esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Distributions of Non-Spherical Submicron Particle Velocity
Figure 7: _Cont._  
In order to better understand the mechanism of aerodynamic focusing, we compared the velocity in the \(y\)-direction of the Al particle released from the same initial position with variable shape factors for \(d_{p}\) = 0.7 \(\upmu\)m in Figure 8. Here, \(u_{c\_y}\) is defined as the flow field velocity in the \(y\)-direction. With the increase in \(S_{f}\), the gradient of \(u_{p\_y}\) decreases in the diverging section of the micronozzle; in other words, \(u_{p\_y}\) at the outlet increases, making the aerodynamic focusing more obvious. It can be concluded that by reducing the shape factors, the particle focusing is weaker, but the particle velocity can be higher. Figure 9 shows the particle velocity in the \(y\)-direction with \(S_{f}\) = 0.5 for \(d_{p}\) = 0.7 \(\upmu\)m using three kinds of material particles, Al, Ti and Cu. The densities are \(\rho_{Al}\) = 2688 kg/m\({}^{3}\), \(\rho_{Ti}\) = 4506 kg/m\({}^{3}\) and \(\rho_{Cu}\) = 8880 kg/m\({}^{3}\), respectively. It is observed that the heavier Cu particle is easier to focus. Therefore, by increasing particle sizes and particle densities, the particle focusing is stronger due to their gained inertia. Therefore, the decisive influence of the aerodynamic focusing of submicron particles is inertial force. Figure 8: Velocity in the \(y\)-direction of the particle released from the same initial position with variable shape factors.
## 2 The Proposed RDHEI Method
### Room Reservation and Image Encryption
#### 2.1.4 Image Encryption
* \(calculateAuxInfo\) for the calculation of the auxiliary information as detailed in Figure 9.
* \(bitPlaneReordering\) for arranging the homogeneous blocks at the start and non-homogeneous blocks at the end of each bit plane. Furthermore, this function incorporates the compressed auxiliary information at the end of the homogeneous segment, allocating the remaining bits for data embedding through bit replacement. * \(encrypt\) to encrypt the resulting image with the data owner key \(k_{e}\). Figure 11: The encryption process. ```
Input: An image \(x\) of size \(n\times m\), encryption key \(k_{e}\) Output: The encrypted image \(x_{e}\). \(e,labelMap\gets mappedPredictionError(x)\) // binaryStr is used to store the quadtree decomposition of each bit plane \(binaryStr\): string // listHomogeneousBlocks is used to store the list of all homogeneous blocks from each bit plane \(listHomogeneousBlocks\): list // Extract 8 bit planes and calculate their quadtree decompositions for\(i\gets 0\) to 7 do \(bitPlane\leftarrow(e>>i)\) & 1 \(binaryStr,listHomogeneousBlocks\gets quadtreeDecomposition(bitPlane)\) \(auxInf\gets calculateAuxInfo(labelMap,binaryStr,listHomogeneousBlocks)\) \(bitPlaneReordering(e,i,compressedAuxInf,binaryStr,listHomogeneousBlocks)\) endfor \(x_{e}\gets encrypt(e,k_{e})\)
```  
**Algorithm 3** processImage algorithm: by the image owner.
# Exploiting Domain Knowledge to Address Class Imbalance in Meteorological Data Mining
## 6 Conclusions  
We applied Principal Component Analysis to reduce the 71 initial chosen variables of the ERA-40 dataset to 9 variables that were uncorrelated to each other, which explain nearly 85.2% of the variability in the original variables. The reduced ERA-40 dataset and the historical precipitation records of the Meteorological Station of Micra, Thessaloniki, Greece were then input into five data mining and machine learning classifiers we used to build models that predict the occurrence of precipitation at the station.  
The Multilayer Perceptron with back-propagation neural network classifier outperforms all other classifiers on AUC, revealing the most effective classifier in this meteorological domain.  
Moreover, the proposed new strategy D1U with the balanced distribution resulting from the combination of the one day restriction and the Randomized Undersampling method is the recommended strategy to address the class imbalance problem for the Multilayer Perceptron with back-propagation neural network, Decision Tree C4.5, k-Nearest Neighbor and RIPPER classifiers. Alternatively, the Randomized Under-sampling with the balanced distribution strategy U50 could also be used for the Multilayer Perceptron with back-propagation neural network and RIPPER classifiers. Finally, regarding the Naive Bayesian classifier, the proposed sampling strategies did not improve its performance when compared to the natural distribution. We observe that in the class imbalance problem, the application of sampling strategies based on the expertise on the data domain can improve the effectiveness of some classifiers.  
**Author Contributions:** Conceptualization, E.T. and G.E.; Methodology, E.T. and G.E.; Software, E.T.; Validation, E.T. and G.E.; Formal analysis, E.T. and G.E.; Investigation, E.T.; Resources, E.T.; Data curation, E.T.; Writing--original draft, E.T.; Writing--review & editing, E.T. and G.E.; Visualization, E.T. and G.E.; Supervision, G.E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We wish to thank the European Centre for Medium-Range Weather Forecasts and the Greek National Meteorological Service for providing us with the meteorological data. We would also like to thank our colleagues [PERSON] and [PERSON] for their valuable suggestions and comments.  
The authors declare no conflict of interest.
## 5 Implementation and Evaluation
### Cumulant Feature Extraction  
HOS third order cumulant features extracted from the raw CSI traces of the SignFi dataset assumes statistical estimates are unbiased. The process of cumulant feature extraction shown in Figure 7, extracts the cumulants as feature vector. As explained earlier, the SignFi dataset based on Wi-Fi CSI signals deploys one transmitter with three internal antennae (\(N_{t}=3\)), one receiver (\(N_{r}=1\)) and collected 200 samples per gesture from 30 subcarriers (\(N_{s}=30\)).  
HOS-Re, extracts 11 cumulant features (\(F_{1}\)_to \(F_{11},\ldots,F_{331}\)_to \(F_{341},\ldots,F_{661}\)_to \(F_{671},\ldots,F_{980}\)_to \(F_{990}\)_) from 3 channels having 30 subcarriers per channel; thus a feature vector of 990 cumulant features (11 \(\times\) 3 \(\times\) 30 = 990) were extracted. Similar features were extracted from the dataset, which consists of 7,500 instances from five different users for 150 gestures with 200 CSI samples per gesture. Finally, the nth record of the cumulant feature removes the sample mean and stores only the unbiased sample estimates of cumulants, as discussed in Section 3.2. The total number of cumulant features extracted (990) should be optimally reduced based on certain feature selection criteria, to reduce the computational complexity.  
The contours in Figure 8a-d shows 2D plots of the estimated third order unbiased cumulants, between the first-time lag and the second time lag. The plot for chosen gestures 'HORSE', 'MONKEY', 'SNAKE', 'TIGER' with overlapping segments, reveals the basic symmetry of the gestures. The time lag variable discussed in Equation (4), Section 3.2, with the variable maxlag set to 10 for revealing the basic symmetry in the plots.  
Figure 7: Cumulant feature extraction.
# Assessing Blockchain Consensus and Security Mechanisms against the 51% Attack
## 2 Background
### Blockchain Technology: The Digital Concept
#### 2.1.1 Consensus Mechanism  
A consensus mechanism is a protocol which is in place to ensure that all the participants in the blockchain network are complying with the agreed rules [4]. It ensures that the transactions emerge from a legitimate source by having every participant consent to the state of the distributed ledger. The public blockchain is a decentralized technology, and no centralized authority is in place to regulate the required act. Therefore, the network requires authorizations from the network participants for the verification and authentication of any activities that occur in the blockchain network. The whole process is done based on the consensus of the network participants, and it makes the blockchain a trustless, secure, and reliable technology for digital transactions. Distinct consensus mechanisms follow different principles, which enables the network participants to comply with those rules. Several consensus mechanisms have been introduced considering the requirements of secure digital transactions. However, proof of work (PoW), proof of stake (PoS), and delegated proof of stake (DPoS) are the few consensus protocols used by the main cryptocurrencies.
# Exploring Gait Recognition in Wild Nighttime Scenes
## 5 Results and Discussion
### Experimental Settings  
GaitSAT, as a flexible framework, can be dynamically adjusted according to the scale of different datasets. In the spatial feature extraction module, to thoroughly extract spatial dimension features, we employ three cascaded SA-GC blocks, each with a different number of output channels, aiming to comprehensively learn the intrinsic connections and complex interactions between various body parts. The multi-scale temporal rhythm perception module consists of three branches, each composed of serially connected SAT Units. The number of SAT Units was dynamically adjusted based on the dataset. Specifically, for the smaller-scale CASIA-B dataset, the number of SAT Units was 1; for Gait3D, the number of SAT Units was 2; for OU-MVLP and GREW, the number of SAT Units was 3. The output channel details for each basic block are shown in Table 2.  
During the training phase, to enhance the model's robustness and generalization capabilities, we implemented various data augmentation strategies. First, we added Gaussian noise to each keypoint with a probability of 0.3, simulating potential keypoint detection errors in real-world scenarios. Second, we applied an inverse operator to the entire skeleton with a probability of 0.01, resulting in a left-right flip of all skeleton points. Lastly, we randomly selected skeleton sequences with a fixed length of 60 to ensure input data consistency. For optimization, we employed the Adam optimizer in conjunction with the OneCycle learning rate scheduling strategy. The initial, maximum, and final learning  
Figure 7: Example of pedestrian sequence in extreme conditions (with key information obscured). For pedestrian (**a**), the two sequences demonstrate notable variations in lighting direction and color, where the clothing blends closely with the background, making feature extraction more challenging. For pedestrian (**b**), the sequences reveal changes in lighting color and intensity, as well as significant differences in clarity and camera angles. For pedestrian (**c**), the background includes strong lighting variations and dynamic changes caused by moving vehicles, posing additional challenges for accurate gait recognition.  
rates were set to \(1\times 10^{-5}\), \(1\times 10^{-3}\), and \(1\times 10^{-8}\), respectively. This configuration allows the model to converge rapidly in the early stages of training and fine-tune parameters in the later stages, thereby achieving optimal performance. We adjusted the batch size and number of training epochs according to the scale of each dataset, as shown in Table 2.
## 4 System and Channel Model  
A point-to-point line-of-sight FSO link between the optical coordinator and medical center based on an intensity modulation-direct detection (IM-DD) scheme is considered in this work. The optical signal travels in a straight line through the FSO channel. The turbulent channel is considered as memoryless, stationary, and ergodic; therefore, the system can be modeled by the following equation:  
\[y=gx+n=\eta Ix+n \tag{4}\]  
where \(g\) is the intensity gain, \(y\) is the received signal, \(x\) is the modulated optical signal, \(\eta\) is the photodetector (PD) conversion efficiency, \(I\) is the intensity, and \(n\) is additive white Gaussian noise (AWGN). We consider that the received signal's intensity is affected by atmospheric attenuation, turbulence, and geometrical losses. The atmospheric attenuation is modeled by the Beer-Lambert principle, provided by the following equation [45]:  
\[h_{l}=\exp(-d\xi) \tag{5}\]  
where \(\xi\) is the scattering coefficient. The scattering coefficient is weather-dependent and is a function of the visibility \(\
u\), which can be calculated by [45].  
\[\xi=\frac{3.91}{\
u}\bigg{(}\frac{\lambda}{550}\bigg{)}^{-1.3} \tag{6}\]  
The received signal's intensity at the PD randomly fluctuates, which is known as intensity scintillation or simply turbulence [46]. This is a major source of performance degradation in FSO systems. Different channel models were introduced to estimate the turbulence, including the gamma-gamma, log-normal, and negative exponential channel models [46]. The log-normal channel model is typically used for weak turbulence when FSO channel range is on the order of a few kilometers. The probability density function (PDF) of received signal's intensity follows a log-normal distribution, and is provided as follows [45; 46]:  
\[p_{I}(I)=\frac{1}{2l\sqrt{2\pi\sigma_{x}^{2}}}exp\bigg{[}-\frac{\ln(I/I_{o})^{ 2}}{8\sigma_{x}^{2}}\bigg{]}, \tag{7}\]  
where \(\sigma_{x}^{2}\) is the intensity variance due to turbulence, provided by [46]  
\[\sigma_{x}^{2}=0.307C_{n}^{2}k^{7/6}L^{11/6} \tag{8}\]  
where \(L\) is range of FSO channel, \(k=2\pi/\lambda\) is the wave number, and \(C_{n}^{2}\) is the refractive index structure parameter. The value of the \(C_{n}^{2}\) parameter is time-dependant and varies in the range of \(10^{-17}\) to \(10^{-12}\) m\({}^{-2/3}\), respectively indicating weak to strong turbulence [46].
## 2 Materials and Methods
### Isotherm Studies  
For the P\({}_{\text{i}}\) or As\({}_{\text{i}}\) adsorption experiments, 10 mg of each material was added to 10 mL phosphate stock solution with the P\({}_{\text{i}}\) or As\({}_{\text{i}}\) concentration ranging from 50 ppm to 500 ppm and 1000 ppm at pH 6.3-6.7. After the samples were shaken at 23 \({}^{\circ}\)C for 24 h, the equilibrium P\({}_{\text{i}}\) concentration in the solution was measured using the acquired vanadate-molybdate reagent, while the As\({}_{\text{i}}\) concentration was determined via the prepared solution as outlined in the Supplementary Material (SM). After the reaction with the reagent, the solution absorbance was measured using a SPECTRONIC 200E spectrometer (Thermo Scientific, Waltham, MA, USA) at 720 nm for P\({}_{\text{i}}\) and 900 nm for As\({}_{\text{i}}\)[22; 23].  
\(Q_{e}\) was estimated using Equation (1) where \(Q_{e}\) (mg/g) is the equilibrium uptake, \(C_{o}\) (ppm) and \(C_{e}\) (ppm) are the initial and equilibrium anion concentration of the solution, \(m\) (g) is the adsorbate weight and \(V\) (L) is the solution volume. The Sips model (Equation (2)) was fit to calculate the phosphate isotherm parameters [24].  
\[Qe=\frac{(C_{0}-C_{e})*V}{m} \tag{1}\]  
The Sips isotherm is defined by Equation (2), which can account for Langmuir and Freundlich isotherm behaviour under certain limiting conditions [25; 26; 27]. When \(n=1\), the model describes Langmuir behaviour, whereas Freundlich behaviour occurs when \(K*C_{e}^{n}<<1\). \(Q_{e}\) (mg/g) and \(C_{e}\) (mg/L (or ppm)) represent the equilibrium concentration of adsorbate in the adsorbent phase and the concentration of adsorbate in the liquid phase, respectively. Therefore, the adsorption isotherm parameters will be calculated via the Sips isotherm model in Equation (2). \(K\) is the Sips isotherm constant, and \(n\) (dimensionless) is an exponent term that provides a measure of the surface heterogeneity of the adsorption sites present on the adsorbent's surface. Particularly, a value of n closer to 1 reflects adsorption onto a homogeneous surface, while deviation away from 1 reflects adsorption onto heterogeneous surface sites [28].  
\[Qe=\frac{Q_{max}*K*C_{e}^{n}}{1+K*C_{e}^{n}} \tag{2}\]
###### Abstract  
A variety of applications and forwarding protocols have been proposed for opportunistic networks (OppNets) in the literature. However, the methodology of evaluation, testing and comparing these forwarding protocols are not standardized yet, which leads to large levels of ambiguity in performance evaluation studies. Performance results depend largely on the evaluation environment, and on the used parameters and models. More comparability in evaluation scenarios and methodologies would largely improve also the availability of protocols and the repeatability of studies, and thus would accelerate the development of this research topic. In this survey paper, we focus our attention on _how_ various OppNets data forwarding protocols are evaluated rather than _what_ they actually achieve. We explore the models, parameters and the evaluation environments and make observations about their scalability, realism and comparability. Finally, we deduce some best practices on how to achieve the largest impact of future evaluation studies of OppNets data dissemination/forwarding protocols.  
Keywords:opportunistic networks; forwarding; data dissemination; evaluation methodology; simulation; OMNeT++; ONE; Adyton +
Footnote †: journal:
# A Blockchain-Based Secure Inter-Hospital EMR Sharing System
## 4 Security Analysis
### Forward and Backward Secrecy  
If a malicious attacker compromises the session key \(SEK_{AP}\), \(SEK_{BP}\) and \(SEK_{AB}\) which is established between the sender and the receiver, the proposed system still satisfies forward and backward secrecy. The malicious attacker may use the compromised session key \(SEK_{AP}\), \(SEK_{BP}\) and \(SEK_{AB}\) for future malicious communication or use it to obtain previously transmitted messages. However, the session key \(SEK_{AP}\), \(SEK_{BP}\) and \(SEK_{AB}\) is randomly chosen by the sender and the receiver, and the session key may only be used in the current round. The malicious attacker cannot use the same session key \(SEK_{AP}\), \(SEK_{BP}\) and \(SEK_{AB}\) for future malicious communication or use it to obtain previously transmitted messages. Therefore, forward and backward secrecy is achieved in the proposed scheme.
# Multi-View Ground-Based Cloud Recognition by Transferring Deep Visual Information
## 3 Approach
### Transfer Deep LBP  
We propose TDLBP to address the view shift problem. The convolutional layers can capture more local characteristics [40; 41]. Therefore, we propose to extract local patterns from the PSMs of a convolutional layer to represent cloud images. TDLBP is an improved operator over LBP,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Config.** & **The VGG-19 Model** \\ \hline conv\_1 & \(3\times 3\times 64\) \\ conv\_2 & \(3\times 3\times 64\) \\ \hline \multicolumn{3}{c}{max pooling} \\ \hline conv\_3 & \(3\times 3\times 128\) \\ conv\_4 & \(3\times 3\times 128\) \\ \hline \multicolumn{3}{c}{max pooling} \\ \hline conv\_5 & \(3\times 3\times 256\) \\ conv\_6 & \(3\times 3\times 256\) \\ conv\_7 & \(3\times 3\times 256\) \\ conv\_8 & \(3\times 3\times 256\) \\ \hline \multicolumn{3}{c}{max pooling} \\ \hline conv\_9 & \(3\times 3\times 512\) \\ conv\_10 & \(3\times 3\times 512\) \\ conv\_11 & \(3\times 3\times 512\) \\ conv\_12 & \(3\times 3\times 512\) \\ \hline \multicolumn{3}{c}{max pooling} \\ \hline conv\_13 & \(3\times 3\times 512\) \\ conv\_14 & \(3\times 3\times 512\) \\ conv\_15 & \(3\times 3\times 512\) \\ conv\_16 & \(3\times 3\times 512\) \\ \hline \multicolumn{3}{c}{max pooling} \\ \hline fc\_17 & 4096-d \\ fc\_18 & 4096-d \\ fc\_19 & 1000-d, softmax \\ \hline \hline \end{tabular}
\end{table}
Table 1: The configuration of the VGG-19 model. con_i denotes the \(i\)-th convolutional layer, and the convolution stride is set to 1 pixel. Max pooling is implemented by a sliding window of \(2\times 2\) pixels with stride 2.  
Figure 3: The procedure of generating part summing maps.  
which computes a region representation based on the PSMs. The TDLBP is not only invariance to intensity scale changes, but is robust to view shift and obtain the completed scale information of cloud. We first partition each PSM into \(L\times L\) (\(L\) = 1, 2, 3) regions. Second, we extract LBP in each region of the PSMs. We take the PSMs of \(2\times 2\) regions as an example (see Figure 4) and perform the following steps:  
1. Feature extractions for each region in the PSMs. Within each region, we extract three scales of LBP histograms, i.e., \((P,R)\) = (8, 1), (16, 2) and (24, 3). Hence, each region can be described as a 54 dimensional descriptor.
2. Feature pooling. Max pooling is applied on all local features of the local regions at the same position, i.e., preserving the maximum value of each bin among all histograms, resulting in four histograms. The pooled feature of each local region is more robust to view shift.
3. Feature concatenation. The four histograms are concatenated into one histogram to represent each cloud image. The resulting histogram can capture global information and local characteristics of image regions, simultaneously.
# A 12-Bit 1-GS/s Pipelined ADC with a Novel Timing Strategy in 40-nm CMOS Process
## 3 Circuit Implementation and Calibration
### Operational Amplifier (Op-Amp) in the SHA or MDACs  
Figure 9a depicts a fully differential 2.5-bit flip-around MDAC. In the flip-around MDAC schematic, capacitors (Cs and Cf) are used for the sampling phase and the feedback phase, respectively. Cap arrays are composed of capacitors and switches (6 \(\times\) Cs, 6 \(\times\) SP2, 6 \(\times\) SN2). Switch S10 can guarantee that the beginning of output settling is common-mode voltage 0.9 V, decreasing the error of output to some extent. In the sampling phase, switches (2 \(\times\) SP1, 6 \(\times\) SP2, 6 \(\times\) SN3, 2 \(\times\) SN4) turn on. Switch S10 also turns on. At the same  
Figure 8: (**a**) Flip-around SHA schematic. (**b**) Bootstrapped switches, used in the SHA and MDACs.  
time, 3-bit flash, which includes six comparators, obtains the result of the comparison by comparing VIN with VREF. In the feedback phase, switches (SP9, SN9) turn on, and the operational amplifier operates normally.  
A higher gain operational amplifier with a telescope cascode configuration is used in flip-around SHA or MDACs, as is shown in Figures 8b and 9b. Its advantages are high single-stage gain and current efficiency, relatively low noise, and greater stability compared with multistage operational amplifiers. When CLKF is at a high voltage, the operational amplifier of MDAC1 operates normally. Both the NMOS and PMOS sides are cascaded. AP1 and AP2 are the gains of auxiliary op-amps. The gain of this operational amplifier is described by Equation (7):  
\[\mathrm{A}=\mathrm{g}_{\mathrm{m6}}\times\{(1+\mathrm{AP}_{2})\times\mathrm{g }_{\mathrm{m7}}\mathrm{r}_{\mathrm{o7}}\mathrm{r}_{\mathrm{o6}}\}\|\left\{(1+ \mathrm{AP}_{1})\times\mathrm{g}_{\mathrm{m8}}\mathrm{r}_{\mathrm{o8}}\mathrm{r }_{\mathrm{o9}}\right\} \tag{7}\]  
\(\mathrm{G}_{\mathrm{m6}}\), \(\mathrm{g}_{\mathrm{m7}}\), and \(\mathrm{g}_{\mathrm{m8}}\) are transconductances of M6, M7, and M8, respectively. \(\mathrm{R}_{\mathrm{o6}}\), \(\mathrm{r}_{\mathrm{o7}}\), \(\mathrm{r}_{\mathrm{o8}}\), and \(\mathrm{r}_{\mathrm{o9}}\) are the resistance of M6, M7, and M8, respectively. Simulation results show that the gain of this op-amp is about 78 dB. GBW is about 13.5 GHz.