esa-sceva/satcom-chunk-collection huggingface dataset:
# Detecting Jamming in Smart Grid Communications via Deep Learning
## 5 Theoretical Framework and Methodology
### Theoretical Framework
This phase is not mandatory, but it has been demonstrated that it helps to mitigate the impact of noise, thereby helping the classifier to isolate and identify consistent patterns, i.e., the features of the jammer [11; 56]. Subsequently, we treat the jamming detection  
Figure 6: Block diagram of the PLC wiretap channel model. problem as an image classification problem: depending on the scenario, the classifier is challenged to classify the image as consisting of jammed samples (Scenario 1) or as an anomaly with respect to a baseline composed of non-jammed samples (Scenario 2). **Generation of Images. ** A baseline solution to detect the presence of a jammer might involve the use of \(I-Q\) samples (as they are) as the input of the classifier. This methodology has been proven to be prone to (not negligible) misclassifications in the context of wireless communications [61] due to the presence of the physiological noise already present in the communication link, which hides the features of the phenomenon to be detected. In the following, we resort to a pre-processing technique, which turned out to be effective in mitigating the impact of the noise, thus being able to maximize the performance of the classifier. Such a technique involves translating \(I-Q\) samples into images while resorting to the computation of the bi-variate histogram and mapping the output into an image. Figure 7 provides details on the pre-processing stage of our solution and, in particular, how \(I-Q\) samples are translated into images. The dataset is created using the reference model discussed in Section 5.1. The \(I-Q\) samples are generated by simulating the physical layer of a PLC channel and then translating the samples acquired from such a channel into images. Our approach is in line with some other pre-processing techniques of \(I-Q\)-samples [40; 56; 61]. However, such techniques have never been applied to wired channels, and not even in (very noisy) PLC networks. We conducted an empirical study to choose the number of \(I-Q\) samples required to create a bi-variate histogram \(h\). Each value of the bi-variate histogram should be \(\leq\)\(3*256-1=767\), and therefore 634,880 samples are required for each image. We also consider that some \(I-Q\) samples fall away from the ideal positions \(b=0\rightarrow\{Q=0,I=1\}\) and \(b=1\rightarrow\{Q=0,I=-1\}\) due to the noise of the communication link. We filter out such samples by taking the 95 quantile of the \(I\) component and the 95 quantile of the \(Q\) component. We computed the minimum and maximum values of the \(I\) and \(Q\) components and used them as the range for the x-axis and y-axis, respectively. Then, we find the
## Materials and Methods
### Statistical Analysis and Feature Selection  
One-way ANOVA was performed to statistically determine the difference between the groups for all extracted features. A Tukey post hoc test was also performed for multiple comparisons between groups for each extracted feature. Statistical significance (\(\alpha\)) was set at 0.05. All statistical analysis procedures were performed by using SPSS Version 24.0 (IBM Corp, Armonk, NY, USA). A post hoc power analysis was performed on statistically different features determined from one-way ANOVA [24]. The power analysis was performed with G*Power software (ver. 3.1.9.2; Heinrich-Heine-Universitat Dusseldorf) [24].  
Two stages of feature selection were performed in this study. After obtaining the statistically different features from the post hoc test results, the second stage of feature selection was then performed by calculating the signal-to-noise ratio (\(SNR\)) [25]. The \(SNR\) is used to rank features and define how well the feature can discriminate between two classes. The \(SNR\) can be calculated by using the following formula:  
\[SNR=\frac{\left(\mu_{\textit{class 1}}-\mu_{\textit{class 2}}\right)}{\left( \sigma_{\textit{class 1}}+\sigma_{\textit{class 2}}\right)} \tag{1}\]  
where \(\mu_{\textit{class 1}}\) and \(\mu_{\textit{class 2}}\) are the mean value of features of classes 1 and 2, respectively; \(\sigma_{\textit{class 1}}\) and \(\sigma_{\textit{class 2}}\) are the standard deviation of the features of classes 1 and 2, respectively. After the calculation of the \(SNR\) and ranking of all the features, four features with the highest \(SNR\) score were selected to be used as features for the classification model.
# TARS: A Novel Mechanism for Truly Autonomous Resource Selection in LTE-V2V Mode 4
## 4 System Model Assumptions and Proposed Mechanism
### TARS Mechanism
#### 4.4.1 RB Selection  
The purpose of RB selection in TARS is to ensure that neighbor vehicles select unique RBs to avoid resource collision. To control resource collisions effectively, the neighbors' RB information is locally available on every vehicle, and there is a local mechanism to predict the selected RBs by the neighbors as well.  
The neighbors' information is calculated by every vehicle by monitoring the signal strengths of the RBs. This information includes the neighbor identification and the corresponding resource records. This information is gathered after every 5 s interval for every  
Figure 8: Workflow of TARS.  
Figure 7: Resource collision probability; (**a**) of the Markov model and, (**b**) a confidence interval of 95%.  
vehicle. In addition, the stored record is also weighted in such a way that the recently selected RBs have a higher weight than the old RBs.  
\[R_{s}=\sum_{j=1/10}^{1}\sum_{i=1}^{n}w_{j}\,R_{i}\quad,\quad w_{1}<w_{j-1}<w_{j} \tag{7}\]  
where \(R_{s}\) is the selected RB, \(w\) is the weighted moving average, \(w_{1}\) represents the oldest recorded value, and \(w_{j}\) is the latest value. Preference is given to the latest value. \(R_{i}\) is the set of RBs.  
Once the neighbor information is populated on every vehicle, TARS uses a crypto-hash function to select an RB for transmission by predicting the selected RBs of its neighbors. Explicitly, the neighbor information, along with time as a random variable, is fed into the crypto-hash function to acquire a 128 bit value. The 128 bit value is then pruned and mapped to one of the 50 RBs using Equation (8), as shown in Figure 9. Each vehicle calculates RBs in the same way for all of its neighbors.  
\[RB=(val_{cur}-val_{min})*\frac{50}{(val_{max}-val_{min})} \tag{8}\]  
where \(val_{cur}\) is the decimal-converted equivalent of the last two hexadecimal digits obtained by using MD5 hash. The number 50 shows the considered range of RBs. \(Val_{max}\) and \(val_{min}\) shows the maximum and minimum decimal equivalents for two hexadecimal digits, i.e., FF = 255 (\(val_{max}\)) and 00 = (\(val_{min}\)).
## 2 Methodology
### Uncertainty Propagation Analysis  
One of the aims of the uncertainty propagation is to evaluate the impact of the uncertainties of the inputs on the outputs of a generic model and to asses how they affect the performance of the system represented by the computational model. Usually, UP is a demanding activity, requiring a large number of expensive simulations. At the same time, UP is mandatory since uncertainties can have a dramatic impact on the performance of nuclear systems. In fact, one of the most recent methodologies for the licensing of nuclear systems is the so-called Best Estimate Plus Uncertainty method [22; 23], where the output is provided together with its uncertainty. This approach has been progressively replacing the more traditional conservative approach.  
To reduce the computational burden of the UP, several methods have been proposed in the literature. In this work, the Polynomial Chaos Expansion has been used, together with the Unscented Transform for verification purposes, benchmarking the two methods. Only in the case of the EL model has the Monte Carlo approach also been used as a further reference benchmark thanks to the sufficiently short computational time of the detailed model.
# Seismic Performance of Reinforced Concrete Short Columns Subjected to Freeze-Thaw Cycles
## 4 Conclusions  
Three square-shaped short column specimens subjected to different numbers of FTCs have been tested under constant axial load and reversed lateral loads. The effect of freeze-thaw damage on the seismic performance of RC columns was investigated. The results of this research are summarized under the following conclusions.  
(1) The increase in the number of FTCs finally changed the failure mode of the reinforced concrete columns tested in this experiment. The controlled column failed in the flexure dominated flexure-shear failure mode while the most freeze-thaw damaged column failed in the shear dominated column triggered by apparent shear strength degradation.  
(2) Despite a slight increase of load carrying capacity observed in the column with 100 FTCs, a reduction of 9.8% of the maximum force was detected in the case of 200 FTCs.  
(3) The ductility decreased, and the contribution of shear deformation to total lateral displacement increased obviously with the increasing of freeze-thaw duration, which was consistent with crack extensions and failure patterns exhibited during the loading procedure. In addition, the capacity of energy dissipation of the specimens remains almost unchanged and then decreases significantly as the number of FTCs increases.  
This research lays the foundation for simulating and evaluating the seismic performance of existing structures undergoing freeze-thaw attacks. On the basis of the experimental results, there is a pernicious threat of the freeze-thaw environment to the behaviors of the components. Considering the fact that the number of specimens is small in this experiment, more column specimens should be tested to confirm and quantify the effects of FTCs on the load carrying capacities as well as deformation characteristics.  
Conceptualization, Y.Z. and S.Z.; specimen design and testing, Y.Z. and X.R.; data analysis, L.D and H.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z., P.P., and S.Z.  
The authors are grateful for the financial support received from the National Nature Science Foundation of China (No.51678475) and the Research Fund of Shaanxi Province in China (2017 ZDXM-SF-093).  
The authors would like to thank the reviewers' efforts in reading the manuscript.  
The authors declare no conflict of interest.
# Advances in the Study of the Behavior of Full-Depth Reclamation (FDR) with Cement
## 3 Results and Discussion
### Relationship Between Flexural and Unconfined Compressive Strength  
The correlation between the values of the UCS at long-term and FS at long-term is shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **FS (MPa)** & **UCS (MPa)** & **ITS (MPa)** \\ \hline S1 & 0.806 & 3.766 & 0.520 \\ S2 & 0.598 & 3.344 & 0.313 \\ S3 & 0.580 & 3.203 & 0.402 \\ S4 & 0.775 & 3.947 & 0.447 \\ S5 & 0.787 & 3.580 & 0.423 \\ S6 & 0.610 & 3.317 & 0.398 \\ S7 & 0.361 & 2.273 & 0.198 \\ S8 & 0.350 & 2.896 & 0.273 \\ S9 & 0.599 & 3.649 & 0.393 \\ S10 & 0.366 & 2.169 & 0.209 \\ S11 & 0.538 & 4.199 & 0.488 \\ S12 & 0.667 & 4.340 & 0.483 \\ S13 & 0.556 & 3.918 & 0.457 \\ S14 & 0.221 & 2.313 & 0.155 \\ S15 & 0.638 & 3.827 & 0.394 \\ S16 & 0.427 & 2.919 & 0.174 \\ S17 & 0.420 & 2.465 & 0.128 \\ S18 & 0.585 & 5.103 & 0.314 \\ S19 & 0.673 & 4.651 & 0.345 \\ S20 & 0.609 & 4.559 & 0.427 \\ S21 & 0.561 & 3.663 & 0.380 \\ S22 & 0.516 & 3.660 & 0.379 \\ S23 & 0.667 & 4.423 & 0.386 \\ S24 & 0.501 & 3.600 & 0.310 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Long-term results obtained for flexural strength, unconfined compression strength, and indirect tensile strength tests.  
Figure 4: (**a**) UCS test; (**b**) indirect tensile strength (ITS) test.  
After examining various possible functions for correlating these two variables, the best correlation was obtained with an \(S\) shape function, with natural logarithm of the FS as dependent variable and 1/UCS as the independent variable. The developed relationship is shown in Equation (1).  
\[Ln(FS_{LT-UCS})=0.33-0.3225/UCS_{LT} \tag{1}\]  
where \(FS_{LT-UCS}\) is the estimated value of the flexural strength at long-term by means of \(UCS_{LT}\), and \(UCS_{LT}\) is the unconfined compressive strength at long-term, both expressed in MPa.  
The coefficient of determination (\(R^{2}\)) has a value of 0.629, which indicates that the model can explain more than the 62% of the variability of the model.  
The average values obtained in the tests for these two parameters for the recycled material were compared to the usual values for soil-cement and cement-bound granular material [26; 42; 45; 56; 57; 58] in Table 4.  
As seen in Table 4, while the values obtained in the analysis were lower than soil-cement strength due to the bituminous matrix, the relationship between the two parameters was closer to the cement-bound granular material.
## 3 Results
### Physical Properties
#### 3.2.2 Water Activity (AW)
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tested Value** & **Regression Equations** & **R** & **R2** \\ \hline DM & DM \(=0.087\) + \(0.009\cdot\mathrm{D}-0.004\cdot\mathrm{M}-0.006\cdot\mathrm{VI}\) & 0.52 & 0.27 \\ AW & AW \(=16.046\) – \(0.111\cdot\mathrm{D}-0.015\cdot\mathrm{M}-0.03\cdot\mathrm{VI}\) & 0.97 & 0.95 \\ pb & pb \(=117.825\) – \(23.719\cdot\mathrm{D}+31.258\cdot\mathrm{M}-8.260\cdot\mathrm{VI}\) & 0.91 & 0.83 \\ VGI & VGI \(=-28710.417\) – \(75.000\cdot\mathrm{D}+404.167\cdot\mathrm{M}-41.667\cdot\mathrm{VI}\) & 0.99 & 0.98 \\ S & S \(=6776.251\) – \(28.582\cdot\mathrm{D}-32.154\cdot\mathrm{M}-5.589\cdot\mathrm{VI}\) & 0.89 & 0.80 \\ L* & L* \(=6845.856\) + \(6.141\cdot\mathrm{D}-43.009\cdot\mathrm{M}-30.204\cdot\mathrm{VI}\) & 0.88 & 0.79 \\ a* & a* \(=45.943\) – \(1.221\cdot\mathrm{D}-6.347\cdot\mathrm{M}+7.137\cdot\mathrm{VI}\) & 0.94 & 0.89 \\ b* & b* \(=726.384\) + \(0.880\cdot\mathrm{D}-4.986\cdot\mathrm{M}-2.974\cdot\mathrm{VI}\) & 0.93 & 0.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Multiple regression equations, determination, and correlation coefficients. water activity values in all the samples tested. [PERSON] et al. (2020) observed that dried apples subjected to the vacuum impregnation process using xylitol also had lower AW values [25].
# A Framework for Constructing a Secure Domain of Sensor Nodes
## 5 Results and Discussion
### Security Evaluation
#### 5.3.5 Routing Attacks  
There are a number of attacks that target the routing protocol of WSNs. Since all such attacks, like Selective Forwarding, Sybil Attack, Wormhole Attack, Sinkhole Attack, or False Routing Information, are related to the placement of a malicious node inside the network or the manipulation of an existing domain sensor node, the possibilities of performing such attacks in the secure domain are very limited. Continuous monitoring of software integrity and hardware configuration of the sensor node using PCRs of TPM practically eliminates the possibility of installing malicious software.
## 4 Discussion
### Limitations and Future Work  
The framework developed here and applied to Wales will be extended and applied to the whole of Great Britain using data from the Centre for Ecology and Hydrology's Countryside Survey [30]. The same general method could also be applied to produce high-resolution continuous surface maps of other biophysical parameters. For example, in situ moisture data could be used to calibrate Normalised Difference Water Index (NDWI) imagery [35] to produce a regional scale soil and vegetation moisture product. Future work will also explore how dense the field data set needs to be, as well as assessing whether suitable field plots can be identified using the EO data. This will enable a more detailed experimental framework to be developed and will increase the accessibility of the method to other users.  
When relating NDVI to vegetation parameters, including Leaf Area Index, biomass and production, saturation in the relationship can occur [36; 37]. Saturation of the NDVI-ANPP relationship was observed in the summer images and is shown by the lack of relationship between NDVI and In(ANPP) (Figure 3 July images) and the lack of spread of NDVI values in the summer (Figure 5). However, using the spring green-up window (Figure 5) allows separation of the sites pre-saturation. Other studies have observed saturation at high ANPP levels, particularly above 1000 dry mass g m year\({}^{-1}\) g [20]. Therefore, the methodology outlined here is not suitable for reliably predicting ANPP in highly productive ecosystems such as tropical forests or in areas without a prominent green-up period.  
The NDVI-ANPP relationships are image-date specific and so ideally a new relationship should be derived for each time period. The derived relationships could potentially be applied to images from the same month in different years, however, due to differences in the green up time between years this could introduce additional uncertainties. Further work is needed to quantify this uncertainty and investigate ways of accounting for differences in vegetation phenology between years.
## 3 Results and Discussion
### Evaluation Method of NLM Noise Reduction Algorithm in MR Imaging
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Year** & **Evaluation Method** & **Used Specific Method** \\ \hline [PERSON] et al. [39] & 2008 & Comparison parameter & RMSE \\ \hline [PERSON] et al. [40] & 2009 & Quantitative parameter & SNR \\ \hline [PERSON] et al. [41] & 2010 & Quantitative parameter & Mean SI \\ \hline [PERSON] et al. [42] & 2010 & Comparison parameter & RMSE (mean square error) \\ \hline [PERSON] et al. [43] & 2010 & Comparison parameter & PSNR \\ \hline [PERSON] et al. [44] & 2010 & Comparison parameter & PSNR \\ \hline [PERSON] et al. [45] & 2011 & Both & SSIM + SNR \\ \hline [PERSON] et al. [46] & 2012 & Comparison parameter & PSNR \\ \hline [PERSON] et al. [47] & 2012 & Comparison parameter & RMSE, SSIM \\ \hline [PERSON] et al. [48] & 2013 & Comparison parameter & PSNR, SSIM, UQI, BC (Bhattacharya coefficient) \\ \hline [PERSON] et al. [49] & 2013 & Comparison parameter & PSNR, RMSE, SSIM \\ \hline [PERSON] et al. [50] & 2014 & Both & PSNR + SNR \\ \hline [PERSON] et al. [51] & 2014 & Comparison parameter & PSNR, SSIM \\ \hline [PERSON] et al. [52] & 2015 & Quantitative parameter & CNR \\ \hline [PERSON] et al. [53] & 2015 & Both & PSNR, SSIM + SNR \\ \hline [PERSON] et al. [54] & 2015 & Comparison parameter & PSNR, SSIM \\ \hline [PERSON] et al. [55] & 2015 & Comparison parameter & PSNR \\ \hline [PERSON] et al. [56] & 2016 & Comparison parameter & PSNR \\ \hline [PERSON] et al. [57] & 2016 & Comparison parameter & PSNR, SSIM \\ \hline [PERSON] et al. [58] & 2016 & Comparison parameter & PSNR, RMSE, SSIM, BC \\ \hline [PERSON] et al. [59] & 2017 & Quantitative parameter & SNR, CNR \\ \hline [PERSON] et al. [60] & 2019 & Comparison parameter & PSNR \\ \hline [PERSON] et al. [61] & 2019 & Comparison parameter & PSNR \\ \hline [PERSON] et al. [62] & 2019 & Comparison parameter & PSNR, SSIM \\ \hline [PERSON] et al. [63] & 2020 & Comparison parameter & PSNR, SSIM \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Characteristics for the evaluation method of the NLM noise reduction algorithm in MR imaging, from studies retrieved for systematic review. There were only three results from the combination of simultaneous comparison and quantitative evaluation parameters. This means that the quantitative evaluation results are included in the factor for evaluating similarity. After analyzing the number of publications by year, of all three types, no change was observed as time progressed. From our analysis, it is expected that comparison evaluation parameter should be considered first when evaluating images using the NLM noise reduction algorithm on MR images.
## 1 Introduction  
It is a fact that people in developed countries spend almost 90% of their time indoors, where they experience their greatest exposures. However, regulation of air pollution focuses on outdoor air, as indoor environment is less well-characterized and recognized as a potential location for exposure to air pollution.  
What makes indoor air intrinsically more interesting than outdoor air from a scientific point of view? Some sources are undoubtedly uniquely building-related (e.g., cleaning agents, emissions from building materials and personal care products), while some contaminant dynamics operate only in buildings (e.g., the distribution of particles and gases by mechanical ventilation systems and photochemical reactions, and the infiltration of soil gases). Besides this, air pollutant concentrations are often higher indoors than outdoors, particularly following activities such as cleaning and cooking (with a greater source strength indoors than outdoor on a per area basis), while it has already been proven that many indoor air pollutants are harmful to human health.  
Another issue is ventilation. While indoor microenvironments are a microcosm of most urban settings, the effective air exchange and renewal in buildings is much lower than outdoors, even in urban areas. This concern is amplified by the fact that energy efficiency measures, driven by climate change awareness, have made modern buildings more airtight, further degrading the quality of indoor air. Therefore, a person is significantly more likely to inhale a harmful chemical molecule if it is emitted indoors rather than outdoors.  
Monitoring of indoor air pollutants in a spatio-temporal basis is challenging. A key element is the access to local (i.e., indoor residential, workplace, or public building) exposure measurements. Unfortunately, the high cost and complexity of most current air pollutant monitors results in a lack of detailed spatial and temporal resolution. Therefore, individuals of vulnerable groups (children, pregnant, elderly, and sick people) have little insight into their personal exposure levels. This becomes significant in cases of hyper-local variations and short-term pollution events such as instant indoor activity (e.g., cooking, smoking, and dust resuspension). Advances in sensor miniaturization have encouraged the development of small, inexpensive devices capable of estimating pollutant concentrations. This new class of sensors presents new possibilities for indoor exposure monitoring.  
Furthermore, indoor air chemistry models typically account for air exchange with outdoors through ventilation, deposition on indoor surfaces, and photochemical reactions. Surface chemistry on furnishings, building materials, and human bodies is becoming increasingly recognized as being of crucial importance.  
In light of the above, this Special issue on 'Indoor Air Quality' was introduced to collect latest research and address challenging issues in the areas of the triptych: Indoor environment quality monitoring, indoor air modeling, and exposure to indoor air pollution.