esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust CNN Compression Framework for Security-Sensitive Embedded Systems
## 3 Methods
### The Attack Model  
Before describing our proposed method, we first elaborate on the attack model. For the purpose, let us define the SoftMax output of a CNN with weight parameter \(w\in\mathbb{R}^{p}\) as \(f(\cdot;w)\). Let the data pairs \(\{(x_{i},y_{i})\}_{i=1}^{n}\) be a training dataset. Here, \(x_{i}\in\mathbb{R}^{d}\) is an input and \(y_{i}\in\{0,1\}^{k}\) is the corresponding one-hot encoded true label. Then, the training procedure of CNN can be described as the following optimization problem.  
\[w^{*}\in\operatorname*{arg\,min}_{w\in\mathbb{R}^{p}}\frac{1}{n}\sum_{i=1}^{ n}\mathcal{L}(f(x_{i};w),y_{i}). \tag{1}\]  
Here, \(\mathcal{L}\) is the cross-entropy loss [34] that indicates the gap between the SoftMax output and the true label. For the given discrete probability distribution \(p\) and \(q\), the cross-entropy loss is defined as follows:  
\[\mathcal{L}(q,p)=-\sum_{k}p_{k}\log q_{k}.\]  
The objective of the adversary is changing the prediction result of the trained CNN by adding an imperceptible perturbation on the input image, which can be generated by both targeted attack and untargeted attack. In the targeted attack, the adversary generates perturbation that minimizes the cross-entropy between the SoftMax output and the predefined target label that is different from the true label. Given input data pair \((x,y)\) and target label \(y_{t}\), the targeted attack can be described as follows:  
\[\min_{\delta\in\Delta}\mathcal{L}(f(x+\delta;w^{*}),y_{t}),\quad\text{such that }y_{t}\
eq y. \tag{2}\]  
Since the effectiveness of the targeted adversarial attack varies depending on the chosen target label, most robust pruning literature [19; 20; 30] focus on the untargeted attack for experimenting with adversarial examples, and we take the same approach. In untargeted adversarial attack, we generate adversarial examples by maximizing the cross-entropy between the SoftMax output and the true label:  
\[\max_{\delta\in\Delta}\mathcal{L}(f(x+\delta;w^{*}),y). \tag{3}\]  
Also, we suppose a white-box setting where the adversary has full knowledge about the target CNN. In this case, the adversary can solve (2) and (3) by exploiting the gradient of the target CNN.
###### Abstract  
From disaster prevention to mitigation, drug analysis to drug design, agriculture to food security, IoT to AI, and big data analysis to knowledge or sentiment mining, a high computation power is a prime necessity at present. As such, massively parallel computer (MPC) systems comprising a large number of nodes are gaining popularity. To interconnect these huge numbers of nodes efficiently, hierarchical interconnection networks are an attractive and feasible option. A Tori-connected flattened butterfly network (TFBN) has been proposed by the authors in a prior work for future generation MPC systems. In the previous study, the static network performance and static cost-effectiveness were evaluated. In this research, a novel trade-off factor named message traffic congestion vs. packing density trade-off factor has been proposed, which characterizes the message congestion in the network and its packing density. The factor is used to statically assess the suitability of the implementation of an interconnection network. The message traffic density, packing density, and new factor have been evaluated for the proposed network and similar competitive networks such as TTN, TESH, 2D-Mesh, 3D-Mesh, 2D-Torus, and 3D-Torus. It has been found that the performance of the TFBN is superior to the other networks.  
Tori-connected flattened butterfly network; massively parallel computer; hierarchical interconnection network; static network performance; packing density; message traffic density; traffic congestion vs. packing density trade-off factor  
10 September 2021  
29 October 2021  
15 November 2021  
###### Abstract  
From disaster prevention to mitigation, drug analysis to drug design, agriculture to food security, IoT to AI, and big data analysis to knowledge or sentiment mining, a high computation power is a prime necessity at present. As such, massively parallel computer (MPC) systems comprising a large number of nodes are gaining popularity. To interconnect these huge numbers of nodes efficiently, hierarchical interconnection networks are an attractive and feasible option. A Tori-connected flattened butterfly network (TFBN) has been proposed by the authors in a prior work for future generation MPC systems. In the previous study, the static network performance and static cost-effectiveness were evaluated. In this research, a novel trade-off factor named message traffic congestion vs. packing density trade-off factor has been proposed, which characterizes the message congestion in the network and its packing density. The factor is used to statically assess the suitability of the implementation of an interconnection network. The message traffic density, packing density, and new factor have been evaluated for the proposed network and similar competitive networks such as TTN, TESH, 2D-Mesh, 3D-Mesh, 2D-Torus, and 3D-Torus. It has been found that the performance of the TFBN is superior to the other networks.
## 1 Introduction
Previous analyses successfully investigated the stress generated in the bending and torsion condition of the NiTi instrument in reason of their cross sections [30; 31] or of their thermal treatment [1]. This kind of FEA was also used to predict the cyclic fatigue resistance of various NiTi instruments through the simulation of traditional testing devices [32; 33]. It was also able to display the effect of root canal treatment procedures on the stress distribution in a tooth resin model [34]. However, there are no studies that have reported their mechanical behavior in simulated sound (normal) and high mineralized dentine elastic modulus root canal models. For these reasons, there is a need to simulate NiTi instruments under more complete simulating clinical conditions and to analyze the stress distribution produced by different alloy endodontic instruments. In this context, the aim of the current research was to evaluate the mechanical behavior of five different commercially available NiTi instruments by means of a 3D-FEA, focusing the influence of the material-shape combination (conventional and heat-treated NiTi alloy, cross sectional design, conicity), as well as the elastic modulus of the dentine root canal (18 and 42 GPa). The null hypothesis was that different endodontic instruments would not affect the von Mises stress and strain distributions in root dentine canals with different elastic moduli.
# MFCC Selection by LASSO for Honey Bee Classification
## 2 Materials and Methods
### Audio Signal Parametrization: MFCC and Derivatives  
MFCCs (Mel-frequency cepstral coefficients) are a feature extraction technique commonly used in speech and audio signal processing [32; 33]. The coefficients represent the short-term power spectrum of a signal based on the Mel-frequency scale, which mimics the nonlinearity of human auditory perception. The process involves several steps presented in Figure 3, including segmenting the signal into frames, applying a window function, calculating the Discrete Fourier Transform (DFT) of each frame, mapping the resulting spectrum onto the Mel scale, taking the logarithm of the Mel-spectrum (cepstrum), and finally applying the Discrete Cosine Transform (DCT) to obtain the MFCCs. These coefficients are often used as input features for speech recognition systems or other applications in audio processing [34; 35].  
To simulate processing by the human auditory system, so-called filter banks are used, i.e., collections of filters with parameters similar to the critical bands of the auditory membrane. The most commonly used filter bank is the Mel filter used to model the nonlinearity of pitch perception. Mel filters are triangular in shape and account for differences in the frequency resolution of the human auditory system, which is higher for low frequencies and lower for high frequencies. They are based on the Mel scale, whose dependence on the frequency scale expressed in hertz is given by the formula  
\[mel=2595*log_{10}(1+\frac{f}{700}). \tag{1}\]  
On the output of each filter \(H_{m}\), the energy of the band is calculated by the following formula:  
\[S_{m}=\sum_{f=0}^{K-1}|X(f)|^{2}*H_{m}(f), \tag{2}\]  
where \(K\) is the length of FFT, equal to the number of signal samples in the analyzed frame, \(m\) is a filter index, and \(X\) is a frequency spectrum of an input signal frame.  
In further calculations, the logarithm of the energy \(S_{m}\) is used, which allows to reduce the sensitivity of the filters to very loud and very quiet sounds, and to model the nonlinear amplitude sensitivity of the listening system. The final stage of the algorithm is the application of the Discrete Cosine Transform (DCT). The resulting values of the MFCC coefficients are calculated as  
\[c_{i}=\sqrt{\frac{2}{M}}\sum_{m=1}^{M}log(S_{m})cos(\frac{\pi i}{M}(m-0.5)), \tag{3}\]  
Figure 3: Mel-frequency cepstral coefficient extraction diagram.  
where \(i\) is the MFCC coefficient index, and \(M\) is equal to the number of used filters.  
An important advantage of MFCC coefficients is their low sensitivity to noise. In combination with the MFCC coefficients, their first and second derivatives, also called deltas, are often used. They are denoted by the symbols \(\Delta\) and \(\Delta^{2}\), or **Delta** and **DeltaDelta**, respectively. The derivatives of the coefficients provide a simple way to describe signal dynamics at relatively low computational cost.
# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 2 Background
### Datasets and Assessment Metrics  
#### 2.4.1 Datasets  
Prior to 2017, only a few datasets, such as the T91 image dataset [54] and the Berkeley Segmentation Dataset (BSD) [55], were commonly used in SR tasks. Real-time SR models were primarily trained using these datasets. However, after the introduction of the DIV2K dataset [56] in the NTIRE 2017 competition [55], it became the main dataset for training most lightweight SR algorithms. Table 1 shows the most recent datasets used to train and evaluate SR models along with the explanation of the datasets.  
Figure 3: Taxonomy of hybrid-based SR techniques.
# Experimental Evaluation of a LoRa Wildlife Monitoring Network in a Forest Vegetation Area
## 5 Experimental Results and Discussion
### Discussion  
In this study, we tested experimental LoRa communication in the forest environments. LoRa was designed for long-range, with theoretical coverage of 15 km for suburban and 5 km for urban areas. Nevertheless, measurements in the forest had an extremely short range compared to the theory. The maximum measured range achieved for a very dense forest vegetation area is 860 m for SF12. This drastic reduction is due to the difficult propagation conditions in the forest environment. LoRa has been shown to be sensitive to the presence of obstacles and reflectors, despite a CSS modulation technique that is more robust against interference.  
It is clear that the performance achievable with LoRa can vary greatly depending on the deployment scenario and is also sensitive to the choice of operational parameters. Whilst there are conditions under which choice of conservative parameter values, e.g., using worse \(SF\) and \(CR\) values can mean the difference between connectivity and the lack thereof, there are other situations where aggressive parameter values can be safely leveraged to maximize performance. Identifying the optimal value for these operational parameters and adapting them in response to changes in the operational environment remain a challenge. Furthermore, the obtained results from the theoretical coverage study corroborate to the experimental field results. Despite the similarities, there are still some discrepancies. For example, in the theoretical coverage heat map, a weak RSSI is obtained for POS D, whereas in the experimental study, it shows no connection to the gateway. This shows that there is still a statistically significant difference between the experimental and the theoretical results.  
Figure 16: SNR vs. SF with CR = 4/5, BW = 125 KHz for POS 1–POS 3.  
The observed behaviour from our experimental results suggests that the encountered vegetation should represent a case of a slow fading channel. Experiments in [56] in fact confirm how, in a fast fading channel, better LoRa performances are obtained with lower spreading factor, and thus a higher baud rate that assures a symbol period lower than the coherence time of the channel.  
In addition, all tests conducted have shown that 433 MHz performs better than 868 MHz in terms of achievable coverage, due to the lower radio propagation losses, but the unbalanced antenna system in favour of the 433 MHz band must be taken into account.  
Figure 17: Theoretical coverage heat map for the forest environment area (**a**) 433 MHz band (**b**) 868 MHz band.  
account. Given the receiver sensitivity of LoRa and the claims in the existing literature of being able to achieve several kms of range in practice, it is also of interest to assess how far in terms of distance connectivity could be established reliably in different forest vegetation areas. This result will provide meaningful insights into academia in providing accurate propagation models in a forest vegetation area of a LoRa network.
# Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System+
## 1 Introduction
However, this methodology suffers weaknesses due to its inability to capture the pre-determined existing causal relationship among the climate factors. In this study, we use the partial least squares path modelling (PLS-PM) [8] methodology to analyse the causal relationships among meteorological variables, e.g., minimum average temperature, maximum average temperature, relative humidity, wind speed, precipitation and solar radiation, and explored their impact on the outbreak of malaria. In doing so, we develop an integrated model that provides insight into which lacking pre-determined confounding effects could be identified as hidden ecological factors. In the third stage, we have used machine learning algorithms to identify a pattern/model that will be used to make an accurate prediction of malaria outbreak. We have evaluated the prediction of machine learning algorithms, and obtained a very high accuracy rate. Machine learning has been used for prediction and diagnosis of several diseases, e.g., Parkinson's [9], cancer [10] and heart disease [11]. Among machine learning methods, Support Vector Machines (SVM) [12] have been used in malaria incidence prediction [13]; but this study has several shortcomings:(i) the dataset used was extremely small (the size is only 33), which makes accuracy of prediction questionable; (ii) the dataset was used without analysing ecological factors, which could result in the inclusion of statistically insignificant variables in the prediction model, and hence could cause overfitting; (iii) there is no systematic methodology to transform this predictor into a smart healthcare system. In the fourth stage, we have developed a mobile application by embedding the best predictor generated in the previous stage. The application reads climatic information, i.e., temperature, relative humidity, wind speed, solar radiation and precipitation, from free weather and geographical Application Programming Interface (APIs). It then predicts the possibility of malaria outbreak several days in advance (based on available forecasting data). The subsequent sections of this paper are presented as follows: In Section 2, we present the complete analysis of identification of hidden ecological factors for the incidence of malaria transmission and its health implications to the change in biodiversity. Section 3 presents the intelligent malaria outbreak warning system, comprising data pre-processing, generating a prediction model using a machine learning algorithm and deployment of an intelligent mobile application. Section 4 concludes the paper by providing the summary of our results and our future work.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 2 The Principle and Theory of Rainfall Measurement by CML
### Mathematical Models  
The amount of signal attenuation, denoted as \(A_{tot}\) (dB), is obtained by subtracting the received power from the transmitting power. Here, \(A_{tot}\) comprises three types of attenuations, i.e., baseline attenuation \(A_{baseline}\) (dB), path-integrated rain attenuation \(A_{rain}\) (dB), and wet antenna attenuation \(A_{WAA}\) (dB). That is,  
\[A_{tot}=A_{baseline}+A_{rain}+A_{WAA}. \tag{2}\]
## 4 Experiments and Results Analysis
### Comparison of Methods  
In this paper, three algorithms are selected as comparative experiments. Two of them are based on collaborative filtering with Least-misery and average strategy, and one algorithm is based on context aware matrix factorization and intragroup divergence. We make a comprehensive comparison with three baseline methods.  
(1) CFL (CF with the least misery strategy): CFL employs user-based collaborative filtering algorithm to estimate recommendation score of each user in the group for candidate POI, and it adopts the least misery strategy, which focuses on the user with the lowest score in the group to get the final result for group.  
(2) CFA (CF with the average strategy): CFA employs user-based collaborative filtering algorithm to estimate recommendation score of each user in the group for candidate POI, and it adopts the average strategy, which calculating the average of these location scores of all users in the group as the final score of the group.  
(3) CMFC (context aware matrix factorization with an improved group consensus function) [19]: CMFC firstly obtains the preference of the individual for POI based on matrix factorization, then it uses group decision strategy with group consistency and group divergence computation and ranking adjustment to get the predicting score of POIs and the top-N POIs with the highest score are the recommendation result of the group.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Dataset** & **New York City** & **Tokyo** \\ \hline users & 1083 & 2293 \\ POIs & 38,333 & 61,858 \\ check-ins & 227,428 & 573,707 \\ two-person group & 2055 & 2129 \\ multi-person group & 338 & 220 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description information of the dataset.
## 3 Results and Discussion
### Influence of Joint Stiffness Ratio  
We defined the joint stiffness ratio (\(\gamma\)) as the joint rotational stiffness (\(k\)) against the sidewalls' linear stiffness (\(i\)), as demonstrated in Equation (7). The linear stiffness of the sidewalls was the ratio of the flexural rigidity of the sidewalls against the height of the utility tunnel:  
\[\gamma=\frac{k}{i}=\frac{kh}{EI} \tag{7}\]  
where \(k\) is the rotational stiffness of the joint, \(EI\) is the flexural rigidity of the sidewalls, \(h\) is the height of the utility tunnel, and \(i\) is the linear stiffness of the sidewalls, i.e., the ratio of the flexural rigidity of the sidewalls against the height of the utility tunnel (\(EI/h\)).  
By running FE simulations of the three types of models (uniform stiffness model, beam-spring model, and stiffness reduction model) consecutively, the stiffness reduction coefficient (\(\eta\)) and the bending moment adjustment coefficients (\(\xi_{t}\) and \(\xi_{b}\)) could be obtained, as outlined in Section 2. Setting the joint stiffness ratios \(\gamma\) as 0.1, 0.5, 1, 2, 5, and 10 respectively, the relationship curve between \(\gamma\) and \(\eta\) was obtained, as shown in Figure 5a, and the relationship curves between \(\gamma\) and \(\xi_{t}/\xi_{b}\) are shown in Figure 5b.  
It can be seen from Figure 5a that, when \(\gamma\) increased from 0.1 to 10 gradually, the stiffness reduction coefficient increased from 0.30 to 0.86, indicating that the stiffness reduction coefficient of the sidewalls increased with the increase in the joint stiffness ratio, in general. It can be seen from Figure 5b that, when \(\gamma\) increased from 0.1 to 10 gradually, the upper-end bending moment adjustment coefficient \(\xi_{t}\) decreased from 0.49 to 0.03, and the lower-end bending moment adjustment coefficient \(\xi_{b}\) decreased from 0.41 to 0.02, indicating that the bending moment adjustment coefficient decreased with the increase in the joint stiffness ratio, in general, which was true for \(\xi_{t}\) and \(\xi_{b}\). It can also be seen that, for a given joint stiffness ratio, \(\xi_{t}\) was slightly larger than \(\xi_{b}\). As is known, the process of changing the rotational stiffness of a joint from small to large corresponds to the process of increasing the stiffness of the joint. This led to a decrease in the deflection at the joint (\(\Delta b_{2}\)), leading to increase in \(\eta\) according to Equation (1). Increasing the rotational stiffness of the joint also led to an increase in the bending moment of the joint at the middle-height of the side wall (\(M_{m}\)), which led to a decrease in both \(M_{t}\) and \(M_{\rm b}\) since the lateral earth pressure loading was kept unchanged. As a result, \(\xi_{t}\) and \(\xi_{b}\) decreased with the increase in \(\gamma\) according to Equations (2) and (3).