channels in the fifth convolution layer and use these differences to correct errors based on the convolution layer under the rules of the game, as shown in Figure 2.  
Figure 2: General framework of intelligent error correction algorithm.
## 6 Experimental Results  
### Experimental Platform  
The electromagnetic signal acquisition platform constructed in this paper is shown in Figure 5a,c. The platform consists of a digital oscilloscope, PC, FPGA chip, and electromagnetic probe. The power supply is used to power the FPGA.  
The specific process is as follows:Firstly, The PC sends the plaintext and key required by the encryption algorithm to the FPGA. Secondly, the AES-128 encryption algorithm is running in the FPGA, and the electromagnetic probe collects the electromagnetic radiation signal of the near field of the chip at the same time. Thirdly, the electromagnetic probe transmits the collected signal to the oscilloscope, and the oscilloscope displays the signal and saves it. Finally, the oscilloscope sends a signal to the PC, and the PC sends the plaintext and key to the FPGA again, collecting a large number of required electromagnetic leakage signals.  
In terms of the specific use of FPGA, we ran our experiments on the widely-used side channel evaluation platform SAKURA-G, which consists of two chips. The main chip is a larger XC6 SLX75 for security implementations, controlled by an auxiliary Spartan6 XC6 SLX9 [42]. As a proof of concept, we considered the aforementioned AES encryption module as the targeted cryptographic core, implemented in the main FPGA and ran at a frequency of 24 MHz. The AES-128 module is a relatively small implementation, occupying 1002 Flip-Flops and 3189 LUTs. Its footprint in this FPGA is 1% of the total flip flops and 6% LUTs. In the meantime, we show the entire floorplan of the experimental setup in Figure 5b, and the AES core has been labeled.
# Self-Organized Fuzzy Neural Network Nonlinear System Modeling Method Based on Clustering Algorithm
## 2 Preliminary Knowledge
### Fuzzy Neural Network  
For nonlinear dynamic systems, the core problem is to fit the mapping relationship between the input and output variables using a fuzzy neural network, with a mathematical model that can be described by the mathematical expression Equation (3):  
\[y(t)=f(x(t)), \tag{3}\]  
where \(x(t)=[x_{1}(t),x_{2}(t),\ldots,x_{k}(t)]^{\mathrm{T}}\) is the input vector at moment \(t\), \(k\) is the dimension of the vector \(x\), \(y(t)\) is the output of the system at time \(t\), and \(f(\cdot)\) is this unknown nonlinear system.  
To model a multiple-input single-output system, we defined a four-layer fuzzy neural network model with the structure shown in Figure 1: this neural network can be divided into input layer, membership function (MF) layer, rule layer, and output layer [25].  
**The input layer:** This layer has a total of \(k\) neurons, each input neuron represents a different variable of the system, and the output value of this layer can be expressed as:  
\[u_{i}(t)=x_{i}(t),i=1,2,\ldots,k, \tag{4}\]  
where \(u_{i}(t)\) is the output value of the \(i\)th input neuron \(x(t)=[x_{1}(t),x_{2}(t),\ldots,x_{k}(t)]^{\mathrm{T}}\) at moment \(t\), and \(k\) is the dimension of the input variable determined by the system model.  
**The MF layer:** In this layer, the membership functions are Gaussian functions, which can effectively improve the generalization. Each membership function neuron is represented as:  
\[u_{ij}(t)=e^{-\frac{(x_{i}(t)-c_{ij}(t))^{2}}{\sigma_{ij}^{2}(t)}},j=1,2, \ldots,r;i=1,2,\ldots,k, \tag{5}\]  
where \(u_{ij}(t)\), \(\sigma_{ij}(t)\), and \(c_{ij}(t)\) are, respectively, the output value, width value, and center of the \(i\)th dimensional input of the \(j\)th neuron, and \(r\) is the number of membership functions.  
Figure 1: Fuzzy neural network structure diagram.  
**The rule layer:** There are \(r\) neurons in this layer. The output value of each rule neuron is the multiplication of the MF layers. The output value of the \(j\)th rule layer neuron can be expressed as:  
\[v_{j}=\prod\limits_{i=1}^{n}u_{ij}(t)=e^{-\sum\limits_{i=1}^{n}\frac{(x_{ij}(t)- x_{ij}(t))^{2}}{\sigma_{ij}^{2}(t)}},i=1,2,\ldots,k;j=1,2,\ldots,r, \tag{6}\]  
The obtained output needs to be normalized, as shown in Equation (7):  
\[h_{j}(t)=\frac{v_{j}(t)}{\sum\limits_{j=1}^{r}v_{j}(t)}=\frac{e^{-\sum \limits_{i=1}^{n}\frac{(x_{ij}(t)-x_{ij}(t))^{2}}{\sigma_{ij}^{2}(t)}}}{\sum \limits_{j=1}^{r}e^{-\sum\limits_{i=1}^{n}\frac{(x_{ij}(t)-x_{ij}(t))^{2}}{ \sigma_{ij}^{2}(t)}}},i=1,2,\ldots,k;j=1,2,\ldots,r, \tag{7}\]  
where \(h(t)=\left[h_{1}(t),h_{2}(t),\ldots,h_{r}(t)\right]^{\text{T}}\) is the normalized output.  
**The output layer:** In this layer, the values obtained from the previous layer are summed by the weights to obtain the output result  
\[y(t)=w(t)v(t)=\sum\limits_{j=1}^{r}w_{j}(t)h_{j}(t), \tag{8}\]  
where \(w(t)=\left[w_{1}(t),w_{2}(t),\ldots,w_{r}(t)\right]\) is the vector of weights between the regular layer neurons and the output neurons at moment \(t\).
# Peak Age of Information Optimization in Cell-Free Massive Random Access Networks
## 5 Simulation Results and Analysis
### Validation of Theoretical Analysis  
To optimize the network-wide average PAoI, we first verify the impact of channel access probability \(q\) on PAoI across groups. Consider a two-AP scenario: non-overlapping groups \(\mathcal{G}_{\{1\}}\) and \(\mathcal{G}_{\{2\}}\) each contain 75 MTDs, while the intermediate overlapping group \(\mathcal{G}_{\{1,2\}}\) contains 50 MTDs. All groups have a packet arrival rate of 0.01. The initial value of \(q^{\{1\}}\) is set to 0.01 and incremented by 0.005 in each step, while \(q^{\{1,2\}}\) and \(q^{\{2\}}\) remain fixed at 0.01. The simulation runs for 7 cycles.  
As shown in Figure 4, as \(q^{\{1\}}\) gradually increases, the PAoI for \(\mathcal{G}_{\{1\}}\) initially decreases and then starts to rise. The optimal \(q^{\{1\}}\) lies between 0.015 and 0.025, indicating potential optimization opportunities in the network. Furthermore, when \(q^{\{1\}}\) varies, the adjacent group \(\mathcal{G}_{\{1,2\}}\) is greatly affected by it, while the remote group \(\mathcal{G}_{\{2\}}\) shows smaller variations. This observation validates the spatial attenuation characteristics of cross-group interference, where interference effects diminish with distance across groups.
# Post-Quantum Digital Signature: Verkle-Based HORST
## 9 Verkle Tree  
Verkle trees are more efficient than Merkle trees and require fewer verifications. They are crucial for post-quantum cryptography because of their ability to reduce expenses, maintain high security, and get rid of redundant data and storage space. They provide effective verification processes by keeping only the information that is necessary. BecauseVerkle trees are more adaptable and need more hash computations for data integrity verification, they are ideal for handling large datasets.  
The Verkle Tree approach generates a tree by using Vector Commitments instead of cryptographic hash techniques, which we use in Merkle Tree. To build the tree, choose k components and then compute a Verkle Tree using files f0, f1,..., fn. Calculate a Vector Commitment (VC) for each subset of files to see if all of the memberships in that subset exhibit PRi with regard to VC [23]. Calculate the vector commitments across the tree until the root commitment is identified.  
Figure 2 shows the division of nine files into subsets of size k = 3 with a splitting degree of 3. With VC1, VC2, and VC3 remaining, we create VC and compute proofs for the subset. For commitments VC1, VC2, and VC3, membership proofs PR9, PR10, and PR11 are calculated in relation to commitment VC4. The Verkle Tree digest serves as the root commitment in the Vector Commitment VC4, which is built over these commitments.  
The proofing characteristics of Merkle and Verkle trees are different. Merkle trees need a proof that includes all nodes in order to take into account each sister node [24]. However, Verkle trees reduce the quantity of evidence required for value establishment by using \"batching nodes\" to verify numerous pathways at once. Additionally, they need less proof and have a wider scope. Because Verkle trees employ vector commitments in their hash algorithm, they are more efficient than Merkle trees.
# Continuous-Wave THz Imaging for Biomedical Samples
## 5 The Application of CW Imaging with Biological Tissues  
Cancer is one of the most common fatal diseases in both developed and developing countries. The incidence and mortality of cancer are still rising all over the world [73]. Most cancers have no specific clinical manifestations in the early stages, but the difference  
Figure 11: Schematic layout of the setup of CW THz ptychography.  
Figure 12: Schematic of the THz-computed tomography (CT) imaging system.  
between cancer tissues and normal tissues can be observed through various imaging methods. THz radiation is non-ionising, which is safe for biomedical samples. Recently, THz imaging technology has made some progress in identifying the intrinsic characteristics of cancer tissue samples. As a result of the abundant blood vessels or tissues oedema, tumour tissues usually contain much more water than normal tissues do. Since THz is more sensitive to the absorption of water, it can distinguish cancer and normal tissues according to their water content. Furthermore, the intensity of the THz images will vary according to the changes in tissue composition, such as structural changes in the cirrhotic cells [74]. The main applications of CW imaging technology focus on animal tissues in and ex vivo, and only a small amount of research has so far focused on superficial human tissues such as skin and ex vivo breast tissues.
## 3 Integrity Monitoring Method of Space-Ground Integration
### LEO Integrity Enhancement  
LEO satellites perform space-based monitoring of medium- and high-orbit navigation signals using onboard GNSS receivers. LEO satellites transmit integrity information to the medium- and high-orbit GNSS.
# Determination of Corrosion Rate in GAlvanized Pipes in Centralized Hot Water Supply Systems
###### Abstract
The experiment was carried out under static conditions at a temperature of 21 \({}^{\circ}\)C. It was found that Zn\({}^{2+}\) ions released from the anode sites interacted with OH\({}^{-}\) ions formed at the cathode sites, which led to the formation of poorly soluble Zn(OH)\({}_{2}\). Due to the corrosion reaction, the initial pH of seawater decreased to 4.4 at the anode sites due to the hydrolysis of metal ions. This fact led to the formation of a complex of various zinc corrosion products that consumed at least part of the OH- ions formed at the cathode. The results presented in the article showed that the rate of removal of zinc ions increased from 3.20 g/m\({}^{2}\) after 8 days (which corresponds to a zinc corrosion rate of 0.0167 gm\({}^{-2}\)h\({}^{-1}\)) to 4.80 g/m\({}^{2}\) (0.0067 gm\({}^{-2}\)h\({}^{-1}\)) after 30 days, when the corrosion potential reached the greatest negative value. After 90 days, zinc removal decreased to 4.50 g/m\({}^{2}\) (0.0021 gm\({}^{-2}\)h\({}^{-1}\)). The dependence of the corrosion rate of zinc in distilled water on temperature was estimated in [19]. At 60 \({}^{\circ}\)C, the corrosion rate was 58 g/m\({}^{2}\) per day (or 2.42 gm\({}^{-2}\)h\({}^{-1}\)). The beginning of intensive corrosion of the steel base of galvanized pipes is, as a rule, indicated when consumers receive drinking water of a rusty color. The initial accumulation of iron in the hot water system is facilitated by the following:  
* Increased (more than 0.3 mg/L) iron content in the cold water that enters the water heaters;
* Low water consumption compared to the designed value;
* Pipe areas that are not protected with zinc, especially the ends with their threaded connections;
* Steel and cast-iron shut-off valves without coating;
* A large length of pipelines;
* An increase in the temperature of the hot water. If measures are not taken to prevent corrosion, sediment accumulates on horizontal sections of pipes with low water flow velocity as well as in dead ends, for example, in uninhabited apartments or on sections of risers with drain taps. In this case, subsedimentary corrosion may occur. A sharp increase in the water velocity causes sediment churning and the flow of rusty water to consumers through water intake devices. It is known from the theory of corrosion processes that some ions, such as Fe\({}^{3+}\), Cu\({}^{2+}\), and Hg\({}^{+}\), are depolarizers [7; 20], i.e., they are able to accept electrons and recover. In the sediment layer, some of the iron is in ionic form. At the same time, the concentration of trivalent iron, Fe\({}^{3+}\), has higher values than the concentration of iron in tap water entering the water supply system of residential buildings from the outdoor city water supply. Therefore, the purpose of this study was to assess the effect of iron-containing sediment on the corrosion rate of zinc coatings and to study the protective effect of zinc in relation to an unprotected surface.