esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability Assessment of Steel-Lined and Prestressed FRC Slabs against Projectile Impact
## 4 Experimental Program  
The impact testing of several prestressed and non-prestressed FRC slabs of 800 \(\times\) 800 \(\times\) 100 mm in size (Figure 1) was carried out, and the results of the tests were used for the probabilistic analysis. The experimental program involved impact testing of prestressed and non-prestressed FRC slabs using a gas gun (Figure 2). Three parameters were varied for testing of slabs under the projectile impact, viz., a quantity of steel fibers in the concrete, steel lining on the backside of the slab, and the prestressing level. All test specimens were reinforced with \(\phi\)8@100 mm both ways provided on both faces of the slabs.  
Figure 1: Details of prestressed slab specimens reinforced with rebars (All dimensions are in mm).  
There were twelve groups of slabs. The groups were different in (i) volume fraction of steel fibers (0%, 0.6%, and 1.2%), (ii) steel lining (no lining and steel lining at the back face), and (iii) prestressing (no prestressing and prestressing). Three slabs of each type were prepared for testing (except in one group where only two slabs were cast for testing), each at a different projectile strike velocity. Thus, a total of 35 slabs (Table 1) were tested. The prestressing was conducted in both directions using a post-tensioning system, which employed high-strength prestressing rods threaded and anchored at the ends using high-strength anchor nuts. The PVC ducts were embedded in concrete before casting for passing prestressing rods. Thick steel blocks were used for the distribution of prestressing force at the end anchorages. The 1.8-mm thick steel lining (provided at the back face) was anchored to concrete using shear studs of \(\phi\)8 steel rebars, which were welded to the steel lining (Figure 3). Additional details about the experimental program, data analysis, and damage mode can be found in [PERSON] et al. (2018).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Specimen** & & & & \\
**Number** & **Specimen ID** & **Fiber (\%)** & **Prestressed** & **Steel Lining on** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 1: Details of the test specimens employed for reliability analysis.  
Figure 2: (a) Gas gun for projectile impact testing of targets; (b) Steel projectile with a hemispherical nose, 40 mm in diameter and 100 mm in height.
## 5 Experimental Design and Result Analysis
### Experiment in Laboratory  
In this section, some experimental results are presented to prove the superiority of the proposed method over competitive methods in hash acoustic environments. In the first experiment, improved PNCC features, original PNCC features and MFCC features were extracted from the audio data of trunk borer pests in the laboratory environment. Then, these features were normalized and dimensionally reduced by principal component analysis (PCA) (as shown in Figure 10). Finally, SVM was used for classification. In the previous section, there is a detailed SVM parameter selection and training process. The signal processing flow of this experiment is shown in Figure 11. To show the efficiency of audio detection, we introduce the following Equation [32]  
\[DAAS=\frac{NPRTS}{NATS}100\% \tag{18}\]  
where \(DAAS\) denotes the detection accuracy of acoustic signal, \(NPRTS\) denotes the number of properly recognized test samples, and \(NATS\) denotes the number of all test samples. Next, in order to better highlight the differences between the methods, we count the average detection accuracy of the above three methods under different signal-to-noise ratio (SNR) noise. For this purpose, the following Equation was introduced  
\[ADAAS=\frac{\sum_{n=1}^{N}DAAS_{n}}{N}100\% \tag{19}\]  
where \(ADAAS\) denotes average detection accuracy of acoustic signal, \(DAAS_{n}\) denotes detection accuracy under the interference of type \(n\) noise and \(N\) denotes the kind of noise contained in the experiment. Table 1 shows the experimental results of the detection accuracy of the three methods in different noise environments.  
Figure 9: Genetic algorithm parameter optimization line chart.  
Compared with the data in Table 1, the proposed method had stronger noise robustness than the other methods. The proposed method improves the detection accuracy under different reverberations, with retaining the excellent white-noise- and pink-noise-suppression ability of the original PNCC method. In practical applications, whether in the laboratory environment or in the field environment, most of the sound signal acquisition equipment will collect more or less white noise when collecting audio signals. Concretely, professional audio acquisition equipment for trunk pests collect less white noise, such as AED2010L. However, its disadvantage is also obvious; that is, the cost is too high. There  
Figure 11: Signal processing flow chart.  
Figure 10: PCA principal component analysis.  
fore, it is not suitable for large-scale audio detection. In this paper, the vibration signals collected by piezoelectric sensors often contain more white noise. However, the detection method based on improved PNCC features has strong robustness to white noise, which greatly improves the detection accuracy of audio vibration signals. At the same time, the low-cost characteristic of piezoelectric sensor makes it possible to intercept trunk borers on a large scale.
## 4 Using the Influence Map as the State Space in Reinforcement Learning
### Using the Influence Map to Solve the Sparse Reward Problem  
#### 4.5.1 Sparse Reward  
In deep reinforcement learning tasks, many environments have sparse rewards problems. For example, in tasks such as those in Go and chess, the agent can only receive rewards after the entire game is over, causing the agent to receive no reward most of the time and possibly resulting in the inability to optimize the policy.  
There exists a handling technique called curiosity-driven learning [47], the idea of which is that in addition to the external reward provided by the environment, the agent provides an intrinsic reward for itself. The intrinsic reward is generated through an intrinsic curiosity module (ICM). The module predicts what will happen in the next state and compares it with the actual next state. If the predicted result differs from the actual result too much, it means that the next state is encountered less frequently, and a larger intrinsic reward is then be generated to encourage exploration of the uncertainty. Through this intrinsic reward, the agent tends to explore more diverse states, which can increase the diversity of collected data to a certain extent.  
Curiosity-driven learning fails in some situations, which is called the noisy TV problem. In these situations, the state always gives the agent a higher intrinsic reward when the next state is randomly generated. The generated reward causes the agent to tend to stay in the current state, even if this state is actually worthless.  
Figure 5: Dual-channel image after superimposition. (**a**) The original image. (**b**) The influence map.  
To solve the noisy TV problem, OpenAI proposed random network distillation (RND) [48]. The idea behind this technique is similar to curiosity-driven learning, but two neural networks are used to evaluate the current state. The parameters of one neural network are fixed after random initialization and remain frozen, whereas the other neural network continues learning and training. When the two neural networks have a large difference in evaluating the same state, the current state must be explored, and the agent is given a larger intrinsic reward. If a certain state is experienced many times, another neural network gradually reduces the evaluation error during the training, and the intrinsic reward obtained becomes increasingly smaller. This algorithm is based on a simple idea, but the effect of solving the noisy TV problem is desirable. For some reinforcement learning algorithms that already have two neural networks, it is very time-consuming to maintain the inference and training of the four neural networks simultaneously; thus, the training speed of RND is considerably reduced.
## 2 Background
### IoT in Pharmaceutical and Process Industries  
The digital transformation of the manufacturing industry is typically referred to as Industry 4.0 based on the significance and potential of digitalization in industrial settings. The key technologies of Industry 4.0 include smart IoT devices and sensors that enable the collection of vast amounts of data from different manufacturing processes and operations. Combined with the technological advancements in computing power, analyzing methods for big data and machine learning, Industry 4.0 enables significant improvements in the efficiency of manufacturing processes [32].  
In addition to automation, digitalization also provides new possibilities to enhance the work where automation is not feasible or needs to be supported by manual operations, i.e., smart working. Smart-working-enabling technologies include, e.g., wearable IoT devices and augmented reality, which aid the interaction between the operators and manufacturing systems [33]. IoT technologies can also be used to improve worker safety in the working environment [34].  
One of the main technologies that is being studied and developed based on the Industry-4.0-enabling technologies is the cyber-physical systems, i.e., digital twins, in which, e.g., data, simulation models, and predictive analytics are used to form as accurate a digital representation of a physical entity as possible to further improve the possibilities to analyze their performance and behavior [35, 36, 37, 38].  
In terms of quality management, the effects of Industry 4.0 are prominent in the digitalization of methodologies such as total quality management (TQM) and the use of IoT technologies in quality management operations and processes [39]. In some cases, the advancements in IoT technologies have also led to situations where the traditional quality management is not in alignment with the requirements of the IoT [40].  
In the process and pharmaceutical industries specifically, the benefits of IoT and Industry 4.0 can be seen as being the same as in other manufacturing industries [41, 42]. However, in the pharmaceutical industry, digitalization has been slowed due to the interdependence of processes and strict regulations requiring, e.g., comprehensive validation of newly developed systems and software, due to which the investments in Industry 4.0 solutions can be both risky and expensive [43, 44, 45]. In a survey by [PERSON] et al. the main areas of focus for Industry 4.0 adoption in the pharmaceutical industry were process optimization, production performance monitoring, insurance of regulatory compliance, and production downtime minimization [46].
# A Wideband Dielectric Waveguide-Based 160-GHz Radar Target Generator
## 3 Components
### Dielectric Waveguide
With increasing frequency, the attenuation decreases to a minimum of \(1.1\,\mathrm{dB/m}\) at \(180\,\mathrm{GHz}\) For higher frequencies the attenuation increases again due to increasing dielectric losses. The high attenuation at low frequencies stems from radiation losses which result from the smaller cross-section. The attenuation was determined in a back-to-back measurement with different waveguide lengths. The dielectric waveguides were fed by a rectangular metallic waveguide, since the TE\({}_{10}\) is very similar to the HE\({}_{11}\) mode. For the mode conversion a mode transformer is used [16], which has an insertion loss of \(0.7\,\mathrm{dB}\). Figure 4: Range spectra, normalized to their respective peak power, for an frequency-modulated continuous wave (FMCW) radar in free space and with the radar target generator (RTG). The target is positioned at \(L_{\mathrm{mech}}=\,2\,\mathrm{m}\). Figure 5: Cross-sectional view of the extruded dielectric waveguide (**a**) and the measured attenuation of the dielectric waveguide (**b**).
## 1 Introduction
Due to these disadvantages, slip-critical connections are not permitted by many composite structure design guide books, such as the American society of civil engineers (ASCE) design guide for fiber reinforced polymer (FRP) composite connections [1] and the Composites-Design Manual [7]. Studies on slip-critical connections involving composite materials are currently uncommon, and we are still far from a complete understanding of the mechanism of load transfer by friction forces with GFRP and stainless-steel faying surfaces. In this paper, we focus on to the effects of the cover plate, surface treatment, and prevailing torque on the slip factor and long-term performance of slip-critical connections in composite structures. Shear tests and pre-tension relaxation tests were completed under quasi-static monotonic loads. Test results were presented in terms of load-displacement curve, slip load and slip factor. We performed our analysis to probe into the optimal configuration of slip-critical composite connections. Based on the adhesion theory of friction, explanations of the working mechanism of the connection are offered.
## 3 Results  
### Heavy Metal Contents  
3.1.1 Soil Samples  
The statistical parameters of heavy metal contents and pH in 3312 surface soil samples from the Xinping area are listed in Table 1, and the histograms and their box plots are shown in Figure 2.  
The pH values of the surface soil samples vary from 3.89 to 8.12, which can be divided into four parts, pH \(\leq\) 5.5 for 2803 samples, 5.5 < pH \(\leq\) 6.5 for 418 samples, 6.5 < pH \(\leq\) 7.5 for 82 samples, and pH > 7.5 for 9 samples according to the soil criterion (GB15618-2018). The samples with pH values lower than 6.5 are about 97.3%, which indicates that the surface soil in the Xinping area is generally acidic.  
Compared to the geometric mean contents of surface soils in Yunnan province [41], it can be seen from Table 1 that the geometric mean contents of As, Cr, and Hg in Xinping area are greater than those in Yunnan Province, while the geometric mean contents of Cd and Pb are lower.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Analysis** & **Min.** & **Q\({}_{1}\)** & **Q\({}_{2}\)** & **Q\({}_{3}\)** & **Max.** & **n1** & **Mean1** & **Mean* & **n2** & **Mean2** & **Mean*** \\ \hline As & 0.05 & 8.0 & 15.2 & 25.4 & 480 & 3312 & 12.9 & 10.6 & 3282 & 13.3 & 9.1 \\ Cd & 10 & 73 & 110 & 180 & 6130 & 3312 & 120 & 254 & 3251 & 116 & 211 \\ Cr & 4.78 & 79 & 107 & 139 & 4619 & 3312 & 112 & 95 & 3045 & 104 & 87 \\ Hg & 8 & 57 & 111 & 179 & 10200 & 3312 & 101 & 65 & 3298 & 100 & 58 \\ Pb & 2.11 & 25.2 & 30.9 & 37.7 & 855 & 3312 & 30.9 & 41 & 3190 & 31.0 & 36 \\ pH & 3.89 & 4.69 & 4.93 & 5.26 & 8.12 & 3312 & 5.02 & - & 3204 & 4.96 & - \\ \hline \hline \end{tabular} Notes: The units of As, Cr, and Pb are ug/g except Cd and Hg, which are in ng/g. Q\({}_{1}\), Q\({}_{2}\), and Q\({}_{3}\) are first, second, and third quartiles of heavy metal contents and pH values, respectively. Mean1 and Mean2 are the geometric mean contents of surface soil samples from the Xinping area. Mean1 was calculated from all samples, while Mean2 was calculated by repeatedly culling data outliers using the threshold of avg. \(\pm\)3 SD geometrically. Mean* and Mean** are the geometric mean contents of surface soil (0–20 cm) in Yunnan province after [PERSON] et al. [41]. Mean* was obtained from all samples, while Mean* was calculated by culling data outliers.  
\end{table}
Table 1: Statistical parameters of analytical values of surface soil samples in the Xinping area.
_Article_  
Modeling of the Snowdrift in Cold Regions:  
Introduction and Evaluation of a New Approach  
[PERSON] 1,2, [PERSON] 1,2,*, [PERSON] 1,2 and [PERSON] 1,2  
Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education,  
Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin 150090, China  
Correspondence: [EMAIL_ADDRESS]; Tel.: +86-150-4581-1536  
Received: 17 July 2019; Accepted: 14 August 2019; Published: 17 August 2019  
**Featured Application: A new approach for investigating snowdrifts around buildings and snow loads on building roofs based on a new facility named the \"snow-wind combined experimental facility\" is proposed in this paper. First, the method proposed in this paper is convenient and economical, and also the experimental results prove that the method is feasible and reliable. In this paper, only experiments on snowdrift around a cube were chosen for the verification for the sake of generality, but this method could also be adopted for the prediction of snowdrift around complex building environments and snow loads on various shapes of building roofs.**  
**Abstract:** Unbalanced, or non-uniform, snow loads caused by snow drifting or sliding in cold regions with heavy snowfalls, can be a serious problem for the building industry. However, the methods for predicting snow distribution still need to be improved. Field observation is the most direct and reliable method to study snow distribution, but because the natural environment is uncontrollable and varies dramatically, sometimes conclusions may be confused under the influence of the many variables in the investigation. This paper proposes a snowing experiment approach using an outdoor snow-wind combined experiment facility for the study of snow distribution. The facility can produce a stable and controllable wind field and snowfall environment. Experiments which focused on snowdrift around a building were conducted during the winter to make an evaluation of the repeatability and reliability of the new approach. Finally, from the analysis of results, it was demonstrated that the experimental facility was stable and that the similarity criterion adopted for the snowing pattern was reliable. Especially, the minimum value of the friction speed ratio was suggested to ensure the test accuracy.  
**Keywords:** snow-wind combined experiment facility; snowdrift; field observation; scale experiments; similarity criterion
# Single Trace Side Channel Analysis on NTRU Implementation
## 2 Background
### Algorithm of NTRU
#### Encryption  
The purpose of encryption is to transport the data by converting a message using the public key of the receiver. Then only an owner of proper private key can decrypt the message. To encrypt a plain-text \(m\in\mathcal{B}_{m}\), we first choose a random polynomial \(r\) in \(\mathcal{B}_{r}\) and compute the cipher-text \(e\) as Equation (2).  
\[e=r\cdot h+m\pmod{q} \tag{2}\]  
The modulus \(q\) in the above equation means that each coefficient in a polynomial is reduced modulo \(q\).
## 5 Electric Field (EF) and Eddy Current (EC) Losses in the Tank Wall
As a result, the following asymptotic expression for the total EC losses can be obtained:  
\[ \begin{array}{rl}P_{\text{tot}}&\approx\sum\limits_{n=-\infty}^{+\infty} \frac{2|\beta_{n}|^{4}|_{n}|^{2}}{\pi\alpha ha^{2}}\sum\limits_{m=0}^{\infty} \frac{1}{|\lambda_{n,m}|^{4}}\int\limits_{a}^{b}rdr\big{|}\sqrt{\frac{a}{r}e^{ -\lambda_{n,m}(r-a)}-\frac{a}{r}}\big{|}^{2}\\ &\hskip 113.811024 pt+\frac{|\lambda_{n,m}|^{2}}{k_{n}^{2}}r\epsilon^{-2\text{ Re}[\lambda_{n,m}](r-a)}\}\\ &=\sum\limits_{n=-\infty}^{+\infty}\frac{2|\beta_{n}|^{4}|_{n}|^{2}}{\pi \alpha ha}\sum\limits_{m=0}^{\infty}\frac{1}{|\lambda_{n,m}|^{4}}\int\limits_ {a}^{b}dr\{\frac{a}{r}+\bigg{(}1+\frac{|\lambda_{n,m}|^{2}}{k_{m}^{2}}\bigg{)} e^{-2\text{Re}[\lambda_{n,m}](r-a)}\\ &\hskip 113.811024 pt-2\text{Re}\bigg{(}\sqrt{\frac{a}{r}e^{-\lambda_{n,m}(r-a )}}\bigg{)}\\ &=\sum\limits_{n=-\infty}^{+\infty}\frac{2|\beta_{n}|^{4}|_{n}|^{2}}{\pi \alpha ha}\sum\limits_{m=0}^{\infty}\frac{1}{|\lambda_{n,m}|^{4}}\bigg{\{}a \ln\frac{b}{a}+\frac{1-e^{-2\text{Re}[\lambda_{n,m}](r-a)}}{2\text{Re}[ \lambda_{n,m}]}\bigg{(}1+\frac{|\lambda_{n,m}|^{2}}{k_{m}^{2}}\bigg{)}\\ &\hskip 113.811024 pt-2\text{Re}\bigg{(}\sqrt{\frac{\pi a}{\lambda_{n,m}}}e^{ \lambda_{n,m}a}[\text{erf}\big{(}\sqrt{\lambda_{n,m}a}\big{)}-\text{erf}\big{(} \sqrt{\lambda_{n,m}a}\big{)}]\bigg{)},\end{array} \]  
where \(\text{erf}(x)\) is the error function [16]. Finally, taking advantage of the fact that the value of \(\big{|}\sqrt{\lambda_{n,m}b}\big{|}\) is high we can approximately write: \(\text{erf}\Big{(}\sqrt{\lambda_{n,m}b}\Big{)}\approx 1\). As a result, the following approximation can be suitably used for calculating the total losses:  
\[\begin{array}{rl}P_{\text{tot}}&=\sum\limits_{n=-\infty}^{+\infty}\frac{2| \beta_{n}|^{4}|_{n}|^{2}}{\pi\alpha ha}\left|\cosh\!\left(\frac{\beta_{n}h}{2} \right)\right|\sum\limits_{m=0}^{\infty}\frac{1}{|\lambda_{n,m}|^{4}}\bigg{\{}a \ln\frac{b}{a}+\frac{1}{2\text{Re}[\lambda_{n,m}]}\bigg{(}1+\frac{|\lambda_{n,m }|^{2}}{k_{m}^{2}}\bigg{)} \tag{29}\]  
where \(\text{erfc}(x)=1-\text{erf}(x)\) is the complementary error function [16].
## 4 Results and Discussion
### Stability and Thermodynamic Properties  
To investigate the stability and thermodynamic properties of the \(\alpha\)- and \(\beta\)-phases, we calculated their phonon densities of states (PHDOS). Since \(\beta\)-FeSi\({}_{2}\) is a large-unit system, we used, for this purpose, the Phonopy code. The calculated phonon spectrum was compared with that obtained in the theoretical investigation of [24], and we found that they are in good agreement. For \(\alpha\)-FeSi\({}_{2}\), the PHDOS was calculated using the DFPT approach for the 3-atom cell and the Phonopy code for the 48-atom supercells. The results are presented in Figure 9. One can note that, for \(\alpha\)-FeSi\({}_{2}\), both methods provide similar results, and in further investigations, the PHDOSs were calculated using the Phonopy code. The gap between 12 THz and 14.5 THz in the PHDOS of \(\alpha\)-FeSi\({}_{2}\) is lacking in the spectrum of the \(\beta\)-phase because of the large number of inequivalent sites with different surroundings.  
In Figure 10 the calculated heat capacity as a function of temperature is shown for the \(\beta\)-phase in comparison with the experimental results [62; 63]. Although the experimental data are scattered, the calculated curve in the temperature region up to 1000 K reproduces the overall temperature evolution of the experimental C\({}_{\rm v}\)(T) rather well. Thus, the simplest model based on phonon spectra is capable of describing the thermodynamic properties of \(\beta\)-FeSi\({}_{2}\) with acceptable accuracy in a temperature range up to 1000 K.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Structure** & **B** & **G** & **E** & \(\sigma\) & **KIC** \\ \hline \(\beta\)-FeSi\({}_{2}\) & 180.8 & 128.1 & 310.9 & 0.21 & 2.3 \\  & (172.8) a  & (130.1) a  & (312.0) a  & (0.20) a  & & \\ \hline \(\alpha\)-FeSi\({}_{2}\) & 177.0 & 129.4 & 312.1 & 0.21 & 2.3 \\  & (172) b  & & & & (2.5) d  \\ \hline \(\gamma\)-FeSi\({}_{2}\) & 173.5 & & & & & \\  & (167) b  & 29.8 & 84.6 & 0.42 & 1.1 \\  & (206) c  & & & & \\ \hline \end{tabular}
\end{table}
Table 5: Calculated Hill bulk (B), shear (G), and Young (E) moduli (in GPa); Poisson ration (\(\sigma\)); and fracture toughness (KIC) (in MPa\(\cdot\)m\({}^{1/2}\)) for the \(\alpha\)-, \(\beta\)-, and \(\gamma\)-phases of FeSi\({}_{2}\).