esa-sceva/satcom-chunk-collection huggingface dataset:
# Application and Performance Evaluation of Resource Pool Architecture in Satellite Edge Computing
## 3 Proposed Architecture
### Satellite Node Selection
#### 3.3.3 Load Balancing Rate of Resource Nodes  
One benefit of the resource pool is that it can avoid overusing resources by some domestic satellites while other overseas satellites are idle, that is, to achieve resource load balance. Load balance is evaluated by resource utilization. The resource utilization of a certain kind of satellite refers to the time ratio of resource \(m\) on satellite \(j\) during the completion time of a task, which is defined as follows:  
\[\
u_{m}=\frac{\sum\limits_{j}tend_{j}-tstart_{j}}{T_{j}} \tag{11}\]  
In (11), \(tend_{j}\) is the time when task \(j\) finishes executing on resource \(m\), \(tstart_{j}\) is the time when task \(j\) starts executing on resource \(m\), and \(T_{j}\) is the time difference between the time when task \(j\) finally finishes the task and the time when task \(j\) first starts executing. The calculation is as follows:  
\[T_{j}=\max\limits_{0\leq j\leq n}\{tend_{j}\}-\min\limits_{0\leq j\leq n}\{ tstart_{j}\} \tag{12}\]  
There are various resources on satellites, including computing, storage, network, sensor, and other resources. The average utilization rate of \(m\) resources is:  
\[\
u=\frac{\sum\limits_{i}^{m}\
u_{i}}{m} \tag{13}\]  
In this paper, the load balance degree is used to evaluate the load between satellites. It means that the same resources should be allocated to each satellite as much as possible, rather than being used too intensively on one satellite [26]. Resource load balance can ensure the efficient execution of tasks, improve the utilization of resources, and avoid excessive consumption of a single node, thus prolonging the service life of satellites. The measurement method of resource load balance is:  
\[d=\sqrt{\frac{\sum\limits_{i}^{m}{(v-v_{i})}^{2}}{m}} \tag{14}\]  
The closer \(d\) is to zero, the smaller the difference in the operation of each resource, and the better the resource load balance.
# Lessons Learned on Geosynthetics Applications in Road Structures in Silesia Mining Region in Poland
## 3 Results and Discussion on the Research Outputs
Despite the 10-year period of use and additional load, caused by mining deformations, the calculated deflections were lower than the permissible values, assumed for the highest class of the technical condition. However, it should be noted that in the area from \(323+800\) km to \(323+400\) km, larger deflections were observed, especially on the southern section, which is closer to the so-called zone of the main impacts of mining operations. Such observations could also be found in researches conducted worldwide [36; 37; 38]. The major difference between the different studies is that the mining influenced area studied in the present paper was much more complex and specific for this particular region. Despite the difficult working conditions, the material allowed for the significant limitation of the subsidence effect. Further analysis of the technical condition of the pavement was based on FWD results, by performing inverse calculations to determine the elastic modules for surface layers, using such parameters as loading magnitude, circular loading plain radius, and deformations measured using a geophone located within the loading axis. The results of calculations for each layer is presented in Figure 13.  
Figure 12: Measured (using Falling Weight Deflectometer) and permissible deflections of the motorway pavement loaded with KR7 traffic, after 10 years of exploitation. Figure 13: Calculation of moduli for: (**a**) Mineral asphalt mix, (**b**) aggregate layers, and (**c**) subgrade. Detailed analysis of the values for the calculated pavement elastic modules indicated that the pavement and the subsoil were of high rigidity, which is in agreement with the assumptions made by [PERSON] et al. [39] and [PERSON], et al. [40]. At the same time, in the section of increased deflections (i.e., from 323 + 800 km to 324 + 00 km--southern section), the value of modules was reduced. Whereas, the minimum modules did not exceed the minimum value, taking into account the loosening of the substrate with the mining deformations (Figure 13c). The embankment sections, additionally, had a layer of aggregate stabilized by two layers of monolithic geogrids, in the form of geomattresses. The same solution was also included within the dipper layers of the pavement structure. The durability of the applied solution was proved by the values obtained from calculations on the basis of FWD field measurements [18]. To estimate the reduction of the module due to the loosening effect, a value of 1.5 mm/m was used. This value is in line with Polish Mining Area Category I.
## 3 Results
### Stability Test of MNDVI Estimation for \(f\)PAR
#### 3.3.4 Accuracy Assessment at Satellite Scale  
Ground experiments have shown that MNDVI has a stable ability to retrieve _f_PAR, with minimal influence from variations in light conditions, crop varieties, and growth differences, enabling its application in large-scale _f_PAR retrieval. However, further experiments are needed to verify its accuracy at the satellite scale. Therefore, in this study, three cloud-free 10 m Sentinel-2 images of the SZ during the critical growth stages of winter wheat were selected for accuracy validation. The validation area was chosen as a 1000 \(\times\) 1000 pixel (10 \(\times\) 10 km) region, with non-winter wheat areas masked out. All winter wheat pixels in this region were used for validation. The _f_PAR calculated using the Biophysical Processor tool in SNAP 9.0.0 software was used as the ground truth data to verify the reliability of the data obtained from Equation (3). Figure 6 shows the results of the comparison between the actual _f_PAR and the estimated _f_PAR during the critical phenological stages. The coefficients of determination for the three stages were 0.718, 0.550, and 0.578, with the lower coefficient in the later stage possibly due to the higher _f_PAR values. However, overall, the estimated values were lower than the actual values, indicating a certain underestimation phenomenon in this method, which may be caused by vegetation index saturation in the later stages.  
Figure 5: Validation of estimation accuracy of index model based on MNDVI in different irrigation schemes.  
In this study, we compare the ability of satellite-scale physical models and the empirical model obtained in this research to retrieve winter wheat \(f\)PAR in the southern and eastern parts of the North China Plain, specifically in ZK and Huaibei HB. The \(f\)PAR values of the two models were calculated based on Sentinel-2 images during the critical phenological period of winter wheat, and the results are shown in Figures 7 and 8. In ZK, the determination coefficients of the \(f\)PAR values estimated by the two models were 0.9003, 0.7818, and 0.6534, while in Huaibei, they were 0.6929, 0.7183, and 0.8312. It can be seen that even at the satellite scale, the \(f\)PAR estimated based on MNDVI and the physical model still have a good correlation in different regions and under different planting conditions, further demonstrating the universality of estimating \(f\)PAR based on MNDVI.
## 3 Protection Coordination Constraints
### Perfect Knowledge Protection  
The simplest approach for studying the behavior of microgrid protection is to assume perfect protection operation, as illustrated in Figure 8. In this case, there is a set of \(\mathcal{Z}\) protective zones. Each zone \(\zeta\in\mathcal{Z}\) is associated with a set of \(\mathcal{P}_{\zeta}\) protective devices. Each zone protects \(\mathcal{B}_{\zeta}\) nodes, while each protective device \((i,j,k)\) is associated with a branch terminal. For a fault located in the set \(\mathcal{B}_{\zeta}\), the branches associated with that set of protective devices become inoperable.  
The impact of protective device operation in terms of load lost can then be expressed as a restoration minimum-load shed problem, where branches associated with a tripped state have indicator variable \(\delta_{ijkr}=0\) and those not tripped have \(\delta_{ijkr}=1\).  
Figure 7: Graphical illustration of constraints for a grid-forming inverter.  
The implemented three-phase power flow [64] is established based on current (\(I\)), voltage (\(V\)), admittance (\(Y\)), and power (\(S\)). These quantities are defined on a per phase basis, \(\Phi=\{a,b,c\}\), which is linked through voltage and admittance. The full list of notation specific to three-phase power flow are provided in Appendices A and D.  
In an unbalanced three-phase configuration, the power flow on a line \((i,j,k)\) and phase \(\phi\) is affected by the voltages on all three phases according to the conductance and susceptance values appearing in the admittance matrix \(Y^{\Phi}_{ijk}\). This relationship is expressed as follows:  
\[\forall(i,j,k)\in\mathcal{E}: \tag{45a}\] \[I^{\Phi}_{ijkr}=\delta_{ijkr}Y^{\Phi}_{ijk}(V^{\Phi}_{ir}-V^{\Phi}_{jr})\] (45b) \[\sum_{g\in\mathcal{G}_{i}}\delta_{gT}S^{\Phi}_{gT}-\delta_{ir}S^{\Phi}_{ir}= \sum_{(i,j,k)\in\mathcal{E}^{+}_{i}}S^{\Phi}_{ijkr}-\sum_{(j,i,k)\in\mathcal{E }^{-}_{i}}S^{\Phi}_{jikr}\]  
where the first equation can be expanded to:  
\[\begin{bmatrix}I^{a}_{ijkr}\\ I^{b}_{ijkr}\\ I^{c}_{ijkr}\end{bmatrix}=\delta_{ijkr}\begin{bmatrix}\mathbf{Y}^{aa}_{ijk}& \mathbf{Y}^{ab}_{ijk}&\mathbf{Y}^{ac}_{ijk}\\ \mathbf{Y}^{ab}_{ijk}&\mathbf{Y}^{bb}_{ijk}&\mathbf{Y}^{bc}_{ijk}\\ \mathbf{Y}^{ac}_{ijk}&\mathbf{Y}^{bc}_{ijk}&\mathbf{Y}^{cc}_{ijk}\end{bmatrix} \cdot\begin{bmatrix}(V^{a}_{ir}-V^{a}_{jr})\\ (V^{b}_{ir}-V^{b}_{jr})\\ (V^{c}_{ir}-V^{c}_{jr}).\end{bmatrix} \tag{46}\]  
The above equations lead to the following representation:  
\[\forall(i,j,k)\in\mathcal{E},\ \forall\phi\in\Phi_{ijk}: \tag{47a}\] \[I^{\phi}_{ijkr}=\sum_{\phi\in\Phi_{ijk}}\delta_{ijkr}\mathbf{Y}^{\phi\phi}_{ ijk}\left(V^{\phi}_{ir}-V^{\phi}_{jr}\right)^{*}, \tag{47b}\]  
where \(\Phi_{ijk}\) denotes the phases supported by line \((i,j,k)\).  
In case of perfect knowledge protection, the objective of the problem formulation is to maximize the load supplied, subjected to constraints (45a)-(47b) and bounds on current, voltage and power:  
\[\max\quad\sum_{i\in\mathcal{B}}\sum_{\phi\in\Phi_{i}}\delta_{ir}P^{\phi}_{ir}. \tag{48}\]
# A Novel CDMA-Based Secret Message Multiplexing Scheme by Image Data Hiding +
## 1 Introduction
This spreading code serves as the secret key to extract secret data. The cross-correlation between any two different secret keys is equal to zero. Due to this orthogonal property, only the authorized recipient can decode the respective hidden secret message using a specific secret key. Therefore, data security is well ensured. At the same time, with the help of spreading codes, a secret data bit is spread to several bits. These characteristics make the proposed scheme robust against some kinds of noise attacks such as Gaussian noise attack and salt-and-pepper noise attack. The novelty and contributions of this work mainly include the following aspects. (1) We introduce a novel CDMA-based secret message multiplexing scheme applicable to digital images, in which multiple secret messages for different participants are embedded into a single image by CDMA multiplexing and modulo operation. (2) The theoretical PSNR of the proposed scheme is analysed and compared with experimental results. It is verified that the PSNR performance is independent of the characteristics of the cover image. (3) We apply the Gaussian noise attack, salt-and-pepper noise attack, and cropping attacks to the marked images. The bit-error rate (BER) and logotype secret images are utilized to evaluate the robustness of the proposed scheme. We also provide theoretical BER for the Gaussian noise attack. The rest of this paper is organized as follows. The technology of CDMA multiplexing is introduced in Section 2. The proposed CDMA-based secret multiplexing scheme is presented in Section 3. Section 4 demonstrates the experimental results. Finally, conclusions are made in Section 5.
###### Abstract  
Although the use of polyethylene (PE) pipelines has become increasingly widespread in recent years, few studies have addressed their seismic design and ability to withstand blast waves. In order to establish their seismic capacity, the dynamic response of buried pipelines subjected to blast waves must be explored in depth. Here, we studied the dynamic response of PE pipes situated near an explosive source. Time histories of dynamic strains were measured by conventional strain gauges after simple waterproof treatment, and pipe and ground vibration velocity curves were obtained. Based on the experimental data, the attenuation law of the peak strains under the conditions of different charge masses and blast center distances was analyzed, and the spectrum characteristics of strain, velocity of the pipe, and ground velocity were studied. The results revealed that a large hoop strain on the PE pipes was produced due to the local impact near the explosive source. We found that peak hoop strain (PHS) or peak axial strain (PAS) had a power attenuation relationship with the scaled distance, and this relationship could also be derived by dimensional analysis. The average frequency of strains had the same attenuation form as the charge mass, which was between 10 Hz and 50 Hz. Additionally, the vibration of the pipe showed a low frequency. We also determined that the attenuation of the average frequency of pipe and ground vibration velocity was closely related to the charge mass and the scaled distance. Pipe peak vibration velocity (PPVV), ground peak particle velocity (GPPV), and the peak dynamic strain of pipe were highly positively correlated, which verifies the feasibility of using GPPV to characterize pipeline vibration and strain level. Thus, a blasting criterion of 10% minimum request strength (MRS) for PE pipe was proposed, which means that the additional PHS or PAS of the dangerous point must be less than 10% MRS, and we also propose limiting the safety distance-charge mass for blasts near buried PE pipelines by the criterion. Some results in this paper can serve as the basis for future in-depth theoretical research.  
blast wave; PE pipe; dimensional analysis; dynamic strain; vibration velocity; spectrum analysis; average frequency; blasting criteria +
Footnote †: journal: Applied Physics Letters
## 3 Application and Illustration of the Proposed CRITIC-GRA Model
### Stability Index of Open Stoping
#### 3.2.3 Stope Stability Analysis with CRITIC-GRA Evaluation Model
Therefore, when the stope length was larger than 50 m, it was wise to select a stope with a width of 15 m to ensure the integrity of the side walls. The value of stope height has the greatest effect on the wall stability, followed by stope length and width. It can be seen from Figure 5(b) that the values of grey correlation degree representing the stability conditions of the stope roof decreased with the increase in stope length. For both single stage and double stage open slopes, the roof stability with a slope width of 15 m was much greater than that of the 20 m width. Specifically, for the slopes with a width of 15 m or 20 m, the roof stability of the single stage stope was a little higher than the double stage stope. Stope width has the greatest influence on the roof stability, followed by the stope length and height. Therefore, it is the key consideration to optimize the stope width to achieve satisfactory roof stability. Figure 5(c) gives the overall stability around the stope void quantitatively represented by the grey correlation degree, which meant all the safety indexes for stope roof and side walls computed by the Mathews empirical graph method and the numerical simulation method were wholly input into the CRITIC-GRA model. It is interesting to note that when the stope length varies from 40 m to 48 m, the overall stability of the single stage stope with a 20 m width is superior to that of the double stage stope with a 15 m width. However, when the stope length falls in the range of 48 m to 60 m, the double stage stope with the 15 m width has the whip hand in stability. Therefore, when the stope length exceeds 48 m with the given rockmass quality, the double stage open stope with the 15 m width prevails over the single stage open stope with the 20 m width. It can be further obtained from Figure 6 that the overall stability of the open stope can be improved instead of reduced to enlarge the single stage stope height 60 m to a double stage height of 120 m by slightly reducing the stope width from 20 m to 15 m. This will dramatically increase the blasted ore volume with long-hole mass mining and obtain a desirable overall stability in a stope with a width of 15 m and a height of 120 m compared to a stope with a width of 20 m and a height of 60 m.
## 4 Remote Sensing Platforms Used in Peatland Research  
### Satellite-Based Remote Sensing  
The literature reveals that satellite-based remote sensing techniques have been widely used for peatland research. It has emerged as a valuable tool for monitoring and studying peatland ecosystems [58]. In recent years, the application of satellite data has emerged as a promising approach to assessing the condition of peatlands [11; 59; 60; 61], estimating carbon fluxes [37; 62; 63], and monitoring the effectiveness of restoration initiatives [64]. In this section, we evaluate the effectiveness and limitations of satellite-based remote sensing applications in peatland research (Table 3), focusing on their contribution to peatland mapping, vegetation monitoring, and carbon stock estimation.  
Figure 3: Number of articles distributed by type of platform and country of study; (a) the code of the country of study based on ISO 3166-1 alpha-3 codes (MIX stands for several countries in one study), where the size is controlled by the number of articles in each category, the distribution of articles per remote sensing data source (satellite, airborne, UAV, in situ and mixed plus active, passive, or Act+Pas, which stands for mixed or sensor fusion), and the year of study (b).
# A Flexible FPGA-Based Stochastic Decoder for 5G LDPC Codes
## 4 Proposed Architecture
### Control Unit  
The control unit of the decoder manages internal and external control signals, including commands to stop or proceed with the iterative decoding. A new group of \(\eta_{v}\times w\)-bit intrinsic probabilities may need to be loaded into the IMMU, and this can be indicated by the LOAD signal. The decoder must decode a new frame, and all modules must be reset to their default conditions using the RESET signal. The ability of the proposed architecture to swap the current BGM to decode the message contained in the IMMU during a single clock cycle is one of its essential characteristics. The supporting BGMs are parameterised in ROMs at compile-time to enable this flexibility level. The process of decoding is started and maintained by the START signal. As long as this signal is active, the decoder keeps doing iterative decoding; the decoding operation is stopped once if the START signal is absent.
# Blockchain-Based Multiple Authorities Attribute-Based Encryption for EHR Access Control Scheme
## 4 Scheme Model
### Syntax  
This VO-PH-MAABE scheme includes the following nine algorithms.  
\(GlobalSetup(\mathbf{1^{3}})\to GP\): CA executes the global setup algorithm. This algorithm takes as input a security parameter \(\lambda\) and outputs the system public parameter \(GP\).  
\(A\)\(A\)\(A\)\(Setup(GP)\to(pk_{i},sk_{i})\): Different AAs execute the attribute authority setup algorithm using the Shamir secret sharing scheme. This algorithm takes as input the public parameters \(GP\), then outputs the public and private keys\((pk_{i},sk_{i})\) for each \(AA_{i}\).  
\(PA\)\(K\)\(Gen(pk_{i})\to PAK\): The on-chain contract executes the public attribute key generation algorithm. This algorithm takes as input the public key \(pk_{i}\) from \(AA_{i}\), then outputs the public attribute key \(PA\).  
\(D\)\(U\)\(Setup(GP)\to(upk,usk)\): DU executes the data user setup algorithm. This algorithm takes as input the public parameter \(GP\), then outputs public and private keys\((upk,usk)\) for DU.  
\(U\)\(A\)\(K\)\(Gen(GP,S_{uid})\to U\)\(IA\): AAs and the on-chain contract jointly execute the user attribute key generation algorithm. This algorithm takes as input the public parameter \(GP\) and an attribute set \(S_{uid}\) for the data user with identity \(uid\), then outputs the user attribute key.  
\(U\)\(A\)\(K\)\(Gen(GP,S_{uid})\to U\)\(IA\): The contract jointly executes the user attribute confused key generation algorithm. This algorithm takes as input the public parameter \(GP\) and an attribute set \(S_{uid}\) for the data user with identity \(uid\), then outputs the user attribute confused key.  
\(Encryption(KEY,M_{data},AP,GP,PAK)\to CT\): DO executes the encryption algorithm. This algorithm takes as input the symmetric key \(KEY\), plaintext \(M_{data}\), access policy \(AP\), public parameters \(GP\), and public attribute key \(PAK\), then outputs ciphertext \(CT\).  
\(OutDecryption(CT,SK_{O},AP)\to CT^{\prime}\): CSP executes the outsourcing decryption algorithm. This algorithm takes as input the ciphertext \(CT\), access policy \(AP\), and outsourcing decryption key \(SK_{O}\), then outputs the outsourcing decryption ciphertext \(CT^{\prime}\). \(SK_{O}\) is the user attribute key \(U\)\(IA\) that satisfies the access policy \(AP\).  
Figure 1: The VO-PH-MAABE system model.  
\(\mathbf{FullDecryption}(\mathbf{CT^{\prime}},\mathbf{usk})\to M_{data}\): DU executes the full decryption algorithm. This algorithm takes as input the outsourcing decryption ciphertext \(CT^{\prime}\) and user private key \(\mathbf{usk}\), then outputs the plaintext \(M_{data}\).
## 4 Results and Discussion
### Classification Results  
We have used two primary sources of datasets ([PERSON] [36] and [PERSON] [41]) and one dataset generated from the DCGAN process, as explained in the previous subsection. During the image dataset generation process utilizing the DCGAN, the GPU's limitations introduce restrictions, resulting in the production of only 300 chest X-ray pictures in a single compilation. Subsequently, following the repetition of the process for a total of ten iterations, we successfully amassed a dataset of 3000 chest X-ray pictures that were generated. The generated data will be integrated with actual images from primary sources for the purpose of training. According to the analysis of generated images, it has been determined that a portion of the DCGAN data accounts for around 39% of the entire dataset. The generated dataset has 1000 artificially created photos, encompassing each instance of normal lung conditions, pneumonia, and COVID-19 cases.  
We have trained our proposed model with 7600 datasets with three different classes: normal, pneumonia, and COVID-19. Later, we carried out a test for 89 images of normal cases, 67 images for pneumonia, and 45 images for COVID-19. We trained our model using Inception Learning V3 with 100 epochs and a batch size of 128. The result shows that the proposed model's best training and validation accuracy are 98.86% and 98.49, respectively. The validation loss is 0.015. Refer to the graph depicted in Figure 8. The graph exhibits fluctuations during the initial stages of training, which can be attributed to the limited availability of data. However, to address this issue and ensure stability during the training process, additional data were subsequently introduced. The occurrence of this volatility is also prevalent in the majority of TensorFlow training processes.  
The prediction result is fascinating and can classify the tested image accordingly compared to the original dataset. Figure 9 depicts the impact of the prediction of our proposed system. Figure 9A,B,D illustrate the prediction result where the original image is pneumonia, and it is correctly predicted as pneumonia. In contrast, Figure 9C was originally normal and is indicated as a normal case.  
Figure 7: Generator and discriminator loss during DCGAN training.  
Figure 8: Training and validation accuracy.  
Figure 9: Prediction result samples.