esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving the Robustness of Model Compression by On-Manifold Adversarial Training
## 5 Experiments  
### Datasets and Models  
To investigate the effectiveness of our proposed method, we conducted experiments with different datasets. Details on the dataset and the target classifier for each dataset are described below.  
* Simulation dataset: the simulation data described in Section 4.1.1, in which on-manifold adversarial examples have been generated based on known data distribution. The simulation dataset consists of 70,000 instances with 16-dimensional inputs belonging to four different classes. We used the modified LeNet5 [55] as the classifier, which consists of two convolutional layers with max-pooling (\(5\times 5\) kernel size; 32, 64 channels for simulation dataset; 64, 128 channels for MNIST), and two fully connected layers.
* MNIST [56]: the dataset for handwritten zip-code digit classification problem, originally intended for the faster distribution of physical mail in the US post offices--the images were taken with low-resolution (\(28\times 28\)) camera sensors. This dataset consists of 70,000 grey-scale images, and the train/test split ratio is \(6:1\). For the target classifier, we used the same model as in the simulation dataset with three fully connected layers.  
* Fashion-MNIST [57] (FMNIST): FMNIST consists of 10 different categories of fashion product images. It also includes 70,000 grey-scale images, and the train/test split ratio is 6 : 1. We used a CNN with three convolutional layers (\(4\times 4\) kernel size; 32, 64, 128 channels) and three fully connected layers.
* CIFAR-10 [58]: CIFAR-10 consists of 60,000 RGB-color images, and the train/test split ratio is 5 : 1. For the classifier, we used ResNet-18 [59].
* UCI human activity recognition [60] (HAR): HAR dataset consists of nine embedded sensors and data from accelerometers and gyroscopes in smartphones. The task of the dataset is to classify six different human activities with the sensor data received from smartphones. There is a total of 10,299 instances, and we used a train/test split ratio of 7 : 3. For the classification problem, we adopted a one-dimensional convolutional neural network model with three convolutional layers and three fully connected layers.
## 3 Experimental Analysis of the Acoustic Cloak
### Experiment Results: Wave Guiding Effect of the Cloak Shell
The highest sound pressure values were obtained in the (_Cx_ = 200, _Cy_ = 140) position, the outermost boundary of the cloak shell, which verified that the waves guided by the cloak shell were restored at the zone. The sound field of the combined object and cloak shell is shown in Figure 25d. In order to minimize the effect of the local sound source, the sound field of Figure 25a was taken as the reference field in order to normalize the other cases. Figure 26a-c plot the normalized results of the object, the cloak shell, and the object and the cloak shell combined, respectively. We superimpose the normalized sound field results and the real structure position of the cloak shell in one figure and show them in the right column of Figure 26 for readers to more easily understand the behavior. Figure 26a shows the object in the space, and the normalized results are clear evidence of the typical scattering field conforming to the numerical in Figure 13 of the uncloaked object case. The object shadowed the incident wave and formed the shadow region behind the object. The measured low pressure zone between positions _Cx_ = 130-270 mm, _Cy_ = 55-150 mm is the shadow region. The normalized field of the cloak shell in Figure 26b shows the obvious wave guiding effect of the cloak shell. The sound  
Figure 25: Sound field measurements obtained within the zone affected by the cloak shell: (**a**) only the specimen supporter as a reference sound field; (**b**) the object; (**c**) the cloak shell; (**d**) the object and the cloak shell. field distribution peak values in the shape of a semicircle were attributed to waves guided around by the cloak shell. Moreover, the observed low-pressure region in the middle position was also because of the wave being guided around the shell and resulting this low-pressure region. The maximum sound pressure peak value in the outer position (\(Cx\) = 150-250 mm, \(Cy\) = 125-150 mm) was the result of the wave being guided and restored back to the outer region of the cloak shell, and the high normalized field values inside the cloak shell area evidenced that the acoustic waves were guided in a pre-determined path around the circumference of the shell. This mechanism changed the direction of the acoustic waves, which resulted in the successful cloaking of an object. The experimental result showed the changing wave direction within the anisotropic cloak shell. Furthermore, in both the object and cloak shell results shown in Figure 26c, as compared with the object case in Figure 26a, the guiding of the cloak shell forming low normalized field values between \(Cx\) = 130-270 mm and \(Cy\) = 0-50 mm, which is the position of the object and the transformed region. The low field value in the transformed region avoid the incoming wave interact directly with the object and reduce the production of strong scattering. The results show that the cloak shell reduced the scattering disturbance and created the cloak effect, thereby proving the effectiveness of the cloak shell design. Figure 26: _Cont._
# A Short-Term Forecasting Method of Inter-Frequency Clock Bias for GPS Block IIF Satellites
## 2 Methods
### Method of IFCB Estimation
Moreover, in order to obtain high-precision IFCB estimates, when performing IF combinations for the carrier phase, it is necessary to select the observed noise scaling minimum combination.
## 4 FTN Detection Scheme in Multipath Channels Based on Neural Network
### Proposed Deep-Bi-LSTM Architecture for FTN Signal Detection
#### 4.2.2 Reasons for Using Bi-LSTM Structure  
In FTN signal detection, it is necessary to consider that the shaping filter is axisymmetric, which means that for the current time k, the interference caused by the symbols at \(k-1\) and \(k+1\) is of the same magnitude. It is obviously not enough to only use the ISI  
Figure 3: Block diagram of using CNN network structure to detect FTN signaling.  
before time k as the interference feature of the current signal. The method to solve this problem is to introduce the Bi-directional LSTM model. As shown in the yellow part of Figure 3, the bidirectional cyclic neural network model includes a cyclic neural network with a front-to-back processing sequence and a cyclic neural network with a back-to-front processing sequence, which are used to transmit the forward state \(\overleftarrow{h_{t}}\) and the backward state \(\overleftarrow{h_{t}}\).  
\[\overrightarrow{h_{t}}[n]=\text{sigmoid}\Big{(}W_{x,\sigma t}^{\prime} \vec{x}[n]+W_{c,\sigma t}^{\prime}\vec{c}[n]+W_{h,c\tau}^{\prime}\overleftarrow{ h}_{t}[n+1]+\vec{b}_{c\tau}^{\prime}\Big{)}\odot\tanh(\vec{c}[n]) \tag{10}\]  
\[\overleftarrow{h_{t}}[n]=\text{sigmoid}\Big{(}W_{x,\sigma t}^{\prime}\vec{x} [n]+W_{c,\sigma t}^{\prime}\vec{c}[n]+W_{h,c\tau}^{\prime}\overleftarrow{h}_{t }[n+1]+\vec{b}_{c\tau}^{\prime}\Big{)}\odot\tanh(\vec{c}[n]) \tag{11}\]  
where \(\vec{x}[n]\) is the model input, \(\vec{c}[n]\) is the information storage unit in the LSTM, and \(W\) and \(\vec{b}\) are the weights and offsets.  
Then, the feature information from the pre-order traversal and the post-order traversal is feature merged. Fusion strategies generally include addition, multiplication, averaging, or simple concatenation, etc. The feature fusion method selected here is the default concatenation, where \(\oplus\) represents the integration operation.  
\[\mathbf{h}_{t}=\left[\overrightarrow{\mathbf{h}}_{t}\oplus\overleftarrow{\mathbf{h}}_{t}\right] \tag{12}\]
# Leveraging IPv6 and ICMPv6 for Delay-Tolerant Networking in Deep Space
## 3 Architecture Proposal
### Custom ICMPv6 Messages
#### 3.2.5 Security Concerns  
While the proposed architecture enables comprehensive monitoring and control, it also raises potential security concerns. IPsec is the native security mechanism in IPv6 networks. One of its operation modes is the \"Transport Mode\", which enables the encryption and integrity protections of the IPv6 payload without involving the IPv6 header. When applied to our design, the usage of IPsec treats the ICMPv6 header and payload as the payload of the IPv6 packet, which means our design can benefit from both authentication and integrity protection. When it comes to the proposed custody transfer mechanism, since the custodian information is carried as part of an extension header, IPsec does not protect it. This leaves it vulnerable to attacks, specifically to tampering by intermediate nodes. There is no easy solution, as modification of the hop-by-hop header by the intermediate DTN nodes is an integral part of the custody transfer mechanism. These required modifications prevent the application of integrity protection to the headers, and the encryption of IPv6 headers is not possible in a non-private network.
# Development of a Multifunctional Unmanned Boat Platform for Aquaculture Automation
###### Abstract  
To reduce labor costs in aquaculture and enhance the level of automated management, this study designed and developed a multifunctional unmanned boat platform (UBP) by integrating technologies such as sensors, satellite positioning, and artificial intelligence. The platform contains three major modules for data collection, underwater vision, and motion control, enabling functions like cruise path planning, water quality monitoring, identification of aquaculture products, and bait feeding. To verify its reliability and practicality, verification experiments were conducted in the aquaculture area of Lianyungang, China. The results show that the UBP can efficiently distribute feed to an area of 10,000 square meters within 20 min based on feeding points, outperforming the 47 min required for manual feeding. Over a two-month period, the weight of sea cucumbers raised by unmanned boats increased by 67.7% compared to those raised manually, with a 24.33% reduction in feed usage. Additionally, the unmanned boat reduced daily aquaculture costs from 225 RMB to 120.2 RMB, a total reduction of 46.7%. In conclusion, this platform reduces labor costs by improving aquaculture efficiency, and addresses limitations of the existing aquaculture feeding machinery in adaptability and real-time responsiveness, which can provide a feasible solution for aquaculture automation.  
unmanned boat; aquaculture; automatic cruising; water quality monitoring; target recognition Article
## 4 Result of the Experiment and Discussion
### Real Data Processing
The reconstructed spectrograms obtained from the single-channel undersampling, processed by the traditional and the proposed methods, and processed by the spectrum \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Targets**} & \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**AASR (dB)**} \\ \cline{3-4}  & & **Left** & **Right** \\ \hline \multirow{3}{*}{Target A} & Single-channel & \(-10.4\) & \(-6.1\) \\  & Traditional method & \(-15.9\) & \(-11.7\) \\ \cline{1-1}  & Proposed method & Lower than Clutter & Lower than Clutter \\ \hline \multirow{3}{*}{Target B} & Single-channel & \(-6.8\) & \(-9.2\) \\  & Traditional method & \(-12.5\) & \(-15.7\) \\ \cline{1-1}  & Proposed method & Lower than Clutter & Lower than Clutter \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The AASR of the targets in Figure 14.  
Figure 14: Azimuth cut of the distributed SAR imaging results. (**a**) Azimuth cut of the single-channel undersampling imaging results for Target A. (**b**) Azimuth cut of the single-channel undersampling imaging results for Target B. (**c**) Azimuth cut of the traditional method imaging results for Target A. (**d**) Azimuth cut of the traditional method imaging results for Target B. (**e**) Azimuth cut of the proposed method imaging results for Target A. (**f**) Azimuth cut of the proposed method imaging results for Target B.  
reconstruction process are compared in Figure 15. The single-channel method generates spectral ambiguity, which leads to a significant elevation in the spectrum edges and severe spectral distortion. When spectrum reconstruction is performed after estimating and compensating the channel error by the traditional method, the spectrum diffusion phenomenon is improved and the energy within the main lobe is enhanced. However, the sidelobes of the spectrum are still high. After using the proposed method to estimate and compensate for both channel and motion errors, the reconstruction spectrum is significantly improved, and the out-of-band residual energy is small. In summary, the effectiveness of the proposed method is verified by processing real data. The proposed method is a significant improvement over the traditional method in terms of spectrum improvement and ambiguity suppression.
###### Abstract  
Satellite communication systems, especially multi-beam low-Earth-orbit (LEO) satellites, could cater to the needs of different industrial applications through flexible resource allocation. Unfortunately, due to the wide coverage of LEO satellites, the data exchange within the LEO satellite networks suffers from the risk of eavesdropping and malicious jamming, which could severely degrade the performance of the industrial production process. To address such challenges, this paper introduces a multi-dimensional resource allocation strategy to facilitate covert communication within the multi-beam LEO satellite network. Our approach ensures the rate requirements of different user equipments while preventing the detection of communication signals by an eavesdropping geostationary orbit (GEO) satellite. Specifically, we formulate an optimization problem that jointly optimizes satellite beam-hopping scheduling, frequency band allocation, and the transmit power of different user equipments, under the covertness constraint. By introducing auxiliary binary variables, we transform this optimization problem into a Mixed-Integer Linear Programming (MILP) problem, which allows us to utilize machine learning-based techniques for efficient solution finding. The simulation results demonstrate the effectiveness of our proposed scheme.  
c 2024 13561. [[https://doi.org/10.3390/electronics13173561](https://doi.org/10.3390/electronics13173561)]([https://doi.org/10.3390/electronics13173561](https://doi.org/10.3390/electronics13173561))
## 1 Introduction
However, MCDM models, which combine computational and mathematical methods to provide a subjective assessment of performance criteria by decision-makers, have emerged as a part of the process study. In addition, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a powerful tool for examining Multi-Criteria Decision-Making (MCDM) problems, is used. TOPSIS has been widely utilized to tackle decision-making difficulties. This strategy is based on comparing all the possibilities in the issue. The TOPSIS approach provides the following advantages: Simplicity, rationality, comprehensibility, high computational efficiency, and the ability to quantify the relative performance of each choice in a simple mathematical form. Consequently, in this investigation, blockchain technology has been employed to improve the privacy and security of the system. Using an edge platform can also improve system efficiency, reduce latency, and improve performance. To select an appropriate node for a specific mission, the proposed fuzzy-based method uses three input parameters: IoT Node's Free Buffer Space (INFBS), IoT Node's Remaining Energy (INRE), and IoT Node's Distance to Event (INDE). Using a hybrid MCDM model, the node selection issue in blockchain-enabled edge-IoT platforms using a fuzzy-based method has been analyzed. This investigation's significant outcomes are listed below:  
* Proposing a secure framework for integrating edge and blockchain technologies into IoT networks to ensure data protection and energy efficiency. * Providing a platform for node selection for various IoT-edge frameworks. * Utilizing an edge platform to increase performance, decrease latency, and increase system efficiency. * Introducing a novel fuzzy-based method using a hybrid MCDM model. * Improving parameters such as INFBS, INRE, and INDE. The rest of this article is organized in the following manner. Section 2 deals with the theoretical background. Section 3 covers the adopted methodology, Section 4 deals with results, and Section 5 discusses the conclusion and future scope.
# Stressing State Analysis of CFST Arch Supports in Deep Roadway Based on NSF Method
## 4 Expanded Experimental Data Through the NSF Method
### Strain and Stress Fields Expanded by the NSF Method  
For purpose of further studying the working behavior of the CFST arch supports, the measured strains should be expanded to obtain strain and stress fields of each cross section through applying the NSF method. At first, Using the finite element software ANSYS, the cross section models of the supports are constructed and Shell 181 and Beam 188 are respectively chosen as the element types of the core concrete and steel tube. After dividing mesh, the strain of each element node on each cross section can be calculated by numerical shape function based on the 19 measured strains, correspondingly, the expanded stress data could be achieved through the constitutive relation of the materials. When the CFST arch supports enters the elastic-plastic deformation stage, the mechanical properties are very complex and nonlinear, which must be considered in the calculation of stress data. So, the five-stage stress-strain model for steel tube and the plastic damage model for core concrete are adopted to calculate the stress fields [38; 39; 40]. Hence the expanded strain and stress fields are obtained, which is established for structural stressing state analysis in depth.  
The experimental data itself contain the influence of elastic-plastic deformations, so the elastic-plastic behavior of the supports could be well embodied in the interpolated strain field based on the experimental data. The expanded stressing fields are also calculated by the appropriate constitutive relation which has considered the nonlinear of materials. Therefore, the NSF method could be utilized to deeply analyze the working behavior of the supports under linear and nonlinear state. Besides, the more experimental data are measured, the more accurate interpolation results would be obtained.
## 3 Experimental Results
### PDT Results
#### 3.2.4 Cumulated Hysteresis Dissipated Energy  
The cumulated hysteresis dissipated energy of the specimen is calculated by the formula as follows:  
\[E_{\text{h}}=\sum_{0}^{n}\frac{1}{2}(F_{i+1}+F_{i})(X_{i+1}-X_{i}) \tag{9}\]  
where \(F_{i+1}\) and \(F_{i}\) are the restoring force of the \(i\)+1 th and \(i\)-th time point, respectively; \(X_{i+1}\) and \(X_{i}\) are the corresponding displacements, respectively.  
To reflect the energy dissipating degree of the specimen with the increase of PGA, the relative dissipated energy ratio is defined as the ratio between the cumulated hysteresis dissipated energy of the specimen under different loading cases of the PDTs calculated by Equation (9), equivalently the corresponding PGA is 0.05, 0.10, 0.20, 0.30, 0.40, 0.60, and 0.80 g, and the cumulated hysteresis dissipated energy of the specimen under the final loading case of PDTs (PGA = 0.8 g). Figure 13 shows the cumulated hysteresis dissipated energy (\(E_{\text{h}}\)) and the relative dissipated energy ratio of the specimen under different loading cases. It can be observed that the cumulated hysteresis dissipated  
Figure 12: Deterioration behavior of the specimen under PDTs: (**a**) relative stiffness ratio and stiffness versus peak ground acceleration (PGA); (**b**) fundamental period versus PGA; (**c**) maximum roof drift ratio versus PGA and (**d**) maximum base shear force versus PGA.  
energy \(E_{\rm h}\) gradually increased along with the increase of PGA. And the growth rate of the cumulated hysteresis dissipated energy \(E_{\rm h}\) increased significantly when PGA came up to 0.40 g, due to the more deterioration in stiffness and the larger plastic deformation. Before the PGA came up to 0.40 g, the relative dissipated energy ratio was only 19%.
## 3 Results and Discussion
### Porous Carbons Characterization
#### 3.1.6 TPD Analysis  
The amounts of CO and CO2 evolved in the TPD experiments were also measured to understand the nature of the carbon surface groups. CO2 evolution results from the decomposition of carboxylic acids at T \(<\) 450 \({}^{\circ}\)C and lactones at higher temperatures, 550 \(<\) T \(<\) 800 \({}^{\circ}\)C; carboxylic anhydrides originate in both CO and CO2 at intermediate temperatures, 400 \(<\) T \(<\) 600 \({}^{\circ}\)C; phenols decompose into CO at intermediate temperatures, 550 \(<\) T \(<\) 800 \({}^{\circ}\)C; and carbonyl-quinone groups release CO at high temperatures, 750 \(<\) T \(<\) 1100 \({}^{\circ}\)C [40]. Figure S5 (Supplementary Materials) displays the TPD spectra for both carbons. Table 5 presents the results of the deconvolution of TPD spectra using a multiple Gaussian function, as well as the amounts of CO and CO2 released obtained by integration of the areas under the TPD peaks.  
Overall, ACK carbon presented higher amounts of CO and CO2 released, confirming the results of the elemental and XPS analyses that indicated the presence of more oxygen functional groups at this carbon's surface. Additionally, the amount of CO released was higher than that of CO2 for both samples, indicating that the oxygen groups present in the carbons were mainly nonacidic (carbonyl and quinone groups), in agreement with the pHPZC of these carbons (Table 3). Effectively, as indicated by the TPD results, the main surface functional groups in both carbons are phenols and carbonyls/quinones.
# DRAM Retention Behavior with Accelerated Aging in Commercial Chips
## 3 Testing Methodology
### True and Anti-Cell Detection  
After collecting and analyzing the new DRAM modules' retention errors for different patterns and retention times, the errors are attributed to process and architectural variation. From the retention test results, the phenomenon of design-induced variation is observed in two forms: (i) true cells and anti-cells due to architectural variation of the cells and (ii) variation across local parts of the bank due to relative distance from the peripherals [27]. True cells induce error only for logic '1' as the cell capacitor discharges from HIGH to LOW  
Figure 3: Overview of the testing methodology of a DRAM module.  
state, and they do not induce any error for logic '0' as the capacitor remains at the LOW state. On the other hand, anti-cells discharge for logic value '0' and induce error and show no error for logic value '1'.  
In order to identify the true cells and anti-cells, the follwing steps are required:  
1. The retention test is performed separately with all-zeros (0x00) and all-ones (0 xFF) patterns in a module.
2. The retention errors in bank are aggregated for each row.
3. The rows that induce error only for the 0 xFF pattern and show no error for the 0x00 pattern are identified as true cells. The rows inducing error only for the 0x00 pattern are identified as anti-cells. The rows that show errors for both patterns have a mixture of true and anti-cell configuration.  
Identifying true cells and anti-cells is important for the aging process. In the aging process, our goal is ensure maximum stress to the storage capacitors. In order to achieve that, data pattern 0 xFF is written to the true cells and pattern 0x00 is written to the anti-cells to ensure uniform aging across the DRAM array. In the modules with a mixture of both true cells and anti-cells, the 0 xFF pattern is written to all the rows during the aging process.