esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Requirements and Challenges of the Smart Grid
### Real-Time Monitoring  
The network must acquire, process and analyze data from the power assets in a suitable period of time to allow decision-making and control algorithms running in the central node to react against these changes appropriately. In this regard the differences between the asset management and the power system protection should be noted. While the former does not impose hard constraints in terms of real-time processing and generates large amount of data, the latter inherently requires a strict real-time decision-making system and dispatches fewer packets [21, 27]. It is accepted by the research community that measured data in wireless asset-managing systems, like the one proposed here, should reach sink nodes in less than 15 s [15]. It is important to highlight, that such constraint only applies to wireless asset-managing system and other systems (wired or those intended to be used in power system protection) may have to meet other requirements (such as those suggested in the IEC61850 standard).  
From a technological point of view, to ensure an appropriate asset managing solution in terms of timing constraints, diverse aspects must be taken into account. Firstly, despite the WSN nodes of the Smart Grid are able to collect large amounts of data, this information has to be pre-processed in every node before being sent to the wireless network. This is carried out to prevent large volumes of data from simultaneously reaching the central node, negatively affecting its ability to furnish a real-time decision making system. It has been proven as a non-trivial issue in widespread WSN sensing systems such as the London Traffic Management system [28]. Secondly, WSN nodes are extremely hardware limited devices incapable of computing complex mathematical operations, therefore there must exist a study of the trade-off between pre-processing in the node and the number of packets dispatched to the network.  
Moreover, to guarantee adequate data collection times, data latency must rank within a tolerable range. To achieve this goal, specific protocols settings must be tweaked as will be explored later in Section 5.1. Finally, extensive tests must be carried out to validate the deployed solution in terms of end-to-end delay and to ensure the proper functioning of the network.
## 4 Axial Compression Bearing Capacity
### Extended Parameter Analysis  
#### 4.1.1 Numerical Model  
Numerical simulations were performed using ABAQUS and the numerical model was simplified as follows:  
1. The material parameters of the concrete, bolts, and welded wire mesh were adopted according to the measured values, as shown in Tables 2 and 5. The solid element was used to simulate for concrete and the plasticity damage model implemented in ABAQUS was used as the constitutive relationship of concrete in the elastoplastic stage. Damage factors of the damage plasticity model were calculated according to the code [26]. The bolts and welded wire mesh were simulated with the wire element and the double broken line model was used as the constitutive relation of steel;
2. Referring to [9], the connection between segments was simulated by hard contact, meaning the contact surface was only under compression. The connections between bolts and the segment concrete were simulated using the embedded connection. According to the working characteristics of the interface and referring to the study by [PERSON] [9] and [PERSON] [27], the binding connection was used to simulate the connection between the filled concrete and its adjacent members and the Coulomb friction model was adopted;
3. The unreinforced parts of the specimens were not simulated. The top and bottom surfaces of the specimens were simplified and coupled into one point. Constraints were set on each direction of the point obtained from the top coupling and on the point obtained from the bottom coupling, except in the \(Z\) direction;
4. Displacement loading was adopted and the initial stress was used to simulate self-stress.  
According to the above points, the finite element numerical model was established, as shown in Figure 12. Due to the difference between the simulated constitutive relationship and the actual constitutive relationship of the materials, as well as the influence of interface simulation and loading error on the test, the numerical model needed to be continuously adjusted according to the test results. The numerical model was adjusted with the peak load and load-displacement curve of actual specimens P1 and S1-S as the control index. By adjusting the constitutive relationship of the material, interface parameters, and loading system in the numerical model, the load-displacement curves of the specimen recorded in the tests and the numerical model were obtained, as shown in Figure 13. It can be seen that the load-displacement curves of specimens P1 and S1-S recorded in the tests were basically consistent with those recorded by the numerical model. The difference between peak loads was less than 5%, indicating that the numerical model was well in accordance with the specimens, meaning it could be used as the basic model for the extended parameter analyses.
## 2 Materials and Methods
### Analytical Model  
Analytical model is utilised in order to obtain envelope based layers reflection attenuation function, which is used in proposed algorithm as a source of time delay dependent weight value in weighted summing of both signals. A chain matrix-based model of harmonic electromagnetic wave propagation in cascade connection of \(n\) non-lossy sections in the case of normal incidence will be considered [41; 42]. This situation is schematically presented in Figure 8a. \(c_{i}\) and \(b_{i}\) are forward- and backward-traveling wave amplitudes in each layer in the case of the considered frequency. For cascade connections of \(n\) sections (layers) we have the following equation [41; 42]:  
\[\left[\begin{array}{c}c_{1}\\ b_{1}\end{array}\right]=\prod_{i=1}^{n}\frac{1}{T_{i}}\left[\begin{array}{cc} e^{i\phi_{i}}&R_{i}e^{-i\phi_{i}}\\ R_{i}e^{i\phi_{i}}&e^{-i\phi_{i}}\end{array}\right]\left[\begin{array}{c}c_ {n+1}\\ b_{n+1}\end{array}\right]=\left[\begin{array}{cc}A_{11}&A_{12}\\ A_{21}&A_{22}\end{array}\right]\left[\begin{array}{c}c_{n+1}\\ b_{n+1}\end{array}\right], \tag{4}\]  
where \(\phi_{i}=k_{i}d_{i}\) is the electrical length, \(d_{i}\) is the layer thickness, \(k_{i}=k_{0}\sqrt{\epsilon_{ri}}\) is the propagation constant, \(R_{i}=\frac{Z_{i}-Z_{i-1}}{Z_{i}+Z_{i-1}}\) is the reflection coefficient, \(Z_{i}\) is the wave impedance, \(T_{i}=1+R_{i}\) is the transmission coefficient, and \(A_{kl}\) are the elements of the chain matrix.  
Assuming only one direction of excitation pulse--there is no electromagnetic wave incident from back side of examined material (\(b_{n+1}\) = 0)--we have:  
\[b_{1}=\frac{A_{21}}{A_{11}}c_{1}. \tag{5}\]  
Equation (5) enables calculation of the reflected wave amplitude \(b_{1}\) for a given frequency \(f\) based on the incident field amplitude \(c_{1}\) and the selected chain matrix elements. A block scheme of the reflected pulse calculation algorithm is presented in Figure 8b. Incident Gaussian pulse is transformed to frequency domain, where the analytical model (Equation (5)) is applied for each harmonics. After calculation of reflected wave \(b_{1}(f)\), the result is transformed back to the time domain. The incident pulse and its spectrum, as well as the reflected pulse, are presented in Figure 8c,d, respectively. One can observe the similarity of the obtained signal with the measured one presented in Figure 6. It consists of FSR and BSR pulses, as well as the exponentially-attenuated layer reflection pulse.
# AOA-Based Three-Dimensional Multi-Target Localization in Industrial WSNs for LOS Conditions
## 1 Introduction
The prototype system comprises two portable transmitters (Tx) that send orthogonal pseudo-noise (PN) sequences simultaneously to emulate MSNs and a receiver (Rx) that captures the signals using a rectangular planar antenna array to emulate an AN. The antenna array comprises 64 elements and the system operates at the carrier frequency of 3.5 GHz. In the experiment, the two Tx's were moved along a line, and the AOAs of their LOS paths to the AN were correctly determined. The field experiment has verified the effectiveness and feasibility of ATML in practical systems. The proposed ATML provides a fast multi-target localization method suitable for SIWSNs with movability, low transmission power, and low cost. It has the following advantages. * The designed MT-SIMO signal transmission scheme utilizes the existing communication systems without the need of extra cost, weight, and volume for new hardware. The scheme requires high hardware complexity and computational capacity on ANs, and minimizes the complexity and working load of MSNs, which is preferable in WSNs. * ATML adopts a simple iterative ML method to estimate the azimuth and elevation AOAs of propagation paths, which enables the 3D localization. The simulation and experimental results demonstrate the high accuracy of the 2D AOA estimation. * By using the CDMA approach, the spread spectrum gain is exploited to mitigate the interference among MSNs and also from external sources. Meanwhile, since the AOAs are estimated based on the array signals collected by multiple antennas, the received SNR is increased significantly by the array gain. Therefore, ATML can achieve high localization accuracy with low transmission power to save the energy consumption on MSNs. * MSNs broadcast pre-assigned spreading sequences simultaneously and ANs separate the signals based on the signal orthogonality. The relative positions of multiple MSNs can be determined at the same time. Hence, ATML has a high speed and efficiency for multi-target localization, which is critical for the industrial applications with stringent latency requirements. The remainder of this paper is organized as follows. In Section 2, we briefly review the node localization and AOA estimation technologies. Section 3 introduces the system model of the designed SIWSN architecture. The ATML scheme is proposed in Section 4 including the communication system architectures on both MSNs and ANs, signal separation based on CDMA, propagation path parameter estimation, and multi-target localization. Section 5 presents the simulation results to evaluate and compare the performance of ATML. The testbed implementation and experiments are presented in Section 6. Section 7 concludes the paper and points out the future research issues.
# Freshness Monitoring of Packaged Vegetables
## 4 Intelligent Packaging (IP)
### Indicators  
Indicators are devices that provide information about the change in characteristics as a result of interaction in the presence or absence of substance such as micro-organisms, oxygen and carbon dioxide. These devices do not have a receptor or a transducer as in sensors. They just rely on direct visible changes. This class of devices can be sub-divided into three main categories: time-temperature indicators (TTIs), gas indicators, and freshness indicators [117]. These indicators are widely used in the meat industry. However, the low-cost and simplicity of integrating them with packaging, opened the opportunity for their use in other food industries [117].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sensing Material** & **Transducer** & **RH Range (\%)** & **Response Time (s)** & **Sensitivity** & **Hysteresis (\%RH)** & **Ref.** \\ \hline \multirow{8}{*}{Polymer} & \multirow{4}{*}{Resistive} & 12–90 & 15–20 & 136 & \textless{}0.16 & [151] \\  & & 20–95 & \(\sim\)5 & high & Small & [152] \\  & & 20–95 & 97 & 31.5\%/\%RH & \(-\)1.1 to \(-\)0.8 & [153] \\  & & 10–90 & 45 & \(-\)0.0327 log & & \\  & & & & Z/\%RH & 6.1 & [154] \\  & & 20–95 & - & \(162\pm 0.6\) & & \\  & & 20–95 & - & ff/\%RH & \(<\)7 & [155] \\  & & & & & \(0.33\) pf/\%RH & & \\  & & 15–90 & 0.075–0.175 & & & \\  & & & & & \(46\) pf/\%RH & - & \\  & & & & & (62–90\%) & & \\  & & 10–90 & 108 & \(\sim\)1 pF/\%RH & \(<\)2 & [158] \\  & & 10–90 & 4 & \(>\)4\%/\%RH & \(<\)2 & [159] \\  & & 40–90 & 4 & 10.01 M\(\Omega\)/\% & \(<\)7 & [160] \\  & & & & & RH & \\  & & 30–95 & 188 & 483 f/\%RH & \(>\)65 & [161] \\  & & 11–98 & 10 & 111 p/\%RH & \(\sim\)2 & [162] \\  & & 30–90 & 45 & \(-\)0.0655 log & & \\ Hybrid & Resistive & & & \(-\)2/\%RH & \(<\)2 & [163] \\ (Polymer/Ceramic) & & 30–90 & 40 & \(-\)0.0306 log & & \\  & & & & Z/\%RH & - & [164] \\  & & & & & \(\sim\)31.6 & \\  & & & & & pF/\%RH & \\  & & 50–95 & 20 & 32.19\%/\%RH & - & [166] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Specification of thin film/membrane humidity sensors.
## 1 Introduction  
Generally, supplying a dependable evaluation of rock mass behaviors (i.e., deformation and strength) is primarily significant in designing and investigating rock engineering implementations, including underground excavation, slope, and foundation. To delve more into the depth of this issue, to analyze the characteristic of rock deformation, two major inputs should be considered for which the elasticity constants of rock are mass (E, v). Generally, it is feasible to measure the parameters of rock mass deformability, including Young's modulus, by field tests essentially in situ moduli (signified by E\({}_{\text{rm}}\)) directly and through research lab examinations as intact modulus (signified by E) indirectly [1, 2]. Furthermore, considering true deformation analysis regarding rock needs to be conducted due to location circumstances; it is significant to assess the parameters of rock deformation by utilizing both field and lab conditions. Therefore, the intact material modulus resulting from lab practices must be associated with the rock mass modulus utilizing a suitable classified scheme.  
The most broadly applied schemes for discovering [PERSON]'s modulus contain plate loading and flat jack examination for determinations in the field [3], along with uniaxial compressive strength (UCS) examination in the lab. Nevertheless, the consequences of these experiments may be vulnerable to be ambiguous according to the discontinuity and anisotropic characteristics of rock mass forced to various field pressures [1; 4]. However, direct measurements of [PERSON]'s modulus are extensively slow and demand expensive facilities, especially for field experiments [1; 5].  
Various polynomial regression patterns utilizing field along with lab analysis data have been offered to overcome these restrictions. These regression models employed associations with data from field and classification methods of rock mass, consisting of rock mass rating [6], tunnelling quality index [7], and geological strength index [8], to measure the values of deformation modulus, \(Em\), for an isotropic rock mass. The results from the majority of these associations present a quite reliable fit to the field data, although the modulus-based equations and the exponential relationships, as recommended by [9; 10], address poor efficiency in rock mass deformation moduli's prediction.  
Moreover, several regression equations were developed using data from lab analyses. The applied method of developing these regressions was relating a specified spectrum of data from simple index tests, including Schmidt hammer [11; 12], ultrasonic velocity [2; 13], point load strength [14; 15], and porosity [1; 16; 17], to [PERSON]'s modulus, E. While some previous studies stated that the traditional statistical methods lack the power of generalizability [17; 18], this present study endeavors to examine this controversy rigorously.  
Based on the latest research, the number of claimed successful applications of intelligent methodologies has noticeably increased in the area of science and engineering since almost 20 years ago [19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31;
## 2 Related Work
### Channel Interference
#### 2.2.1 Interference between 802.11 Networks and Other Networks That Work on 2.4 GHz  
ISM (Industrial Scientific Medical) 2.4 GHz is an open frequency band worldwide, and many communication systems work on it, such as ZigBee, WiFi, Bluetooth and wireless USB. With the development of short-range wireless communication systems in recent years, more and more systems work on 2.4 GHz. However, the frequency band of 2.4 GHz is limited, and that will lead to the interference between different communication systems. The interference problem will be increasingly serious and inevitable with an increasing number of short-range wireless communication systems.  
According to [PERSON] et al. (2001), previous research works have been classified into the following three categories:  
* Interference mechanism/interference principle: Some research works focused on the interference mechanism/interference principle and tried to analyze the possible causes of interference appearing between different communication systems. For example, [PERSON] et al. [PERSON] et al. (2018) divided the interference between WiFi and ZigBee into four cases and analyzed whether there exists channel interference in these four cases. The study of the interference mechanism/interference principle will lay the foundation for the following two types of research works.
* Interference avoidance: The scheduling problem of spectrum resources is the essence of interference avoidance, and the core problem is how to allocate the spectrum resources in different communication systems to transmit data. [PERSON] et al. [PERSON] et al. (2018) and [PERSON] et al. [PERSON] et al. (2018) achieved interference avoidance between WiFi and ZigBee communication systems. [PERSON] et al. [PERSON] et al. (2018) proposed a collaborative approach and a non-collaborative approach to solve the interference avoidance.  
* Interference coexistence: When the spectrum resources are used, there will exist interference. How to make different communication systems coexist is a challenge. [PERSON] et al. [31] achieved the coexistence of interference between WiFi and ZigBee, and [PERSON] et al. [32] achieved the coexistence of interference between WiFi and LTE.
# AIS for Malware Detection in a Realistic IoT System: Challenges and Opportunities
## 3 NPS Simulations and Evaluation  
In this section, we first describe the datasets used in our simulations to evaluate the leading AIS solution, the NPS algorithm [9]. In addition to the widely used NSL-KDD dataset [17] used in [3, 9], we present new NPS results with two recent datasets that were created by the intelligent security group at the University of New South Wales (UNSW) Canberra. The first dataset is the Bot-IoT dataset and the second one is the UNSW-NB15. Next, we benchmark the newly obtained results with existing works that employ the same datasets to highlight the systematic superiority of the NPS algorithm regardless of the dataset.
## 5 Upper Bounds on LDPC Codes Decoding Thresholds
### Further Upper Bounds on LDPC Codes Decoding Thresholds Holding for \(i_{1}=2\)
Moreover, in Reference [9] it was demonstrated that, when \(\lambda^{\prime}(0)\rho^{\prime}(1)>1\) (condition examined in this paper), for any integer \(l\) such that:  
\[l\leq\text{min}\{L_{2}n,(1-r)n\} \tag{32}\]being \(n\) the codeword length, the probability that a randomly chosen code \(\mathcal{G}\in\mathcal{C}(n,\lambda,\rho)\) has any weight-\(l\) codeword is:  
\[1-\mathrm{Pr}(X_{l}=0)=1-\mathrm{e}^{-\frac{(\lambda^{\prime}(0)\rho^{\prime}(1) )^{l}}{2}}+O(n^{-1/3}) \tag{33}\]  
From Equations (32) and (33), it may be concluded that both the parameter \(L_{2}\) and the product \(\lambda^{\prime}(0)\rho^{\prime}(1)\) have an effect on the weight distribution of a LDPC code. **7. Role of the Product \(\lambda^{\prime}(0)\rho^{\prime}(1)\) in Determining the Decoding Threshold of LDPC Codes**  
The bound of Equation (30) is strictly related to the product \(\lambda^{\prime}(0)\rho^{\prime}(1)\) investigated in this paper, but this relationship was not explicitly put in evidence in [10]. In particular, extending the result of [10], the following Lemma can be easily proven. **Lemma 1**. : _The above mentioned third bound on threshold in Equation (30) may be rewritten as:_  
\[s^{*}_{\mathrm{Jensen}}=4\mathrm{log}(\lambda^{\prime}(0)\rho^{\prime}(1)) \tag{34}\]  
Proof of Lemma 1.: Remembering the expression of \(\lambda(x)\) and \(\rho(x)\) in (1) and (2), respectively, \(\lambda^{\prime}(0)\) may be alternatively expressed as  
\[\lambda^{\prime}(0)=\sum_{i=2}^{d_{1}}(i-1)\lambda_{i}x_{x=0}^{i-2}=\lambda_{2} \tag{35}\]  
and \(\rho^{\prime}(1)\) as,  
\[\rho^{\prime}(1)=\sum_{j=2}^{d_{r}}(j-1)\rho_{j}x_{x=1}^{j-2}=\sum_{j=2}^{d_{ r}}(j-1)\rho_{j} \tag{36}\]  
Thus, remembering the expression of \(s^{*}_{\mathrm{[PERSON]}}\) found in Equation (30), which holds for \(i_{1}=2\) only, this can be rewritten as:  
\[s^{*}_{\mathrm{[PERSON]}}=4\mathrm{log}\lambda_{2}+4\mathrm{log}\Big{(}\sum_{j =2}^{d_{r}}(j-1)\rho_{j}\Big{)} \tag{37}\]  
and thus, given the above mentioned expressions found for \(\lambda^{\prime}(0)\) and \(\rho^{\prime}(1)\), also as:  
\[s^{*}_{\mathrm{[PERSON]}}=4\mathrm{log}\lambda^{\prime}(0)+4\mathrm{log}\rho^{ \prime}(1) \tag{38}\]  
from which we get the result. Taking \(\sigma^{*}_{\mathrm{[PERSON]}}=\sqrt{\frac{2}{s^{*}_{\mathrm{[PERSON]}}}}\), the third upper bound of [10] may be rewritten as:  
\[\sigma^{*}_{\mathrm{[PERSON]}}=\frac{1}{\sqrt{2\mathrm{log}(\lambda^{\prime}(0 )\rho^{\prime}(1))}} \tag{39}\]
## 5 Numerical Parametric Study on Tunnel Displacement
### Effect of Cover-to-Depth Ratio  
As the clearance (\(H\)) between the bottom of the foundation pit and the crown of the tunnel increases, the impact on the tunnels caused by the excavation and unloading would gradually decrease. The tunnel displacement responses with variations of the \(H/H_{p}\) ratios are analyzed in depth. The \(H\) values are set as 3 m, 6 m, 9 m, 12 m, 18 m, 24 m, 30 m, 36 m in the numerical runs, and the monitoring point arrangement is the same as Figure 5 described previously. Figure 16 shows the variations of tunnel heaves and horizontal displacements with different \(H/H_{p}\) ratios after the excavation completed. The results confirm that both the tunnel heaves and horizontal displacements gradually decrease with the increase in the \(H/H_{p}\) ratios, and the tunnel heave is much larger than the horizontal displacement. The heave and horizontal displacement differences of the tunnel gradually decrease with the increases in the \(H/H_{p}\) ratios. The tunnel heave is less than 2 mm when the \(H/H_{p}\) ratio is greater than 3.5, at this time the influence of excavation unloading on the tunnel deformation responses is marginal.  
Figure 14: Comparison of tunnel displacements between numerical simulation and experimental test.  
Figure 15: Comparison of earth pressure between numerical simulation and experimental test.
## 1 Introduction
In the papers [47; 48; 49], researchers analysed the capture of vehicle dynamic response data using GNSS methods, which are the simplest to use. They found that the drawbacks of this method are related to non-uniform sampling and errors due to the unfavourable position of the satellites. Today, the RTS method has been proven to overcome these drawbacks.