esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Magnification Based on Bicubic Approximation with Edge as Constraint
###### Abstract  
Image magnification can be reduced to construct an approximation surface with data points in the image while keeping image details and edge features. In this paper, a new image magnification method is proposed by constructing piecewise bicubic polynomial surfaces constrained by edge features. The main innovation includes three parts. First, on the small adjacent area of each pixel, the new method constructs a quadratic polynomial sampling patch to approximate pixels on the small neighborhood with edge features as constraints. All quadric polynomial sampling patches are weighted to generate piecewise whole bicubic polynomial sampling surface. Second, a technique for calculating the error image is proposed: the error image is used to construct a correction surface to improve the accuracy and visual effect of the magnified image. Finally, in order to improve the accuracy of the approximation surface, a technology of balancing polynomial coefficients is put forward. Experimental results show that, compared with other methods, the proposed method makes better use of the local feature information of the image, which not only improves the PSNR/SSIM numerical accuracy of the magnified image but also improves the visual effect of the magnified image.  
image magnification; medical image aided diagnosis; edge feature constraints; quadratic polynomial surface +
Footnote †: journal: Medical Research, McGill University, Montreal, QC H3S 1Y9, Canada
## 2 Methodologies
### Lstm-Scn  
The LSTM-SCN model proposed in this paper consists of two main components: the LSTM, which acts as a feature extractor, and the SCN, which performs load forecasting using these features. The structure of the model is illustrated in Figure 4.  
Data are preprocessed before being input into the model. Missing values, outliers, and noise in the data can affect the accuracy of the results, necessitating preprocessing operations. Additionally, data normalization can improve the performance of the LSTM model and prevent issues with vanishing or exploding gradients.  
The proposed model employs an LSTM network for feature extraction. The internal architecture of the LSTM network consists of two layers [27]: the first layer contains 1024 units, and the second layer contains 256 units. To mitigate overfitting, dropout is applied between the two LSTM layers. The output from the second layer, representing the extracted features, serves as the final output of the model.  
This paper dispenses with the fully connected layers commonly used in most models, opting instead for an innovative approach that employs the SCN for forecasting. In traditional models that use fully connected layers for forecasting, it is necessary to frequently set and adjust the number of hidden layers. The SCN allows for a relatively large number of hidden layer nodes, enabling the model to autonomously identify the optimal network structure within the permissible range of node configurations. This process not only consumes time but also requires significant computational resources. In contrast, the SCN structure is determined by setting a maximum number of hidden nodes and a tolerance level. The number of hidden nodes in the SCN will incrementally increase until the specified tolerance is achieved or the maximum node limit is reached. The SCN is set with the following parameters: the maximum number of hidden layers \(L_{max}=200\), weights scale sequence Lambdas = [0.5, 1, 5, 10, 30, 50, 100, 150, 200, 250], training tolerance  
Figure 3: Cell structure of the LSTM model.  
\(\varepsilon=0.001\), contractive sequence r = [0.9, 0.99, 0.999, 0.9999, 0.9999, 0.99999, 0.999999], and maximum number of candidate nodes \(T_{max}=100\).
# Predicting Online Item-Choice Behavior: A Shape-Restricted Regression Approach
## 4 PV Sequence Model
### Operations Based on Recency and Frequency  
From the perspective of frequency, it is reasonable to assume that item-choice probability increases as the number of PVs in a particular period increases. To formulate this, we define the following operation:  
**Definition 1** (Up).: _On the domain_  
\[\mathcal{D}_{\mathbb{U}}:=\{(\mathbf{v},s)\in\Gamma\times[1,n]\mid v_{s}\leq m-1\},\]  
_the function \(\mathbb{U}_{\mathbb{P}}:\mathcal{D}_{\mathbb{U}}\to\Gamma\) is defined as_  
\[((\ldots,v_{s},\ldots),s)\mapsto(\ldots,v_{s}+1,\ldots).\]  
For instance, we have \(\mathbb{U}_{\mathbb{P}}((0,1,1),1)=(1,1,1)\), and \(\mathbb{U}_{\mathbb{P}}((1,1,1),2)=(1,2,1)\). Since this operation increases PV frequencies, the monotonicity constraint \(x_{(0,1,1)}\leq x_{(1,1,1)}\leq x_{(1,2,1)}\) should be satisfied by item-choice probabilities.  
From the perspective of recency, we assume that more-recent PVs have a larger effect on increasing item-choice probability. To formulate this, we consider the following operation for moving one PV from an old period to a new period:  
**Definition 2** (Move).: _On the domain_  
\[\mathcal{D}_{\mathbb{M}}:=\{(\mathbf{v},s,t)\in\Gamma\times[1,n]\times[1,n]\mid v _{s}\leq m-1,\,v_{t}\geq 1,\,s<t\},\]  
_the function \(\texttt{Move}:\mathcal{D}_{\mathbb{M}}\to\Gamma\) is defined as_  
\[((\ldots,v_{s},\ldots,v_{t},\ldots),s,t)\mapsto(\ldots,v_{s}+1,\ldots,v_{t}-1,\ldots).\]For instance, we have \(\texttt{Move}((1,1,1),2,3)=(1,2,0)\), and \(\texttt{Move}((1,2,0),1,2)=(2,1,0)\). Because this operation increases the number of recent PVs, item-choice probabilities should satisfy the monotonicity constraint \(x_{(1,1,1)}\leq x_{(1,2,0)}\leq x_{(2,1,0)}\).  
The PV sequence \(\mathbf{v}=(1,1,1)\) represents a user's continued interest in a certain item over three periods. In contrast, the PV sequence \(\mathbf{v}=(1,2,0)\) implies that a user's interest decreased over the two most-recent periods. In this sense, the monotonicity constraint \(x_{(1,1,1)}\leq x_{(1,2,0)}\) may not be validated. Accordingly, we define the following alternative operation, which exchanges numbers of PVs to increase the number of recent PVs:  
**Definition 3** (Swap).: _On the domain_  
\[\mathcal{D}_{\mathbb{S}}:=\{(\mathbf{v},s,t)\in\Gamma\times[1,n]\times[1,n]\mid v _{s}<v_{t},\,s<t\},\]  
_the function \(\texttt{Swap}:\mathcal{D}_{\mathbb{S}}\to\Gamma\) is defined as_  
\[((\ldots,v_{s},\ldots,v_{t},\ldots),s,t)\mapsto(\ldots,v_{t},\ldots,v_{s}, \ldots).\]  
We thus have \(\texttt{Swap}((1,0,2),2,3)=(1,2,0)\) because \(v_{2}<v_{3}\), and \(\texttt{Swap}((1,2,0),1,2)=(2,1,0)\) because \(v_{1}<v_{2}\). Since this operation increases the number of recent PVs, item-choice probabilities should satisfy the monotonicity constraint \(x_{(1,0,2)}\leq x_{(1,2,0)}\leq x_{(2,1,0)}\). Note that the monotonicity constraint \(x_{(1,1,1)}\leq x_{(1,2,0)}\) is not implied by this operation.
## 5 Experiments and Results
### Key-Based Security
#### 5.3.1 Key-Based Additive Strategy  
Using the additive strategy, the whole audio set was watermarked utilizing a key, \(k_{1}\). Then, data was extracted utilizing a key \(k_{2}\) with \(k_{1}\
eq k_{2}\). As detailed above, the \(\alpha\) value was set to \(\frac{1}{8}\).  
Figure 11 shows the BER results and the BER distribution for the additive strategy.  
The BER mean, median, and variance were calculated as 0.4591, 0.4582, and 0.0025, respectively. Both the mean and median are not equal to 0.5.
## 2 Molecular Modulation  
The technique of molecular modulation differs from traditional stimulated Raman scattering [32; 33; 34; 35; 36; 37] in that it relies on Raman generation in the regime of near maximal coherence. Figure 1a shows a simplified energy level diagram for a typical molecular system (for example, D\({}_{2}\)). Here state \(|a\rangle\) is the ground rotational-vibrational state of the molecule and state \(|b\rangle\) is a particular excited rotational-vibrational state. The two excitation lasers (the pump and the Stokes) drive the coherence (off-diagonal density matrix element \(\rho_{ab}\)) of the Raman transition. When the driving lasers are sufficiently intense, it becomes possible to coherently transfer almost half of the population to the excited state, thus approaching the state of maximum coherence, \(|\rho_{ab}|\approx 1/2\). The concept of maximum coherence has gained considerable attention over the last two decades. It is now understood that for nonlinear mixing processes, the generation efficiency is maximized for a maximally coherent state [38; 39; 40; 41]. Furthermore, because the generation process is almost complete in a single coherence length, phase-matching is unimportant and new beams are generated collinearly.  
When the molecules are driven to a highly coherent state, they act as an efficient frequency mixer with the modulation frequency determined by the oscillation frequency of the molecules, \(\
u_{M}=\
u_{pump}-\
u_{Stokes}\). For linearly polarized driving laser beams, the driving lasers themselves can be modulated to produce higher-order Stokes and anti-Stokes sidebands through four-wave mixing. In addition to the driving lasers, the system can also be used to modulate any other laser beam. As shown in Figure 1, a separate laser (called the mixing beam) with frequency \(\
u_{0}\) can be modulated to produce frequency upshifted (anti-Stokes) and downshifted (Stokes) sidebands at frequencies \(\
u_{0}+\
u_{M}\) and \(\
u_{0}-\
u_{M}\), respectively. If the process is highly efficient or a multi-pass configuration is used, a very broad Raman spectrum can be produced with frequencies of the form \(\
u_{0}+q\
u_{M}\) (\(q\) is an integer). Using the molecular modulation technique with pulsed lasers, several groups have demonstrated the generation of a wide spectrum in molecular H\({}_{2}\) and D\({}_{2}\)[14; 15; 16; 17] and have synthesized the first optical pulses to break the single-cycle barrier [18; 19; 20]. These experiments currently hold the world record for the shortest pulses ever synthesized in the optical region of the spectrum. When the driving laser beams are opposite circularly polarized, as is the case in Figure 1a, the generation of sidebands from the driving lasers themselves is suppressed due to angular momentum conservation rules [42]. In this regime, a frequency upshifted or downshifted sideband from an appropriately-polarized mixing beam will be produced, and the molecular medium can be used as an efficient frequency converter. Further details regarding the molecular modulation technique can be found in a number of review articles [43; 44; 45].
## 3 SDN Performance Challenges
### Communication Overhead  
DevoFlow [9] and DIFANE [12] are the state-of-the-art works to reduce the overhead of the controller by reducing the high volume of messages exchanged between the switch to the controller. DEVOFLOW aimed to reduce the communication overhead, switch--controller using fine-grained wildcards to improve the network-wide visibility of the controller. The mechanism leverages on rule cloning, thereby delegating some of the control function to switches to act locally in a way that the controller preserves the central network intelligence. DEVOFLOW modified the action field with CLONE flag, flag clear value signifies normal forwarding otherwise locally clone wildcard rule [32]. The cloning helps to process short live flows such as mice flow without the need to consult controller [3], hence, reduced the controller overhead. DIFANE presents a new architectural scalability solution built on the OpenFlow switch. The scheme maintains all traffic in the data plane by selectively distributing packets through intermediary switch called authority switches. These intermediary switches handle entry table-misses at edge switches, hence, reduce the need to invoke messages to controller. However, DIFANE and DEVOFLOW may require more complex software and hardware than conventional OpenFlow switches, which increases the cost of the device. Moreover, the fundamental principle of OpenFlow is to delegate all intelligence functions and control to the OpenFlow controller, therefore, such schemes violate this principle [33]. Conversely, [PERSON] et al. [32] introduced a blackhole mechanism to reduce switch to controller overhead while maintaining the benefit of the visibility for every new flow. Packet processing architecture is modified where only the first packet is forwarded to the controller while subsequent table-miss will be handled locally. In this way, the number of events sent to the controller is reduced. Although the use of blackhole has reduced controller consultation, it inherits weakness by its architectural design to drop notified packet, which may significantly result in a large number of packet losses.  
As part of the effort to further reduce the workload on the controller and improve the efficiency of the data plane, the work of [PERSON] et al. [13] reduced controller overhead through packet filtering technique. The mechanism inspects header information aimed at reducing multiple packet-in messages forwarded to the controller. This is achieved by recording the information about packet-in messages forwarded to the controller. It filters duplicate information forwarded via packet-in while switches are to drop packets that bring overhead to the controller. The work of [PERSON] [34] proposed control plane protection mainly to protect the controller from the TCP SYN flooding attack by an SYN cookies approach [13]. However, the work of [13; 32; 34] may also not cope with the nature of flows in a dynamic large-scale network.
# Machine Learning and Deep Learning Models Applied to Photovoltaic Production Forecasting
## 2 Application and Assessment of Models
### Data Preparation  
Data preparation is a crucial stage to obtain an ensemble of information suitable for the Artificial Intelligence methods that are intended to be used in this paper. Real data measured through the sensors is usually incomplete or has multiple errors in the measurement stage because of system failures [41]. The accuracy of Artificial Intelligence methods increases if several conditions are met. Therefore, this section deals with three steps, preprocessing, scaling, and feature engineering.  
Data cleaning and organization of data is carried out in preprocessing. Duplicate and outlier data are detected at this this stage. Brief periods of inconsistencies are corrected using data scrubbing techniques [42]. The set of independent variables is selected considering the relationship between each of them and the objective variable. These must contain a high-quality data, e.g., with a validity of at the least of 95%. A characteristic of Artificial Intelligence models with respect to statistics is that they allow the incorporation of missing values, to make the forecast. Thus, wrong data are retained to ensure data continuity and improve forecast reliability [43].  
In general, without consistency in the data, it is not possible to obtain high performance results. The application of scaling is basic to establish data in a similar range. In this way, Artificial Intelligence methods can better match the importance of each variable through the computation of weights to obtain models with better convergence and precision. Furthermore, with the application of scaling, versatility is granted to the models made, allowing them to be used in other study cases [44; 45]. In this manuscript different typesof scaling are evaluated, such as normalization in the range [0, 1], and the division by the maximum and mean of the series.  
Finally, data mining techniques have been applied to aggregate features with potential to forecast the goal variable through domain knowledge and the use of mathematics and statistics. Obtaining this new set of variables allows a better adjustment to the problem due to their inherent relationship with the target variable [46; 47]. Temporal variables have been extracted by means of feature engineering techniques. Sine and cosine functions have been applied to these new variables since the improvement in the efficiency of the model has been verified.
## 2 Materials and Methods
### Sampling and Experimental Analysis  
Owing to the fact that about 30% of the monitoring results may be biased due to inappropriate sampling and sample transportation [27], all samples were collected with great care to eliminate potential errors. Water samples were collected in accordance with the procedure outlined in EN ISO 5667-3 [28] and ISO 5667-11 [29]. Before sampling, the top water in each piezometer was carefully pumped out. Calculations of the pumping time were based on the volume of water in each piezometer and the pump efficiency. To assure sampling appropriateness, each pumping was accompanied with simultaneous measurements of pH, temperature and electrical conductivity. Groundwater samples were taken four times in each year (March, June, September and November).  
Chemical analysis of COD, BOD and TOC were conducted according to PN-ISO 15705:2005P [30], PN-EN 1899-2:2002P [31] and PN-EN 1484:1999P [32], respectively. Additionally, the values of pH and EC were measured in accordance with the recommendations presented in PN-EN ISO 10523:2012 [33] and PN-EN 27888:1999 [34].  
Analysis of changes of the pollution indicators was carried out in a temporal and spatial aspect. Graphs of BOD, COD and TOC concentrations were presented for piezometers located in the landfill area, composting plant, and the adjacent service facilities. The obtained results were compared with the groundwater quality standard presented in the Regulation of the Minister of the Environment of Poland [35] (third class representing good groundwater quality).
# Improved Chaff-Based CMIX for Solving Location Privacy Issues in VANETs
## 5 Our Improved Chaff-Based CMIX Scheme
### Mutual Authentication  
The authentication between \(RSUi\)s and vehicles starts once the vehicles are in the transmission range of the \(RSUi\)s. In the following, we describe the protocol between \(RSUi\) and vehicles.  
1. \(RSUi\) broadcasts \(\mathcal{B}\)=\((M_{R2V},Sign_{RSUi}(M_{R2V}),\)\(Cert_{RSUi_{l}})\), where \(M_{R2V}=(PP_{i},ACF_{i},\)\(R_{i},E_{i},t_{i})\), where \(R_{i}\) along \(t_{i}\) is the timestamp and \(E_{i}\) is used to generate symmetric keys and \(ACF_{i}\) is the ACF of \(RSUi_{l}\).
2. A vehicle \(VID_{j}\) entering the transmission range receives \(\mathcal{B}\) and validates the signature \(Sign_{RSUi_{l}}(M_{R2V})\). If not verified, it aborts the protocol. Otherwise, \(VID_{j}\) stores \(\mathcal{B}\).
3. \(VID_{j}\) next computes 1. \(P_{j}=R_{j}^{a_{j}}\) and \(P_{i}=R_{i}^{a_{j}}\). 2. \(F_{j}=g_{2}^{f_{j}}\) and \(K=H_{2}(E_{i}^{f_{j}})\), where \(f_{j}\in_{R}\mathbb{Z}_{q}^{*}\).  
\(C_{j}=Enc_{K}(M_{VR},Sign_{VID_{j}}(M_{VR}),\) \(Cert_{VID_{j}},F_{j},t_{j})\), where \(M_{VR}=(P_{j},P_{i},H_{3}(Cert_{RSU_{i}},P_{j},P_{i},F_{j},t_{i},ACF_{j})\) and sends \(C\) to \(RSU_{i}\).
4. \(RSU_{i}\) now computes the symmetric key \(K=H_{2}(F_{j}^{e_{i}})\) and decrypts \(C\). Next, \(RSU_{i}\) 1. First validates the signature \(Sign_{VID_{j}}(M_{VR})\), and aborts if it is not valid. 2. Aborts the protocol if \(H_{VR}^{\prime}\
eq H_{3}(Cert_{RSU_{i}},P_{j}^{\prime},P_{i}^{\prime},F_{j},t_ {i})\), where \(M_{VR}^{\prime}=(P_{j}^{\prime},P_{i}^{\prime},H_{VR}^{\prime})\). 3. Verifies \(ACF_{i}\) and aborts if it is not valid. 4. Searches for a tuple \(\{A^{\prime},P^{\prime},T^{\prime}\}\) in \(List_{pub}\), where \(P^{\prime}=P_{j}^{\prime}\). 5. Aborts the validation if the tuple is expired or it is not in \(List_{pub}\), or it has more than one tuple. 6. Computes \(P_{i}^{\prime\prime}=(A^{\prime})^{r_{i}}\). 7. If \(P_{i}^{\prime\prime}=P_{i}^{\prime}\) then \(VID_{j}\) will be authenticated to \(RSU_{i}\) without revealing its identity.
## 3 Results
### VOC Emissions  
The VOC emissions showed a tendency to rise in _M. piperita_ plants inoculated with WCS417r and to decrease under drought stress (Figure 2). Inoculation with WCS417r induced a 45% increase in the emissions in relation to the control plants (\(p\) < 0.05). In the particular case of the MS plants, although the emission of VOCs decreased by 15%, there was no statistical difference with the plants with regular irrigation (\(p\) > 0.05), and in the case of SS, the decrease in the emission of monoterenes represented 50% less than control (\(p\) < 0.05) (Figure 2). The inoculation of plants subjected to water stress did not reveal any difference with the controls (MS or SS) (\(p\) > 0.05).  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Treatment** & **Limonene** & **Menthone** & **Menthofurane** & **Menthol** & **Pulegone** \\ \hline Regularly watered & 0.57 \(\pm\) 0.10 a & 0.48 \(\pm\) 0.09 a & 0.33 \(\pm\) 0.09 a & 0.41 \(\pm\) 0.10 a & 3.89 \(\pm\) 0.39 a \\ GB03 & 1.17 \(\pm\) 0.13 a & 1.83 \(\pm\) 0.28 b & 0.65 \(\pm\) 0.19 a & 2.40 \(\pm\) 0.66 b & 12.51 \(\pm\) 2.26 b \\ WCS417r & 1.35 \(\pm\) 0.36 a & 2.01 \(\pm\) 0.46 b & 0.67 \(\pm\) 0.29 a & 1.85 \(\pm\) 0.17 b & 18.93 \(\pm\) 2.53 b \\ MS & & & & & \\ Non-inoculated & 1.45 \(\pm\) 0.27 a & 1.64 \(\pm\) 0.22 b & 0.75 \(\pm\) 0.24 a & 1.03 \(\pm\) 0.46 ab & 12.36 \(\pm\) 1.52 b \\ GB03 & 1.79 \(\pm\) 0.42 a & 1.99 \(\pm\) 0.41 b & 0.35 \(\pm\) 0.12 a & 2.67 \(\pm\) 0.40 c & 17.74 \(\pm\) 4.44 b \\ WCS417r & 1.02 \(\pm\) 0.22 a & 2.12 \(\pm\) 0.27 b & 0.43 \(\pm\) 0.14 a & 2.34 \(\pm\) 1.06 b & 13.01 \(\pm\) 2.50 b \\ SS & & & & & \\ Non-inoculated & 1.53 \(\pm\) 0.41 a & 1.49 \(\pm\) 0.30 b & 0.74 \(\pm\) 0.29 a & 1.12 \(\pm\) 0.22 abc & 13.31 \(\pm\) 2.46 b \\ GB03 & 0.92 \(\pm\) 0.19 a & 1.53 \(\pm\) 0.25 b & 0.38 \(\pm\) 0.12 a & 1.62 \(\pm\) 0.36 abc & 11.76 \(\pm\) 3.64 b \\ WCS417r & 1.31 \(\pm\) 0.26 a & 1.55 \(\pm\) 0.24 b & 0.36 \(\pm\) 0.07 a & 1.47 \(\pm\) 0.58 abc & 10.48 \(\pm\) 1.71 b \\ \hline \end{tabular}
\end{table}
Table 2: Concentrations of major Essential Oil (EO) components (\(\mu\)g/g fresh weight) in _Mentha piperita_ plants grown under drought stress (MS: moderate stress, SS: severe stress) and inoculated with different plant growth promoting rizobacteria (PGPR) strains (mean \(\pm\) SE). Means followed by the same letter in a given column are not significantly different according to [PERSON]’s LSD test (n = 15–20, \(p\) < 0.05).  
Figure 1: Essential oil yield in _M. piperita_ plants grown under drought stress (MS: moderate stress, SS: severe stress) and inoculated with different Plant Growth Promoting Rhizobacteria (PGPR) strains (mean \(\pm\) SE). Letters above bars indicate significant differences according to [PERSON]’s LSD test (n = 15–20, \(p\) < 0.05).