esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methods (ASMAG Algorithm)  
The general workflow of ASMAG is presented in Figure 2. The processing relies on three input datasets: (a) Glacier outlines, (b) a DEM, and (c) time series of multi-temporal Landsat satellite scenes. In an outer loop, the algorithm accesses all Landsat folders, reads metadata from each scene, determines cloud cover, converts digital numbers into top of atmosphere reflectance (TOAR) and performs a topographic correction. An inner loop cycles through all glaciers, assesses the cloud coverage per glacier and, if cloud cover is less than 10%, the SC and the SLA is calculated. ASMAG has been tailored to handle each Landsat sensor differently, as metadata can be located at different positions in the file, calibration values for calculating radiance and reflectance differ, the radiometric resolution changes, and the number of bands varies. ASMAG is written in IDL (Exelis Visual Information Solution) and various tools (layer stacking, resampling, topographic modeling including slope, aspect and shaded relief) from the ENVI remote sensing software package are integrated.
# FAIDM for Medical Privacy Protection in 5G Telemedicine Systems
## 3 Proposed Scheme
### Anonymous Signature and Verification Phase  
Gateway node \(GW_{t}\) (verifier) receives and verifies message with signature generated by anonymous private key \(aS_{ij}\) using verification function. Figure 7. illustrates process of anonymous signature and verification phase. Detailed descriptions are stated as follows:  
Step 1: Constrained node \(CN_{ij}\) chooses a random number \(R_{ij}\in Z_{q}^{*}\), computes (\(W_{ij}\), \(V_{ij}\), \(t_{ij}\)) for further computation.  
\[W_{ij} =T_{R_{ij}}(x)\bmod p \tag{33}\] \[V_{ij} =H_{1}(W_{ij}||aID_{ij}||aS_{ij})\] (34) \[t_{ij} =R_{ij}V_{ij}\bmod p \tag{35}\]  
Step 2: Constrained node \(CN_{ij}\) chooses a random number \(L_{ij}\in Z_{p}^{*}\) so that \(L_{ij,g}\) is the \(g\)th bit of \(L_{ij}\). Then, constrained node \(CN_{ij}\) computes (\(O_{ij}\), \(b_{ij}\)) to obtain \(Y_{ij}\) and \(\eta_{ij}\), generates signature with signature \(\sigma_{ij}\), and sends \(\sigma_{ij}\) to gateway node \(GW_{t}\).  
\[O_{ij}=\prod\
olimits_{p=1}^{g_{1}}Q^{\prime}_{g-1} \tag{36}\] \[b_{ij} =H_{1}(Q,\ W_{ij},\ M)\] (37) \[\Upsilon_{ij} =L_{ij}b_{ij}t_{ij}\bmod p\] (38) \[\eta_{ij} =T_{\Upsilon_{ij}}(x)\bmod p\] (39) \[\sigma_{ij} =(Q,\ W_{ij},\ Y_{ij}) \tag{40}\]  
Step 3: Upon receiving signature \(\sigma_{ij}\), gateway node \(GW_{t}\) verifies signature \(\sigma_{ij}\). If \(\eta^{\prime}_{ij}\) holds, signature is accepted.  
\[\eta^{\prime}_{ij} =O_{ij}T_{b_{ij}}(W_{ij})T_{b_{ij}V_{ij}}(aS_{ij})\bmod p \tag{41}\]  
Figure 7: Anonymous signature and verification phase of proposed scheme.
# Target Detection for Coloring and Ripening Potted Dwarf Apple Fruits Based on Improved YOLOv7-RSEs
## 2 Materials and Methods
### YOLOv7 Principle and Structure  
The YOLOv7 model has three main parts: the backbone feature extraction network, the neck network, and the detection head network [19], as depicted in Figure 4. The backbone processes the input image with 640 pixels \(\times\) 640 pixels and extracts valuable information via feature extraction. This process mainly depends on the collaboration of the Conv+BN+Silu (CBS) module, the Efficient Aggregation Network (ELAN) module, the MaxPool (MP) module, and the SPPCSPC module [20].  
Figure 4: Network architecture of YOLOv7.  
Figure 3: Example of image enhancement. (**a**) Random contrast; (**b**) artwork; (**c**) random brightness; (**d**) random rotation; (**e**) random noise; (**f**) random flipping.  
In the backbone, the Multi-Concat-Block and Transition-Block play pivotal roles. The Multi-Concat-Block comprises four branches, each conducting a varying number of normalized convolution operations on the input feature layer. The results from these four branches are combined and then processed through another normalization convolution operation, producing a final output feature layer of fixed size. This design boosts network depth to enhance accuracy while tackling gradient vanishing issues with its multi-branch stacking and skip connection structure.  
The neck network, on the other hand, uses the PANet network structure [21; 22]. This structure combines top-down and bottom-up information flows through a two-way fusion strategy. The neck consolidates information from different backbone and detection layers, enabling multi-scale fusion and extracting 3 enhanced feature layers.  
Finally, the detection head network carefully examines feature points in each layer to determine the exact location, confidence level, and category of the target object.  
YOLOv7 builds a feature pyramid structure like YOLOv5 on the three key feature layers of the backbone network to boost feature extraction. This architecture utilizes Multi-Concat-Block and Transition-Block modules to combine feature layers of varying scales through up-sampling and down-sampling operations, enhancing the extraction of high-quality feature layers for target detection. It is worth noting that YOLOv7 utilizes the unique SPPCSPC module in the early stage to perform deep feature extraction for feature layers of size (20,20,1024), aiming at expanding the sensory field of the network layer. The SPPCSPC module combines the CSP and SPP modules. It divides the feature layer into two parts: one passes through the module after standardized convolution, while the other undergoes normalized convolution. Additionally, the second part is pooled using the SPP module with four different kernel sizes to integrate features into a fixed-size output layer. This not only helps to improve the sensory field and accuracy of the network layer, but also effectively circumvents the repeated extraction of feature information, reduces the computational complexity, and thus improves the computational speed.
# Safety, Security and Privacy in Machine Learning Based Internet of Things
## 4 Machine Learning for IoT Security
### Emerging Techniques for IoT Safety and Security
#### 4.5.1 Federated Learning (FL)  
Federated machine learning is a decentralised collaborative learning technique and is used to gain more experience from large datasets at different locations. The main property of federated machine learning is to secure the data that are collected through different mediums. In contrast with other approaches, FL allows for the model to transfer, without removing data from their origin [71]. This feature makes FL a good choice for IoT systems in terms of privacy and security. Federated learning overcomes the massively distributed  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multicolumn{6}{c}{**Supervised Learning**} & \multicolumn{6}{c}{**Unsupervised ML**} & \multirow{2}{*}{**Threat Detected**} \\ \cline{2-2} \cline{4-11}  & **NB** & **SVM** & **DT** & **KNN** & **RF** & **AR** & **EL** & **K-Means** & **PCA** & \\ \hline
[60] & - & \(\surd\) & - & \(\surd\) & - & - & \(\surd\) & - & - & False detection attack \\
[62] & - & \(\surd\) & - & - & - & - & - & - & - & Intrusion detection \\
[63] & - & - & - & - & \(\surd\) & - & - & - & - & Authorization \\
[64] & - & - & - & - & - & - & - & - & \(\surd\) & Intrusion detection \\
[65] & - & \(\surd\) & - & - & - & - & - & - & - & Authentication \\
[66] & - & - & - & - & - & - & \(\surd\) & - & - & Authorization \\
[67] & - & - & - & \(\surd\) & - & - & - & - & - & Immpersonation attack \\
[68] & - & \(\surd\) & - & - & - & - & - & \(\surd\) & - & Data tempering \\
[69] & - & - & - & - & - & - & - & - & \(\surd\) & Intrusion detection \\
[70] & - & - & - & - & - & - & - & - & Intrusion detection \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of ML Algorithms for IoT security.  
and private datasets, which create challenges in ML, by enabling on-device ML, without the migration of private end-user data to any central cloud.
# Numerical Simulation of Seepage Surface and Analysis of
## 2 Materials and Methods
### Tailings Dam Model
#### 2.4.2 The Model of Three-Dimensional Seepage Simulation and Stability Analysis  
The model for three-dimensional seepage numerical simulation and stability analysis was completed based on the tailings dam at a dam height of 210 m. The model was uniformly divided into tetrahedral meshes and encrypted in key areas, and finally divided into 6,036,684 nodes and 1,509,171 mesh cells, with the grid model of the tailings dam shown in Figure 4.  
\begin{table}
\begin{tabular}{c c} \hline
**Tailings Particle Size Range/mm** & **Mass Content/\%** \\ \hline \(\geq\)0.074 & 29.4 \\ \(\geq\)0.037\(\sim\)\(<\)0.074 & 27.2 \\ \(\geq\)0.019\(\sim\)\(<\)0.037 & 5.4 \\ \(\geq\)0.005\(\sim\)\(<\)0.019 & 22.5 \\ \(<\)0.005 & 15.5 \\ \hline \end{tabular}
\end{table}
Table 1: Results of particle analysis of tailings samples.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Materials** & **Fine Sand Tailings** & **Silty Sand Tailings** & **Silt Tailings** & **Silty Clay Tailings** \\ \hline Density (KN/m\({}^{3}\)) & 17.9 & 19.4 & 20.1 & 19.9 \\ Particles with particle size larger & & & & \\ than 0.074 mm (\%) & 88.2 & 60.4 & 40.3 & 28.4 \\ Mean particle size/dp (mm) & 0.26 & 0.10 & 0.06 & 0.04 \\ Plasticity index & / & / & 8.1 & 14.7 \\ Penetration coefficient (cm/s) & 3.25 \(\times\) 10\({}^{-4}\) & 1.93 \(\times\) 10\({}^{-4}\) & 1.02 \(\times\) 10\({}^{-4}\) & 0.16 \(\times\) 10\({}^{-4}\) \\ Internal friction angle/\(\varphi\) (\({}^{\circ}\)) & 33.8 & 31.0 & 26.2 & 16.7 \\ Cohesion values/c (kPa) & 8.3 & 9.3 & 10.1 & 4.9 \\ \hline \end{tabular}
\end{table}
Table 2: Physical properties of tailings in different layers.  
Figure 4 is the finite element model of the 210 m tailings dam which was established by using the finite element software of MIDAS GTS. Boundary conditions were set as above, and this analysis contains two working conditions: normal water level and flood level.  
Figure 4: Grid diagram for 210 m dam height. (**a**) Models containing the tailing dams and surrounding mountains. (**b**) Models containing the starter dam and embankment.  
Figure 3: Grid diagram for 160 m dam height. (**a**) Models containing the tailings dam and surrounding mountains. (**b**) Models containing the starter dam and embankment.
## 2 Materials and Methods
### Antimicrobial Activity  
As test cultures, the following bacteria and yeasts from the American Type Culture Collection were used: _Candida albicans_ ATCC 885-653; _Staphylococcus aureus_ ATCC 25923; _Escherichia coli_ ATCC 25922; _Enterococcus faecalis_ ATCC 29212; _Streptococcus pyogenes_ ATCC 19615. We also used clinical strains of bacteria and yeasts (_S. aureus_, _E. coli_, _S. pyogenes_, _C. albicans_) isolated from the oral cavities of patients suffering from inflammatory periodontitis. The sensitivity of microorganisms to anthocyanins was determined by the agar diffusion test [15]. Anthocyanins were dissolved in DMSO (100 mg/mL). 100 \(\mu\)L bacterium inoculate in the physiological solution were adjusted to the equivalent of 0.5 McFarland standard, and evenly spread on the surface of Muller-Hinton agar (incubated at 37 \(\pm\) 2 \({}^{\circ}\)C for 24 h); yeasts--on SDA agar (incubated at 35 \(\pm\) 2 \({}^{\circ}\)C for 48 h). 20 \(\upmu\)L of extracts were introduced into wells 6 mm in diameter. The diameters of the inhibition zones were measured in millimetres including the diameter of the well. The antimicrobial effect was assessed by presence of growth inhibition zone. Each antimicrobial assay was performed at least three times.  
As a positive control were used: gentamicin (10 mg/disk) for Gram-negative bacteria, ampicillin (10 mg/disk) for Gram-positive bacteria, nystatin (100 UI) for _Candida_. DMSO was used as negative control. For the analysis of the results of the experiments, we used statistical software Microsoft Office-Excel (2013) with the calculation of averages, error, and standard deviation.  
To determine the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC), serial dilutions in Tryptone Soy Broth with the following anthocyanin concentrations were prepared: 100.0; 75.0; 50.0; 25.0; 20.0; 17.5; 15.0; 12.5; 10.0; 7,5; 5.0; and 2.5 mg/mL. The mixtures were then transposed into a 96-well microtitre (Greiner-BioOne, Austria), inoculated 100 \(\upmu\)L an overnight inoculum of bacteria (0.5 McFarland standard) and incubated at 37 \(\pm\) 2 \({}^{\circ}\)C for 24 h; the yeasts - on SDA agar incubated at 35 \(\pm\) 2 \({}^{\circ}\)C for 48 h. The well contents were sown on the surface of Muller-Hinton agar.
## 1 Introduction
In the single-chain based routing scheme, there is a single multi-edge chain to transmit data to sink. The main objective behind this research is to improve network performance in terms of lifetime and throughput. The rest of the paper is organized as follows: related work and motivation is discussed in Section 2. Section 3 gives the attenuation models to analyze the energy consumption, end-to-end delay and transmission loss of network. Sections 4-6 contain a brief description of 4-chain, 2-chain and single-chain based routing schemes, respectively. Simulation results are presented in Section 7. Finally, the paper is concluded in Section 8.
## 4 Results and Discussion
### Maintenance Phase
#### 4.2.3 Information Tracing  
In this project, we implemented the log function into the Hololens2 device. Landscape staff could use such a function to record various critical information during plant maintenance, such as the maintenance time; the name of the person; the use of water, fertilizer, and pesticides; and other related information during plant maintenance. All such information could be archived and updated in time to establish a comprehensive database for plant maintenance. Landscape managers could also use such a function to accurately trace the maintenance work for monitoring the maintenance process and examination of the maintenance quality. In this way, managers could in a timely manner regulate the plants with poor growth conditions and account for relevant staff. This ensures sufficient soil, moisture, and nutrients and provides the information system of plant maintenance.
## 2 Medium Baseline RTK Algorithm
### Triple-Frequency Ambiguity Resolution
Due to the fixed WL ambiguity \(\
abla\Delta N_{(1,-1,0)}\), the precision of float N1/N2/N3 ambiguities is improved, thus being beneficial for fast AR. It is worth noting that, during the algorithm program implementation, two threads are employed. One thread fixes the WL ambiguity \(\
abla\Delta\hat{N}_{(1,-1,0)}\) according to Equations (6)-(8), while another thread estimates the raw float N1/N2/N3 ambiguities using Equation (1). If the first thread successfully fixes the WL ambiguity, the ambiguity-fixed phase observations are utilized as virtual observations for the second thread's filter updating process. In case the WL ambiguity remains unfixed in the first thread, the algorithm proceeds to the next epoch.
## 4 Results and Discussion
### Storage Stability Results  
Figure 5 presents the results of the storage stability tests, illustrating how asphalt binders modified with nanomaterials retain compatibility. An upward trend in the \(\Delta\)T values is observed with increasing nanomaterial concentrations. The NS-modified asphalt binder exhibits more favorable storage stability than those modified with NT and NA, as indicated by its consistently lower \(\Delta\)T measurements. As the nanomaterial content increases, NS-modified binders show enhanced storage stability compared to NT and NA modifications. For example, the \(\Delta\)T for NS-modified asphalt with an 8% inclusion is 1.3 \({}^{\circ}\)C, representing a 225% increase over the neat asphalt baseline of 0.4 \({}^{\circ}\)C, yet it remains within the compatibility threshold. By contrast, the NT- and NA-modified binders show \(\Delta\)T increases of 400% and 300%, respectively, at the same inclusion rate. This enhanced storage stability of NS can be attributed to its lower density of 0.08 g/mL, compared to 0.51 g/mL for NT and 0.2 g/mL for NA, as detailed in Table 4, which promotes a more even distribution within the asphalt and reduces the potential for particle settling.  
Despite the higher densities of materials like NT and NA, all modified binders keep \(\Delta\)T values within the acceptable range for compatibility with the asphalt binder, set below 2.2 \({}^{\circ}\)C. This assures that, notwithstanding the different densities of the nanomaterials, the modified binders maintain the homogeneity necessary for practical application. The results suggest that the careful selection and quantification of nanomaterials can effectively enrich the asphalt binder's performance while ensuring its stability during storage.  
Figure 4: Effect of nanomaterials on softening point.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Nanomaterial** & **0\%** & **2\%** & **4\%** & **6\%** & **8\%** \\ \hline NT & \(-\)1.33 & \(-\)1.30 & \(-\)1.34 & \(-\)1.22 & \(-\)0.96 \\ NA & \(-\)1.33 & \(-\)1.32 & \(-\)1.22 & \(-\)0.95 & \(-\)0.85 \\ NS & \(-\)1.33 & \(-\)1.12 & \(-\)1.03 & \(-\)1.10 & \(-\)1.04 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Effect of nanomaterials on PI.
## 5 Complexity Algorithms
### Spectral Entropy Test  
Spectral Entropy (SE) measures disorder in the frequency domain [38]. Higher SE values, corresponding to flatter power spectra, indicate greater time series complexity. SE is calculated as follows: For a discrete time series \(\{x(\sigma),\sigma=0,1,2,\ldots,N-1\}\) of length N, the mean-adjusted series is \(x(\sigma)=x(\sigma)-\bar{x}\), where \(\bar{x}\) is the mean. The Discrete Fourier Transform (DFT) is given by  
\[\chi(k)=\sum_{\sigma=0}^{N-1}x(\sigma)e^{-2j\pi\sigma k/N}, \tag{60}\]  
Figure 10: \(0-1\) Test of the fractional discrete model for \((b,\lambda,d,\epsilon)=(20,0.04,0.2,0.13)\) with (a) \(\eta=0.4\), (b) \(\eta=0.9\), (c) \(\eta_{1}=0.63\), \(\eta_{2}=0.86\).  
where \(k\) takes values from \(0\) to \(N-1\), and \(j\) is the imaginary unit. The frequency spectrum at frequency index \(k\) is given by \(|\chi(k)|^{2}\), and the normalized probability distribution of the spectral power is defined as  
\[P_{k}=\frac{|\chi(k)|^{2}}{\sum_{k=0}^{N/2-1}|\chi(k)|^{2}}, \tag{61}\]  
Applying the DFT, corresponding to \(k\) values from \(0\) to \(N/2-1\). The spectral entropy is then represented as  
\[SE=\frac{\sum_{k=0}^{N/2-1}P_{k}\ln(P_{k})}{\ln(N/2)}, \tag{62}\]  
where \(\ln(N/2)\) represents random signal entropy.  
Given the parameter values \((b,\lambda,d,\epsilon)=(20,0.01,0.2,0.8)\), the SE complexity analysis outcomes for the fractional System (12), across a range of derivative orders, are shown in Figure 11.  
Fractional orders clearly influence system dynamics. Figure 11a demonstrates that the commensurate model (12) achieves a high complexity level when \(\eta\in[0.18,0.45]\). Similarly, as shown in Figure 11b, for \(\eta_{2}=0.86\), the complexity of (36) varies with \(\eta_{1}\). Notably, when \(\eta_{1}\) nears \(0.62\), the system reaches its highest complexity. Additionally, in Figure 11c, the impact of \(\eta_{2}\) can be observed on the dynamic behavior when \(\eta_{1}=0.68\). It is evident that complexity increases as \(\eta_{2}\) varies, reaching its peak when \(\eta_{2}\) approaches \(1\). These observations align with the findings from the largest Lyapunov exponent. Thus, for complex real-world applications, choosing appropriate fractional orders is crucial.
# Reputation and Trust Approach for Security and Safety Assurance in Intersection Management System
## 5 Discussion  
In the present work, the method, based on reputation and trust mechanisms has been proposed to improve the safety of AVs in the intersection management system by detecting AVs which transmit incorrect data, and excluding them from further interaction. To verify the effectiveness of the approach, software and physical simulations were conducted, the description of which is given in Section 4. The data obtained in the course of the software simulation, as presented in Figure 3a,b, allow saying that, with an increase in the load on the intersection, the system begins to react more strictly to incorrect data transmitted by AVs. It is worth mentioning that the software simulation was performed with significant assumptions compared to the real world, such as guaranteed message delivery during communication, no interference with data transmission, no delays in data processing, car movement in discrete space, etc. However, the implementation of the method on real physical models showed that its use allows detecting vehicles that transmit false data and excluding them from further communication, which makes it possible to continue further work on improving the implementation of the method on real physical models.
# Source Anonymity against Global Adversary in WSNs Using Dummy Packet Injections: A Survey
## 3 Dummy Packets Techniques
### Exponential Dummy Adaptive Distribution (EDAD)  
In _EDAD_[57], instead of using a uniform distribution as in _CAD_, _DAD_, and _CAD_ techniques, it employs an exponential distribution to reduce the overall overhead of the network. A set of dummy packets is transmitted following the exponential distribution. However, if a real event is detected, the scheduled fake packets will be replaced by the real ones. All nodes in the network use the equation \(t=\frac{\ln p}{-\lambda}\) to predict the next time interval for fake packets. This technique provides a high level of anonymity with reduced overhead. However, it has a higher delay than _CAD_ since it does not have a mechanism to control the latency of real events.
## 3 Results
### HRQoL  
Two-way ANOVA showed a significant effect of the session factor on the SF-36 total scores (F(1,15) = 112.52, \(p\) < 0.001, \(\eta\)2\(p\) = 0.88), physical functioning (F(1,15) = 192.11, \(p\) < 0.001, \(\eta\)2\(p\) = 0.92), social functioning (F(1,15) = 30.29, \(p\) < 0.001, \(\eta\)2\(p\) = 0.66), mental  
Figure 4: Mean \(\pm\) SD of the mean scores of the mood level (BMIS) during the preintervention session compared to the postintervention session for the Zumba group (ZG), Pilates group (PG), and control group (CG). * Significant difference between pre- and postintervention sessions at \(p\) < 0.05, ** at \(p\) < 0.001; # Significant difference between ZG and PG at \(p\) < 0.01; \(\epsilon\) Significant difference in the postintervention session between the CG and the two groups, ZG and PG, at \(p\) < 0.01.  
health (F(1,15) = 26.17, \(p<0.001\), \(\eta 2p\) = 0.63), and role limitations due to emotional problems (F(1,15) = 8.60, \(p<0.05\), \(\eta 2p\) = 0.36). In addition, a significant group \(\times\) session interaction on the SF-36 total scores (F(2,30) = 33.46, \(p<0.001\), \(\eta 2p\) = 0.69), physical functioning (F(2,30) = 192.11, \(p<0.001\), \(\eta 2p\) = 0.53), social functioning (F(2,30) = 12.29, \(p<0.001\), \(\eta 2p\) = 0.45), mental health (F(2,30) = 6.69, \(p<0.01\), \(\eta 2p\) = 0.30), and role limitations due to emotional problems (F(2,30) = 4.04, \(p<0.05\), \(\eta 2p\) = 0.21) was observed.  
The Bonferroni test results showed that the SF-36 scores were significantly better in the post-test session than in the pretest session in terms of the SF-36 total score (ZG: \(p\) = 0.0000; PG: \(p\) = 0.0000) and the physical functioning (ZG: \(p\) = 0.0000; PG: \(p\) = 0.00001) domain (\(p<0.001\) for both the ZG and PG) (Table 2), with significant better total scores (\(p\) = 0.0014) found for the ZG compared to the PG. Nonetheless, the Bonferroni test results showed that the scores on social functioning (\(p\) = 0.00007), mental health (\(p\) = 0.0004), and role limitations due to emotional problems (\(p\) = 0.041) were significantly higher in the post-test session than in the pretest session only for the ZG (Table 3).  
Concerning the rest of the SF36 domains, no significant effects of the session or group factors and no interaction were found regarding health perception, vitality, role limitations due to physical problems, and pain.