esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Interval VIKOR Method for Estimating Steady-State Power Quality
It is worth pointing out that the weight of \(j\)th index is determined by the triangular fuzzy BW method in this paper. Step 5. Determine the comprehensive evaluation value of the data to be evaluated for every day. Based the value of \(S_{i}^{\pm}\) and the value of \(R_{i}^{\pm}\), the comprehensive evaluation value \(\left[Q_{i}^{-},Q_{i}^{+}\right]\) can be determined using Equations (36) and (37):  
\[Q_{i}^{-}=\
u\frac{\left(S_{i}^{-}-\min\limits_{i=1,2,\cdots,m}\left\{S_{i}^{ -}\right\}\right)}{\left(\max\limits_{i=1,2,\cdots,m}\left\{S_{i}^{+}\right\} -\min\limits_{i=1,2,\cdots,m}\left\{S_{i}^{-}\right\}\right)}+(1-\
u)\frac{ \left(R_{i}^{-}-\min\limits_{i=1,2,\cdots,m}\left\{R_{i}^{-}\right\}\right)}{ \left(\max\limits_{i=1,2,\cdots,m}\left\{R_{i}^{+}\right\}-\min\limits_{i=1,2,\cdots,m}\left\{R_{i}^{-}\right\}\right)} \tag{36}\]  
\[Q_{i}^{+}=\
u\frac{\left(S_{i}^{+}-\min\limits_{i=1,2,\cdots,m}\left\{S_{i}^{ -}\right\}\right)}{\left(\max\limits_{i=1,2,\cdots,m}\left\{S_{i}^{+}\right\} -\min\limits_{i=1,2,\cdots,m}\left\{S_{i}^{-}\right\}\right)}+(1-\
u)\frac{ \left(R_{i}^{+}-\min\limits_{i=1,2,\cdots,m}\left\{R_{i}^{-}\right\}\right)}{ \left(\max\limits_{i=1,2,\cdots,m}\left\{R_{i}^{+}\right\}-\min\limits_{i=1,2, \cdots,m}\left\{R_{i}^{-}\right\}\right)} \tag{37}\]  
where \(Q_{i}^{\pm}\) represents the comprehensive evaluation value of power quality on the \(i\)th day. The factor \(\
u\) could take a value from \(\left[0\quad 1\right]\) and it is assumed \(\
u=0.5\) in this study. Step 6. Calculate and rank the PPIs of power quality [31; 42]. Calculate the probability that the power quality of the \(i\)th day is better than the power quality of the \(j\)th day, according to Equation (38). \[p_{ij}=P\left[Q_{i}^{\pm}\geq Q_{j}^{\pm}\right]=\frac{1}{2}\left(\frac{\max \left\{0,Q_{i}^{+}-Q_{j}^{-}\right\}-\max\left\{0,Q_{i}^{-}-Q_{j}^{+}\right\} \right)}{Q_{i}^{+}-Q_{i}^{-}+Q_{j}^{+}-Q_{j}^{-}} \tag{38}\]It is worth pointing out that \(P\Big{(}Q_{i}^{\pm}\geq Q_{j}^{\pm}\Big{)}=0.5\) represents \(\Big{[}Q_{i}^{-}\quad Q_{i}^{+}\Big{]}=\Big{[}Q_{j}^{-}\quad Q_{j}^{+}\Big{]}\). We compare the \(Q_{i}^{\pm}\) of the power quality of each day to each other to obtain the probability value matrix, as shown in Equation (39). Obviously, \(p_{11}=p_{22}=\cdots=p_{mm}=0.5\). \[P= \begin{bmatrix}p_{11}&p_{12}&\cdots&p_{1m}\\ p_{21}&p_{22}&\cdots&p_{2m}\\ \vdots&\vdots&\ddots&\vdots\\ p_{m1}&p_{m2}&\cdots&p_{mm}\end{bmatrix}  \tag{39}\]  
Then, we calculate the PPIs of power quality by Equation (40) [42]. \[\mathrm{PPI}=\frac{1}{m}\Bigg{|}\sum_{j=1}^{m}p_{ij}+1-\frac{m}{2}\Bigg{|},i=1,2,\cdots,m \tag{40}\]  
Finally, we can obtain the daily power quality ranking by ranking their value of PPI. Since the power quality index is a reverse index, the smaller is the PPI value, the better is the power quality. When conducting a comprehensive assessment of power quality, by comparing the upper boundary values of each level given in Table 2 with the daily monitoring values, we can get the power quality level for each day.
# ARMONIA: A Unified Access and Metro Network Architecture
## 2 ARMONIA Architecture
### Description of ARMONIA Network
In combination with the central SDN controller, the architecture includes a network function virtualization orchestrator (NFV Orchestrator) for the installation and management of virtual network services. In order to ensure the optimal installation and the dynamic reconfiguration of these services, the NFV Orchestrator interconnects and cooperates with the SDN-based control plane, gaining also access to the status information of the overall network infrastructure. Through ARMONIA's innovative architecture, the network is able to intelligently monitor its state, correlate the monitoring information to gain insight into its inefficiencies, and use this knowledge to re-optimize its state. The monitoring plane contains a number of enhanced telemetry agents that specialize in monitoring specific network devices from all network segments. The central telemetry handler is placed at the root of the hierarchical telemetry tree and contains details for the overall infrastructure. The number of telemetry agents may vary depending on the size of the network, geographical features, and/or any other criteria. The enhanced telemetry agents collect, pre-process, and correlate telemetry information at the local level. The information is then aggregated and forwarded to the central telemetry handler where it can be used by the provisioning manager and by analytics and optimization algorithms to allocate more efficiently the available resources and trigger automatic re-optimization operations. ARMONIA's monitoring plane provides the following telemetry information: (a) physical layer monitoring data, including: transmitted/received optical signal power, optical spectrum, and pre-forward error correction (pre-FEC) bit error rate (BER) and optical signal-to-noise ratio (OSNR) at the receiver side; (b) packet layer monitoring data, including packet flows, jitter, delay; and (c) computation and storage resources monitoring data.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 6 Discussion and Analysis of Results
### System Performance Characteristics
#### 6.2.3 Limitations  
1. Frequency Range Constraints The experimental validation was conducted at laboratory-scale frequencies (12 kHz carrier, 180 Hz information signals) due to equipment limitations. While the theoretical framework scales to higher frequencies, practical implementation at OWC-relevant frequencies requires validation with appropriate high-frequency components and measurement systems.
2. Component Performance Dependencies The VCO-based implementation exhibits inherent sensitivity to component variations and environmental factors. Temperature stability, supply voltage fluctuations, and component aging can affect frequency accuracy and system performance. The noise analysis focused on additive noise but did not comprehensively address systematic errors introduced by component non-linearities.
3. Demodulation Phase Relationships The derivative-based approach inherently recovers the differentiated signal rather than the original information signal. While this characteristic was quantified through correlation analysis, practical applications may require additional signal processing to reconstruct the original waveform, potentially introducing complexity and processing delays.
4. Power Consumption Analysis Gap The current work does not provide quantitative power consumption analysis. While the circuit simplicity may suggest potential power efficiency advantages, comparative power measurements against alternative PPM generation techniques remain to be conducted.
# Influence Maximization with Priority in Online Social Networks
## 2 Model and Problem Definition
### Graph Notation and Independent Cascade Model  
Let \(G=(V,E)\) be a directed graph representing a social network with a node set \(V\) and a directed edge set \(E\), \(|V|=n\) and \(|E|=m\). Let \(N_{in}(v)\) and \(N_{out}(v)\) be two sets of in-neighbors and out-neighbor of a node \(v\), respectively. The notations of \(S\) and \(S^{*}\) represent to a seed set that is a solution and an optimal solution of IMP, respectively. We also note \(\mathsf{OPT}=\sigma(S^{*})\) is the influence of an optimal solution.  
In _Independent Cascade_ (IC) model, each edge \(e=(u,v)\in E\) has an influence probability \(p(u,v)\in(0,1)\) that represents the information transmission from \(u\) to \(v\). Each node \(v\in V\) hastwo possible states, _active_ and _inactive_. Given a seed set \(S\subseteq V\), the diffusion process from \(S\) happens in discrete steps \(t=0,1,\ldots\), as follow:  
* At step \(t=0\), all nodes in \(S\) is activated.
* At step \(t\geq 1\), for an activated node \(u\) in previous steps, it has a single chance to activate each inactive neighbour \(v\) with the successful probability \(p(u,v)\). An activated node remains _active_ till the end of the diffusion process.
* The propagation process ends when no more node is activated.  
[PERSON] et al. [1] show that IC model is equivalent to _live-edge_ model and estimating the quantity of influence nodes can be done as follows. We first generate a _sample graph_\(g\) from original graph \(G\) by selecting each edge \(e=(u,v)\in E\), independently, with probability \(p(u,v)\), and no select edge \((u,v)\) with probability \(1-p(u,v)\). The probability that a realization \(g\) can be generated from \(G\) (denoted as \(g\sim G\)) is  
\[\Pr[g\sim G]=\prod_{e\in E(g)}p(u,v)\prod_{e\in E\setminus E(g)}(1-p(u,v)) \tag{1}\]  
In this equation, \(E(g)\) is the set edge of \(g\). The number of sample graphs is \(2^{|E|}\). The influence spread of a seed set \(S\) in \(G\) is calculated as follows:  
\[\sigma(S)=\sum_{g\sim G}\Pr[g\sim G]|R(g,S)| \tag{2}\]  
where \(R(g,S)\) denotes the set of reachable nodes from \(S\) in \(g\). For a set of _priority_ nodes \(U\), the influence spread of \(S\) to \(U\) is calculated as follows:  
\[\sigma_{U}(S)=\sum_{g\sim G}\Pr[g\sim G]|R(g,S\to U)| \tag{3}\]  
where \(R_{g}(S\to P)\) denotes the set of nodes in \(U\) that can reach from \(S\) in \(g\). [PERSON] et al. [1] also show that, \(\sigma(\cdot)\) is a monotone and sub-modular function, i.e, for any \(A\subset V\), and \(v\
otin V\setminus B\), we have:  
\[\sigma(A+\{v\})\geq\sigma(A) \tag{4}\]  
and for any \(A\subseteq B\subset V\), and \(v\
otin V\setminus B\), we have:  
\[\sigma(A+\{v\})-\sigma(A)\geq\sigma(B+\{v\})-\sigma(B) \tag{5}\]  
We also easy to see that \(\sigma_{U}(\cdot)\) is a monotone and sub-modular function.
## 3 Results and Discussions
### The Effect of Different Strain Rate  
The compressive stress-strain curve is shown in Figure 11. The plateau pressure \(\sigma_{pl}\) is expressed as, where \(\varepsilon_{0}\) is the yield strain of the foam, and \(\varepsilon_{d}\) belongs to the densification strain.  
\[\sigma_{pl}=\frac{1}{\varepsilon_{d}-\varepsilon_{0}}\int_{\varepsilon_{0}}^{ \varepsilon_{d}}\sigma d\varepsilon \tag{5}\]  
The individual eigenvalues of aluminum foam at different strain rates were extracted and the results are shown in the Table 2 below:  
As shown in Figure 12, the mechanical properties of foam aluminum change significantly with the increase of strain rate: the yield stress, elastic gradient, and plateau stress all increase, while the yield strain and densification strain decrease. The reason for these changes is that under high strain rates, the pore structure of foam aluminum is affected by rapid loading, the stress concentration in pore walls and edges is more pronounced, internal defects (such as pores, cracks, etc.) do not have enough time to expand fully, dislocation movement is inhibited, dislocation density increases, thereby hindering further plastic deformation. At the same time, the deformation of the pore structure is restricted, the rigidity of the pore walls is enhanced, and the elastic modulus increases. In terms of micro-mechanisms, under high strain rates, the deformation rate of the pore structure  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Strain** & **Yield** & **Elastic** & **Densification** & **Platform** & **Platform** \\
**Rate/s\({}^{-1}\)** & **Stress** & **Modulus** & **Strain** & **Modulus** & **Stress (\(\sigma\))** \\ \hline
0.001 & 4.142 & 11.477 & 0.693 & 10.12 & 5.739 \\
0.01 & 3.87 & 17.750 & 0.675 & 13.00 & 6.1 \\
0.1 & 5.25 & 15.130 & 0.64 & 11.62  
accelerates, the stress transfer within the material is faster, the contact between pore walls and edges is tighter, and the material can reach a dense state at a lower strain.
# Vitamin C in Plants: From Functions to Biofortification
## 6 Vitamin C Biofortification
### Manipulation of Other Biosynthetic Pathways  
The overexpression of genes of the alternative biosynthetic pathways have also given good results in terms of vitamin C content in different crops. Regarding the gulose pathway, positive results have been obtained with the expression of rat cDNA encoding GuLO, the enzyme involved in the final step of the animal vitamin C biosynthetic pathway [163; 210; 211]. Lettuce and tobacco plants constitutively expressing this gene showed four- and seven-fold increases in vitamin C levels, respectively [163]. Transgenic potato plants, overexpressing the same gene, show improved vitamin C accumulation in tubers and increased tolerance to several abiotic stresses [210]. In the same way, _Arabidopsis_ lines overexpressing this GuLO contained high vitamin C contents and exhibited improved growth and enhanced biomass of shoots and roots, as well as higher tolerance to diverse abiotic stresses [211].  
Interesting results have also been obtained by manipulating the galacturonate pathway. Overexpression of the strawberry FaGalUR led to a two-fold increase in vitamin C content in potato, and this enhancement allowed for an increase in tolerance to abiotic stresses in the transgenic lines [212]. Similar positive results have been reported for tomato, where, although there was a moderate increase in vitamin C content, an increase in total antioxidants occurred that was linked to redox state regulation [213]; moreover, tomato plants overexpressing FaGalUR were found to be more tolerant to abiotic stresses [214]. Interestingly, in the tomato introgression line IL12-4-SL, the genes encoding for pectin methylesterase, polygalacturonase, and UDP-d-glucuron-acid-4-epiomerase, which are involved in pectin degradation, have been identified as candidate genes for a QTL associated with high vitamin C content, suggesting that marker-assisted selection could be a good strategy to enhance vitamin C accumulation [215].
# Victim Detection and Localization in Emergencies
## 3 Overview of Location Technologies
### WiFi
#### 3.3.2 WiFi Fine Time Measurement  
More recently, the IEEE 802.11 mc variant added a new Fine Time Measurement (FTM) functionality. FTM includes timestamped packets and calculates distances to the User Equipment (UE) accurately with the Round-Trip Time (RTT) protocol [12]. This may be very useful for emergency cases because the number of smartphones that include support for this protocol is increasing [9]. The distance calculation to every router that supports the FTM and RTT protocol is computed in the UE for privacy preservation, even if the UE is not connected to the router.
# Comparing End-to-End Machine Learning Methods for Spectra Classification
## 4 Experiments and Results
### Performance Metric  
In this work, our goal is to find the phase transition point, which also means classifying the spectra into 2 phases or classes during the experiment [12]. As there is no ground-truth phase transition information, we are interested in whether there is a clear classification boundary or an ambiguity zone during the experiment when the classification jumps inconsistently between the phases [12]. The classification confidence is used as the performance metric, which was defined in our previous work [12]. It can indicate how small the ambiguous zone is during the experiment. The classification confidence is defined as \(P_{conf}=1-\frac{N_{f}}{N_{t}}\), where \(N_{f}\) represents the number of spectral curves in the classification boundary or ambiguous region with the class label jump, \(N_{t}\) represents the number of all the tested spectral curves. The classification boundary can be represented by \(\left(C^{0}_{upper},C^{1}_{lower}\right)\), where \(C^{0}_{upper}\) represents the upper bound or the last spectra of continuous class 0, and \(C^{0}_{upper}\) represents the lower bound or the first spectra of continuous class 1. Then \(N_{f}\) can be calculated by \(N_{f}=C^{1}_{lower}-C^{0}_{upper}-1\). Therefore, a clear boundary between these two classes of spectra yields 100% confidence, which would otherwise be less than 100%. Particularly, if no phase transition or boundary between two classes is detected, all the spectral curves are in the ambiguous region, and the classification confidence is 0.
# Averting BER Floor with Iterative Source and Channel Decoding for Layered Steered Space-Time Codes
###### Abstract  
The widespread development in wireless technologies and the advancements in multimedia communication have brought about a positive impact on the performance of wireless transceivers. We investigate the performance of our three-stage turbo detected system using state-of-the-art high efficiency video coding (HEVC), also known as the H.265 video standard. The system makes use of sphere packing (SP) modulation with the combinational gain technique of layered steered space-time code (LSSTC). The proposed three-stage system is simulated for the correlated Rayleigh fading channel and the bit-error rate (BER) curve obtained after simulation is free of any floor formation. The system employs low complexity source-bit coding (SBC) for protecting the H.265 coded stream. An intermediate recursive unity-rate code (URC) with an infinite impulse response is employed as an inner precoder. More specifically, the URC assists in the prevention of the BER floor by distributing the information across the decoders. There is an observable gain in the BER and peak signal-to-noise ratio (PSNR) performances with the increasing value of minimum Hamming distance (\(d_{H,\text{min}}\)) using the three-stage system. Convergence analysis of the proposed system is investigated through an extrinsic information transfer (EXIT) chart. Our proposed system demonstrates better performance of about 22 dB than the benchmarker utilizing LSSTC-SP for iterative source-channel detection, but without exploiting the optimized SBC schemes.  
antenna arrays; BER floor; EXIT analysis; high efficiency video coding; iterative decoding; layered steered space-time codes +
Footnote †: journal:  
0021  
1 Department of Computer Systems Engineering, University of Engineering and Technology Peshawar, Peshawar 25000, Pakistan, [EMAIL_ADDRESS] (N.M.);  
[EMAIL_ADDRESS] (K.S.K.);  
2 National Center in Big Data and Cloud Computing, University of Engineering and Technology Peshawar (NCBC-UETP), Peshawar 25000, Pakistan 2  
Footnote 2: [[https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors)]([https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors))
## 4 Applying Noise to Encrypted Images
### The Additive and Multiplicative Noises  
A high-level description of additive and multiplicative noise is given below. Similarly to the other noises, \(n\) points of the encrypted image are chosen randomly. Furthermore, each point has an associated frequency \(f_{c}(x,y)\) such that \(0\leq f_{c}(x,y)\leq 255\) and \(c\) indicates the basic color.  
For the additive noise, an integer \(\tau_{c}(x,y)\) is chosen randomly for each point and color. The discrete value \(f^{\prime}_{dc}(x,y)\) is calculated according to Equation (43); subsequently, \(f_{c}(x,y)\) is changed by the discrete value of \(f^{\prime}_{dc}(x,y)\).  
\[f^{\prime}_{dc}(x,y)=\left[f_{c}(x,y)+\tau_{c}(x,y)\right]\ \ \ \mathrm{mod}\ 256 \tag{43}\]  
To apply the multiplicative noise in the same way as additive noise, an integer \(\tau_{c}(x,y)\) is selected randomly. Therefore, the frequency \(f^{\prime}_{dc}(x,y)\) is calculated, taking into account Equation (44). Later, the value of \(f_{c}(x,y)\) is replaced by \(f^{\prime}_{c}(x,y)\).  
\[f^{\prime}_{dc}(x,y)=\left[f_{c}(x,y)\times\tau_{c}(x,y)\right]\ \ \ \mathrm{mod}\ 256 \tag{44}\]
## 2 Materials and Methods
### Models and Methods
#### 2.3.3 Ensemble Learning  
Combining multiple models in machine learning, known as ensemble learning [37], can significantly enhance predictive accuracy and robustness. This technique is especially effective when applied to U-Net and cGAN architectures. One method for implementing ensemble learning with U-Net/cGAN involves training various instances of the same architecture using different hyperparameters or random seeds. By averaging the predictions of these models, errors from any individual model can be minimized, leading to improved overall accuracy. Another approach explored in this paper involves combining different U-Net and cGAN generator architectures in an ensemble. An instance of an ensemble could comprise a conventional U-Net paired with a customized variation featuring supplementary skip connections. Alternatively, another ensemble may increase the number of filters in the convolutional layers of a standard U-Net and cGAN generator architectures by 32 channels. While this approach may improve feature extraction and result in more resilient predictions, it would also augment the model's parameter count and necessitate additional computational resources during training and inference. By capitalizing on the unique strengths of diverse U-Net and cGAN architectures through ensemble learning, it is frequently feasible to attain superior outcomes compared to solitary architecture.  
This study employed two ensemble strategies for both U-Net and cGAN. The first strategy involved using the same U-Net architecture but with two different dropout rates in the layers 0.2 and 0.35. After training the models with these rates, we averaged their results. This method was similarly applied to the cGAN generator architecture. The second strategy involved utilizing two different U-Net architectures, one with 10 layers and the other with 12 layers, as previously mentioned. We added a 32-channel layer at both the beginning and end of the network. Each model was trained with three dropout rates: \(-0.2\), 0.3, and 0.35. In total, we trained 6 models using this approach. This same methodology was also applied to the cGAN architecture.