esa-sceva/satcom-chunk-collection huggingface dataset:
# A Drone-Enabled Approach for Gas Leak Detection Using Optical Flow Analysis +
## 5 Results and Discussions
### Comparative Analysis between Stabilization Methods
Since the similarity of frames with the reference frame can be assumed to be high in the lack of sudden and large camera motions, in addition to the similarity factor, ITF can evaluate the resulted distortion in the stabilization process. It is worth mentioning that ITF value of a series of repetitive image frames would be infinity. \[PSNR(I_{k},Ief)=10 log\left(\frac{255^{2}NM}{\sum_{n=1}^{N}\sum_{m=1}^{M}\left[ I_{k}(n,m)-I_{ref}(n,m)\right]^{2}}\right),I(n,m)=[0,255] \tag{3}\]  
\[ITF=\frac{1}{N_{f}}\sum_{k=2}^{N_{f}}PSNR(I_{k},I_{ref}),I_{ref}=I_{1} \tag{4}\]  
Interframe Similarity Index (ISI) is another metric used in this study which is simply the average of Structural Similarity Index (SSIM). SSIM can be defined as the structural similarity index between the reference frame and another frame in the footage, which is explained in Equation (5). ISI shows the perceptual similarity of frames and the reference frame. \[ISI=\frac{1}{N_{f}}\sum_{k=2}^{N_{f}}SSIM(I_{k},I_{ref}) \tag{5}\]  
Local motions are another area to investigate in the stabilization methods. The smoothness of camera motions is another metric named as AvSpeed [37], which is considered in this study, since large motions or sudden movements can create flows with high magnitude disturbing the optical flow analysis required in the gas leak detection algorithm. To measure this metric, the average speed of selected feature points are calculated using Equation (6),  
Figure 8: The processing time for stabilizing one frame in seconds. The abbreviations of method names are defined in Section 3.2 just for referencing simplification. where \(N_{p}\) is the number of feature points, and \(d_{i}(k)\) is the coordinate of ith feature point. The low AvSpeed value shows more perceptual similarity in the consecutive frames. \[AvSpeed=\frac{1}{N_{p}(N_{f}-1)}\sum_{i=1}^{N_{p}}\sum_{k=1}^{N_{f}-1}\left\|d_{ i}(k)\right\|_{2}d_{i}(k)=d_{i}(k+1)-d_{i}(k) \tag{6}\]  
In Table 5, the metrics for all the candidate methods are calculated for all the scenarios. For the first scenario, PC-RF showed the more reliable results; however, because of the few frames with inaccurate registration as shown in Figure 7, the AvSpeed was higher than the other methods. For the second scenario containing thermal image stream with low camera motions, L1-OP and PC-RF both showed significant results. As in the first scenario, in the third scenario, despite the existence of constant but smooth camera shaking and moving objects in the background, PC-RF demonstrated much better results compared to other methods. Finally, in the fourth scenario containing shaking motions causing motion blurriness in some of the frames, OPC showed outstanding results compared to others; however, PC-RF presented better perceptual similarity.
# Analysis and Identification of Nonlinear Acoustic Damping in Miniature Loudspeakers
## 4 Experimental Validation and Discussion
### Results and Discussions
#### 4.2.2 Discharge Coefficient  
As mentioned in Section 3, the discharge coefficient is used to describe the flow separation phenomena near the orifice. The discharge coefficient is an experimentally determined quantity and is known to be sensitive to the orifice shape and surface conditions. Several different values of the discharge coefficient have been used in the literature for squared-edge orifice cases. [PERSON] [6] employed a value of 0.61 in his theoretical calculations,  
Figure 6: Comparison between the identified and calculated values of the nonlinear acoustic damping \(R_{a}(v_{0})\).  
Figure 7: Comparison between the identified values of the equivalent mechanical resistance \(R_{ma}(v)\) and the measured mechanical resistance \(R_{m}(v)\).  
and [PERSON] et al. obtained a value of 0.64 for high sound pressure condition [9]. Moreover, [PERSON] et al. obtained a value of 0.7 based on numerical simulation results [11].  
The discharge coefficient can be obtained from the identification process proposed in this study. Based on Equations (14) and (15), the discharge coefficient can be derived as \(C_{D}=\frac{\rho_{0}A^{3}}{\rho_{0}A^{3}+c_{1}^{2}S_{0}^{2}}\). The identified values of the discharge coefficient and values used in literatures are listed in Table 2. The average value obtained from the identification process is 0.698, which is very close to the values used in [PERSON]'s work.
# Real-Time Failure Prediction of ROADMs by GAN-Enhanced Machine Learning
## 4 Experimental Results and Discussion
### Combined Optical Filter Shift Detection and BER Evaluation
Thus, for actual implementation of this proposed approach, pre-training and generation of DC-GAN-augmented data could be carried out by a conventional server machine near network equipment. Then, the trained model can be copied to the corresponding detection container applications in WhiteBox transponders. The network disaggregation trend has been expanding mainly in the data center interconnection network and gradually penetrating into the Tier1 carrier network. Therefore, such a container-based detection approach could be easily implemented in the WhiteBox-based network.
# DPMF: Decentralized Probabilistic Matrix Factorization for Privacy-Preserving Recommendation
## 4 Evaluation
### Setting  
#### 4.1.1 Datasets  
As shown in Table 2, we implement our experiment on two real-world datasets, namely _FilmTrust_[21] and _Epinions_[22, 23]. The _FilmTrust_ dataset contains 35,497 records with 1508 users and 2071 items. Moreover, 1853 trust statements are given as the relationship between users. Another dataset used in our experiment is _Epinions_ collected from online website Epinions.com, which is also a famous benchmark for evaluating recommendation systems. However, we only sample 30,000 records randomly from the whole dataset due to its large scale. It consists of 1336 users and 6584 items with 7465 trust statements. For both datasets, we scale the ratings into the range of \([0,1]\).
## 4 Evaluation of Model Developments
### Observational Data to Evaluate Model Developments
#### 4.2.3 Fluxnet  
LaThuile FLUXNET data ([PERSON] et al. [79]) used are observations from 253 research sites ([[https://fluxnet.org/data/la-thuile-dataset/](https://fluxnet.org/data/la-thuile-dataset/)]([https://fluxnet.org/data/la-thuile-dataset/](https://fluxnet.org/data/la-thuile-dataset/)), 1 February 2021) which belongs to the FLUXNET eddy covariance network. Net ecosystem exchange (NEE), sensible heat flux (H) and latent heat flux (LE) at high temporal resolution (30 to 60 min) are provided in this dataset. Additionally derived gross primary production (GPP) and ecosystem respiration (Reco) are also provided based on the partitioning method proposed by [PERSON] et al. [80]. Only data without gap filling and flagged as high-quality are used.
# Reputation-Based Blockchain for Spatial Crowdsourcing in Vehicular Networks
## 5 Performance Evaluation
### Performance Based on Spatial Crowdsourcing Reputation  
This section details the characterization of the performance for spatial reputation, which contains nearly 50 missions.  
(i) As we can see in Figure 8, the increase in the number of vehicles will directly lead to increased data collected to complete the crowdsourcing missions, thereby shortening the mission time and improving the efficiency of road-condition gathering. Due to budget  
Figure 6: Reputation vs. Mining rounds.  
Figure 7: Vehicles vs. The overall reputation.  
constraints, the collected data will drop from the peak and then shift back and forth. In addition, the greater increases produced a larger amount of provided data. Eventually, different budgets could bear a significant impact on the size of the collected data.  
(ii) As plotted in Figure 9, a high-reputation entry threshold has considerable impact on the data authenticity. The impact of the threshold on authenticity shows a trend of first decreasing and then increasing. This is mainly because a high reputation threshold leads to a lower overall reputation level, until it subsequently promotes more users with a background of honest behavior to join and enhance the reputation of the pool.  
(iii) We can see from Figure 10 that the increase in the number of vehicles participating in crowdsourcing tasks leads to improved data authenticity. As such, a farther distance from the target location (effective radius) would motivate more miners to become involved in the computing. This is because more blockchain users will improve the security of task in computing and verifying. This can also be analyzed from the perspective of budget, that is, a longer radius generates higher budgets, which in turn inspires more users to participate.  
Figure 8: Vehicles vs. Data size.  
Figure 9: Entry threshold vs. Data authenticity.
## 3 Results and Discussion
### Corrosion Performance  
Figure 4 shows the potentiometric polarization results of the samples immersed for 1 h at 37 \({}^{\circ}\)C in Ringer's solution, in which the anode branch represents the dissolution of the Ti under higher potential, whereas the cathode branch represents the hydrogen evolution reaction [26]. It can be seen in Figure 4 that Nb ion implantation shifted the anode part towards positive potential, and areas of passivation can be observed obviously, which indicates that the implanted samples have better thermodynamic stability. In addition, the results present a higher slope in the anode part than in the cathode part, indicating that the corrosion process is controlled by the anodic process (active dissolution of the metal) and is conducive to inhibiting the dissolution of Ti alloys [27].  
Table 2 shows the electrochemical parameters obtained, including corrosion potential (E\({}_{\text{corr}}\)), corrosion current density (I\({}_{\text{corr}}\)) and polarization resistance (R\({}_{\text{p}}\)) from the Tafel plots. The results clearly show that, compared with the substrate, the E\({}_{\text{corr}}\) of implanted samples increased by 0.15 V, and the I\({}_{\text{corr}}\) also decreased by an order of magnitude. In addition, the R\({}_{\text{p}}\) of the implanted samples is about three times as much as that of the substrate. Additionally, it can be observed that the implanted sample has a lower passivation current, which indicates that the passivation layer of the implanted sample has a better corrosion resistance. The above results show that Nb ion modification can effectively improve the thermodynamic and kinetic stability of the passivation layer, exhibiting a better corrosion protection effect in the short-term immersion.
## 4 Proposed Method: XQIFFT
### Power-Scaled QIFFT (XQIFFT)  
While applying the QIFFT on a logarithmically-scaled magnitude spectrum was found to outperform the QIFFT on a linearly-scaled magnitude spectrum, some systematic error remains [30]. To further reduce the estimation error of the QIFFT method, we review our proposed variation on the QIFFT method that performs a parabolic fit to a magnitude spectrum rescaled by a power function [20].  
The power-scaled QIFFT--the _XQIFFT_--uses (13) and (14) and has a tuning parameter \(p\) that controls the following nonlinear scaling and un-scaling functions:  
\[\mathrm{f}(\Theta) =\Theta^{p} \tag{17}\] \[\mathrm{f}^{-1}(\Phi) =\Phi^{1/p}\,. \tag{18}\]  
The intuition leading to the XQIFFT is simply to try to scale the window transform shape to resemble a parabola. The performance of the XQIFFT depends heavily on choosing the tuning parameter \(p\) properly to scale the shape of the main lobe to be maximally parabolic. The shape of the main lobe is highly dependent on window type, as well as zero-padding factor and window length. Hence the proper value of \(p\) is also window-dependent. A simple procedure for choosing \(p\) is presented later, in Section 5.2.
## 3 Methodology
### Cnn  
The architecture of a CNN layer is primarily comprised of a convolutional layer and a pooling layer, as shown in Figure 4. The convolutional layer is responsible for extracting features from the input data, while the pooling layer serves to reduce the spatial dimensions of the feature maps, thus enhancing computational efficiency and controlling overfitting. Together, these layers form the foundational building blocks of CNNs, enabling them to learn hierarchical patterns from the input data.  
1. Convolutional layer  
The process of extracting features from input data using convolutional kernels in convolutional layers can be expressed as:  
\[\begin{split} Z^{l+1}(i,j)&=\left[Z^{l}\otimes w^{l +1}\right](i,j)+b\\ &=\sum_{k=1}^{K_{l}}\sum_{x=1}^{f}\sum_{y=1}^{f}\Bigl{[}Z^{l}_{k} (s_{0}i+x,s_{0}j+y)w^{l+1}_{k}(x,y)\Bigr{]}\\ &+b(i,j)\in\{0,1,...,L_{l+1}\},\\ L_{l+1}&=\frac{L_{l}+2p-f}{s_{0}}+1\end{split} \tag{6}\]  
where \(Z^{l+1}\) and \(Z^{l}\) represent the output and input of the layer \(l+1\), respectively, \(K\) is the number of channels, \(Z(i,j)\) is the pixel at the point\((i,j)\), \(b\) is the weight bias term, \(w^{l+1}\) is the weight of the convolution kernel in the layer \(l+1\), \(L_{l+1}\) is the size of \(Z^{l+1}\), \(s_{0}\) and \(f\) are  
Figure 4: Basic structure of CNN.  
the size of the convolution step and convolution kernel, respectively, and \(p\) is the number of filling layers.  
(2) Pooling layer  
The pooling layer functions between consecutive convolutional layers to choose the feature mappings, efficiently decreasing the dimensionality of the feature mappings while preserving crucial global information. In this research, the pooling procedure can be formulated as:  
\[A_{k}^{l}(i,j)=\left[\sum_{x=1}^{f}\sum_{y=1}^{f}A_{k}^{l}(s_{0}i+x,s_{0}j+y)^{ p}\right]^{\frac{1}{p}} \tag{7}\]  
Pooling layers can be categorized into various types based on the setting of the default parameter \(p\). Commonly utilized in CNN designs are mean pooling (\(p=1\)) and maximum pooling (\(p\rightarrow\infty\)), which serve to maintain the background and texture details of an image, albeit at the cost of reducing the spatial dimensions of the information or feature map.
## 1 Introduction
### Practice  
MATSim (Multi-Agent Transport Simulation) is an agent-based simulator that leads the example of the activity-based model approach considered in this study. The program is a new open-source framework for the implementation of large-scale activity-based mobility simulations [8]. MATSim uses a co-evolutionary principle, in which each agent optimizes its daily activity plan iteratively in a competition between the space-time slots of all agents traveling. Each cycle starts with an initial demand arising from the daily activity chains of the inhabitants in the study area. This initial demand is optimized individually, based on natural selection, by each agent during iterations. Each plan in a daily activity chain has an associated score. A replaceable econometric utility model [8] is used to calculate these scores, which is the main tool for optimization within the program. The software is flexible and modifications of the program's source code are possible. The software has been utilized in recent years for many purposes, including actual travel demand management [9], testing planned transport infrastructures and new transportation modes [10], observing travel behavioral changes [11], urban logistics planning [12], evacuation planning for natural disasters such as tsunami and hurricane [13; 14], and smart city solutions such as testing V2V (vehicle-to-vehicle) and V2I (vehicle-to-infrastructure) technologies [15].  
Agent-based models such as MATSim mainly require: (1) the simulation environment and (2) synthetic travel demand and transport supply. The simulation environment of MATSim is easily accessible by the open-source code repository of the software while the second requirement is a specific process dependent on certain spatial-temporal raw datasets. In order to support the reproducibility of agent-based simulation studies, the Eqasim framework was introduced in [16]. Eqasim is a pipeline-processing initial raw data to simulation step that utilizes raw datasets such as census data (CD), household travel surveys (HTS), and geographic datasets (GD).
## 3 Conclusions  
In this work, the bi-directional preemptive CP with a constant price interval is considered. For the first time upper and lower bounds that are independent from the number of runs, are given. Furthermore, the algorithm \(EBA\) is presented. \(EBA\) optimally solves the problem for \(T=2\). Due to this, the performance ratio of \(EBA\) for a specific type of sequences could be used to calculate the lower and upper bound. \(EBA\) is based on the idea of error balancing. The definition of a symmetric algorithm is introduced. This definition makes it easier to determine the competitive ratio of a certain type of online algorithms.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(M\) & **1000** & **100** & **10** & **2** \\  & \(T\) & & & & \\ \hline \(EBA\) & 2 & 1.9387 & 1.8182 & 1.5195 & 1.1716 \\  & 5 & 11.0949 & 7.4843 & 3.5321 & 1.5494 \\  & 7 & 36.9558 & 20.4750 & 6.6381 & 1.9286 \\  & 13 & 1365.7294 & 419.2238 & 44.0638 & 3.7193 \\ \hline \(bTH\) & 2 & 1.9387 & 1.8182 & 1.5195 & 1.1716 \\  & 5 & 12.1948 & 7.6472 & 3.4894 & 1.5385 \\  & 7 & 61.8000 & 26.8163 & 7.2430 & 1.9615 \\  & 13 & 9130.6154 & 1233.7339 & 66.2846 & 4.0865 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The experimental results for the upper bound of \(EBA\) and the lower bound of \(bTH.\)The algorithm \(EBA\) is compared with the two other competitive online algorithms for the bi-directional preemptive CP with a constant price interval that exists in the literature. For specific parameters, \(EBA\) is the best algorithm known so far. However, it is shown that \(EBA\) is not optimal in the case of \(T\geq 3\). Therefore, one open question is how to optimally solve the bi-directional preemptive CP with constant price interval for \(T\geq 3\). Another open question is how to solve the corresponding PSP with \(n\geq 2\) assets.  
My special thanks go to [PERSON] for his comments and support during my PhD thesis.  
The author declares no conflict of interest.
# Systematic Literature Review of Challenges in Blockchain Scalability
## 8 RQ3: How Researchers Address the Scalability Issue in Blockchain?
### On-Chain Solution
#### 8.1.1 Block-Data-Related Approaches  
1. Block size increase  
The big block size is typical of on-chain solutions. In this approach, the public blockchain scalability issue has some significant linkages to the block size [118]. Obviously, the big block can accommodate more transactions, which would undoubtedly improve the overall throughput. Bitcoin unlimited [8] is an example of a big block. This method increases transmission limit and decreases cost related to transmission as compared to the conventional method. However, it downgrades block propagation efficiency (in time) and may increase the blockchain forking chances, leading to the probability of orphan block to incur higher maintenance cost. The full node would be more expensive to operate through this approach. Miners prefer this approach because the increased block size can accommodate more transactions in one block, and they incur a larger transaction fee when mining that block.
## 3 Results and Discussion
### The Role of pH during Complex Coacervation  
One of the most critical parameters in microencapsulation of oils by complex coacervation is obtaining the proper pH, where oppositely charged wall materials form approximately neutral coacervate nodules which self-assemble on the oil-water droplet interface. When using a combination of a weakly anionic polyelectrolyte such as GA and a protein, this is typically achieved by lowering the pH slightly below the isoelectric point of the protein [19], as previously reported, for example, gelatin type A and B [17; 20; 22], whey protein isolate [23; 24] and soybean protein isolate [25]. Here, we adapted this approach to a combination of bovine serum albumin (BSA) and gum Acacia (GA), using a ratio of BSA:GA = 1:1 (_w:w_) as wall materials, using different polymer concentrations, Polymer:Oil ratios and crosslinker concentrations. A summary of the reaction conditions and obtained results is shown in Table 1. Figure 1 shows typical optical microscopy images of four of the critical steps in microencapsulation of peppermint essential oil using BSA:GA (1: _w:w_); during emulsification (Figure 1A, pH > 5), coacervation (Figure 1B, pH = 4.2), during crosslinking (Figure 1C, pH = 9) and for fully formed crosslinked capsules (Figure 1D, pH = 7). Lowering the pH below the isoelectric point of BSA (plBSA = 4.6-4.8) results in the formation of loosely associated flocs (Figure 1B), which are easily redispersed into singlecapsules upon increasing pH above plBSA during crosslinking (Figure 1C). The complex coacervation process described herein results in well-dispersed spherical mononuclear microcapsules, with similar sizes as the parent emulsions.
## 5 Energy Efficiency of the MHCP-MHCP Network
### Downlink Channel Capacity  
In this paper, the total downlink channel capacity is defined as  
\[C_{total}=\sum\
olimits_{k=1}^{2}A\lambda_{u}P_{C-k}C_{k} \tag{11}\]  
where A is the area of the MHCP-MHCP network, \(P_{C-k}\) is the probability of the tagged MUE accesses to the BS in k-th tier, \(C_{k}\) is the downlink channel capacity between a k-th tier BS and the tagged MUE. For simplicity without loss of generality, we assume that each BS allocates bandwidth equally to all MUEs accessing it. Thus, \(C_{k}\) can be expressed as  
\[C_{k}=\mathrm{E}\bigg{[}\frac{B}{U_{k}}\log_{2}(1+\beta_{k})\bigg{]}\overset{( a)}{=}\frac{B}{\mathrm{E}[U_{k}]}\log_{2}(1+\beta_{k}) \tag{12}\]  
where step (a) follows from the assumption that \(\beta_{k}\) and \(U_{k}\) are independent, \(B\) is the bandwidth that each BS can allocate to users, and \(U_{k}\) represents the number of UEs accessing the tagged BS in the k-th tier. According to [25; 45], \(\mathrm{E}[U_{k}]\) is given as  
\[\mathrm{E}[U_{k}]=1+1.28\rho_{k}\lambda_{u}/\lambda_{k-MHCP} \tag{13}\]  
where \(\rho_{k}\) is the proportion of the MUE coverage contributed by the BS in \(k\)-th tier. In open access mode, \(\rho_{k}\) in PPP-modelled two-tier HCN can be expressed as [4]  
\[\rho_{k-PPP}=\frac{\lambda_{k}P_{Tk}^{2/\alpha}\beta_{k}^{-2/\alpha}}{\sum_{k= 1}^{2}\lambda_{k}P_{Tk}^{2/\alpha}\beta_{k}^{-2/\alpha}} \tag{14}\]  
Since the ASAPP method is adopted in this paper, each tier of the MHCP network is approximated to the PPP network with the same density by SIR threshold scaling. Thus, \(\rho_{k}\) in MHCP-MHCP networks can be written as  
\[\rho_{k}=\frac{\lambda_{k-MHCP}P_{Tk}^{2/\alpha}(\beta_{k}/G_{k})^{-2/\alpha} }{\sum_{k=1}^{2}\lambda_{k-MHCP}P_{Tk}^{2/\alpha}(\beta_{k}/G_{k})^{-2/\alpha}} \tag{15}\]  
Substituting (15) into (13) and then (13) to (12), \(R_{k}\) can be obtained. Moreover, the probability of the tagged MUEs accessing the BSs in the k-th tier can be written as  
\[\begin{array}{ll}\mathrm{P_{C-k}}&=\mathrm{P_{C}}(\{\lambda_{k-MHCP}\},\{ \beta_{k}\},(P_{Tk}))\cdot\rho_{k}\\ &=\frac{\pi}{C(\alpha)}\frac{\sum_{k=1}^{2}\lambda_{k-MHCP}P_{Tk}^{2/\alpha}( \beta_{k}/G_{k})^{-2/\alpha}}{\sum_{k=1}^{2}\lambda_{k-MHCP}P_{Tk}^{2/\alpha}} \frac{\lambda_{k-MHCP}P_{Tk}^{2/\alpha}(\beta_{k}/G_{k})^{-2/\alpha}}{\sum_{ k=1}^{2}\lambda_{k-MHCP}P_{Tk}^{2/\alpha}}=\frac{\pi}{C(\alpha)}\frac{\lambda_{k- MHCP}P_{Tk}^{2/\alpha}(\beta_{k}/G_{k})^{-2/\alpha}}{\sum_{k=1}^{2}\lambda_{k-MHCP}P_{Tk}^{2/ \alpha}}\end{array} \tag{16}\]According to (16) and (12), we can obtain the total downlink minimum achievable data rate of the MHCP-MHCP network as  
\[TP_{total}=\sum\limits_{k=1}^{2}A\lambda_{u}P_{C-k}C_{k}=\frac{AB\pi \lambda_{u}}{C(\alpha)}\sum\limits_{k=1}^{2}\frac{\lambda_{k-MHCP}P_{Tk}^{2/ \alpha}(\beta_{k}/G_{k})^{-2/\alpha}\log_{2}(1+\beta_{k})}{\left(\sum\limits_{ k=1}^{2}\lambda_{k-MHCP}P_{Tk}^{2/\alpha}\right)\left(1+1.28\frac{\lambda_{u}P_{Tk}^{2/ \alpha}(\beta_{k}/G_{k})^{-2/\alpha}}{\sum\limits_{k=1}^{2}\lambda_{k-MHCP}P_{ Tk}^{2/\alpha}(\beta_{k}/G_{k})^{-2/\alpha}}\right)} \tag{17}\]