esa-sceva/satcom-chunk-collection huggingface dataset:
# Emotion Recognition from ECG Signals Using Wavelet Scattering and Machine Learning
## 2 Materials and Methods
### Feature Extraction
#### 2.4.2 Frequency Domain Features  
Other commonly extracted features are related to the transformation of the signal into the frequency domain and the power of the signal associated to different frequency bands: very low frequency (VLF), low frequency (LF), mid frequency (MF), and high frequency (HF). Several studies indicate that there is some correlation between the ratio of LF/HF power and the sympathetic and parasympathetic activity of the nervous system [35; 36]; therefore, 7 frequency domain features were extracted: VLF, LF, MF, HF, LF/HF ratio, power spectral entropy, sample entropy, and Shannon entropy [27].
## 1 Introduction
NIAC (2009) defines it as the ability to reduce the size and duration of disasters and analyzes that highly resilient infrastructure reduces the damage and scale of various disasters and minimizes losses by reducing the time required for recovery [10]. In addition, it was used for various facilities such as power transmission facilities and water supply facilities in the field of disasters, and the concept of resilience according to the characteristics of each facility was established. In Korea, the establishment of structural and non-structural alternatives for disaster response was explained as the concept of resilience [11]. They argued that structural alternatives should be established to reduce damage caused by disasters, and non-structural alternatives to reduce disaster recovery time should be established. There is also a case of applying resilience to multi-purpose dams and agricultural reservoirs. [PERSON] et al. (2014) evaluated the flood control function of multi-purpose dams by introducing the concept of resilience and presented a method to evaluate alternatives for strengthening the safety of dams [12]. After that, [PERSON] et al. (2018) derived a drought water supply plan for each scenario considering the resilience for Lake Naju [13]. [PERSON] et al. (2021) introduced the concept of resilience to evaluate the power generation capacity of hydropower reservoirs and suggested a method of maximizing the power generation [14].
# Real-Time Remote Sensing of the _Lobesia botrana_ Moth Using a Wireless Acoustic Detection Sensor
## 2 Materials and Methods
### Sensor
#### 2.3.3 Network Connection  
There are many options to connect a Raspberry Pi to the internet. In this case, we used a battery-powered portable router with an access point (AP) from TP-LINK, model TL-MR3040. It provided internet access to the connected devices through an existent AP or through using a SIM Card. Under normal circumstances, only very low data are used.
## 3 Results and Discussion
### TEM Analysis  
The morphology of the samples (as powders) was characterized by transmission electron microscopy (TEM) (see Figure 3).  
Figure 2: Thermogravimetric analysis curves of the sol–gel silica materials (TEOS/DMVES/OTES (**A**), TEOS/DMVES/HDTMES (**B**)) (as powders).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Sample**} & **30–105 \({}^{\circ}\)C** & **105–350 \({}^{\circ}\)C** & \multicolumn{2}{c}{**350–700 \({}^{\circ}\)C**} & **Residue at 700 \({}^{\circ}\)C** \\ \cline{2-6}  & **wt. loss \%** & **wt. loss \%** & \(\mathbf{T_{max}}\)\({}^{1}\)C** & **wt. loss \%** & \(\mathbf{T_{max}}\)\({}^{\circ}\)C** & \(\mathbf{N_{2}}\)\%** \\ \hline A & \(1.50\) & \(5.58\) & \(272.8\) & \(29.75\) & \(488.4\) & \(63.17\) \\ B & \(2.42\) & \(7.37\) & \(239.5\) & \(22.18\) & \(485.4\) & \(68.04\) \\ \hline \end{tabular}  
1 T\({}_{\text{max}}\) (\({}^{\circ}\)C) = T(d\(\alpha\)/d\()_{\text{max}}\).  
\end{table}
Table 3: The weight loss (wt. loss %) and maximum decomposition temperature (T\({}_{\text{max}}\)) of the sol–gel silica materials, obtained as powders (temperature range of 30–700 \({}^{\circ}\)C).  
The TEM images show the amorphous nature of the samples and reveal that the silica matrix was crosslinked through covalently bonded Si-O-Si structures [31]. An enlarged view of the TEM image of sample B indicates that this material presented a more disordered structure compared with sample A.  
The stacked organization of the alkyl chains may have occurred to maximize the van der Waals interactions of the chains [32]. Moreover, it was shown that the morphology of the silica material could be modified as function of the hydrophobization of silica surface with silane precursors. The density of amorphous silica may have been responsible for the textural porosity of the silica powders [33].
# Analysis of the Channel Influence to Power Line Communications Based on ITU-T G.9904 (PRIME)
## 4 Workbench Description and Test Conditions
### Precision of the Simulations
#### 4.4.3 Tests Execution  
The tests have been designed to be executed automatically in an unattended batch process.  
The simulation of every point has required the transmission and reception of \(26,110,000\) PPDUs, or the equivalent time of 104 hours of simulated network. This high computational time is the result of complying with the FER and SNR precision requirements defined in the previous subsections.
# A Treatise on Reconnoitering the Suitability of Fuzzy MARCOS for Assessment of Conceptual Designs
## 2 Methodology
### TFN and Membership Functions  
Considering the multi-dimensional nature and different units of measurements and quantification of the design features and their sub-features, apportioning the crisp number will allow ambiguous and prejudice in the decision process. Hence, a fuzzy number with the triangular membership function is applied by using a linguistic scale to represent the membership functions as presented in Table 1. The linguistic scale was applied for aggregating the relative contributions of the sub-features to the design features and the availability of the sub-features in the design alternatives. For ease of analysis, consider a TFN '\(M\)', of which membership function '\(\mu_{m}(y)\)' is contained in [0 1] as defined in Equation (1) [39].  
\[\mu_{m}(y)=\left\{\begin{array}{ll}\frac{1}{b-a}y-\frac{a}{b-a}&y\in[a\;\;b] \\ \frac{1}{b-c}y-\frac{c}{b-c}&y\in[b\;\;c]\\ \\ 0&\text{Otherwise}\end{array}\right. \tag{1}\]  
In Equation (1), \(a\), \(b\) and \(c\) represent the lower, modal and upper values of \(M\), respectively, such that \(a\leq b\leq c\). The TFN (\(M\)) described in Equation (1) can be defuzzified to obtain a crisp value '\(M_{ crisp}\)', which is the best non-fuzzy performance value, as presented in Equation (2) [40].  
\[M_{crisp}=\frac{a+4b+c}{6} \tag{2}\]  
\begin{table}
\begin{tabular}{l l c} \hline \hline
**Relative Contributions of Sub-Features to** & **Relative Availability of Sub-Features in** & **Triangular Fuzzy Numbers and** \\
**Design Feature** & **the Design Alternatives** & **Membership Function** \\ \hline \hline Indeterminate Contribution (**IDC**) & Extremely Poor Availability (**ELA**) & \(1\,1\,1\) \\ \hline Indeterminate-Moderate Contribution (**IMC**) & Very Low Availability (**VLA**) & \(1\,\frac{3}{2}\,2\) \\ \hline Moderate Contribution (**MDC**) & Low Availability (**LOA**) & \(\frac{3}{2}\,2\,\frac{5}{2}\) \\ \hline Moderate-High Contribution (**MHC**) & Medium Low Availability (**MLA**) & \(2\,\frac{5}{2}\,3\) \\ \hline High Contribution (**HGC**) & Medium Availability (**MEA**) & \(\frac{5}{2}\,3\,\frac{7}{2}\) \\ \hline High-Very High Contribution (**HVC**) & Medium High Availability (**MHA**) & \(3\,\frac{7}{2}\,4\) \\ \hline Very High Contribution (**VHC**) & High Availability (**HGA**) & \(\frac{7}{2}\,4\,\frac{9}{2}\) \\ \hline Very High-Extreme Contribution (**VEC**) & Very High Availability (**VHA**) & \(4\,\frac{9}{2}\,5\) \\ \hline Extreme Contribution (**EXC**) & Extremely High Availability (**EHA**) & \(\frac{9}{2}\,5\,\frac{11}{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Linguistic terms and membership functions for the decision process.
## 4 Conclusions  
Based on the research carried out, the following conclusions were formulated:  
1. The dominant wear mechanisms of S205 and 6160 cutting inserts are abrasion and adhesion. The difference between these inserts lies in the fact that S205 inserts tend to form a built-up edge (BUE) on the rake face, whereas 6160 inserts exhibit a tendency to form a BUE on the flank face. Additionally, ceramic inserts 6160 have a greater tendency for notch wear.
2. Surface topography analysis revealed the formation of individual high ridges on surfaces machined with ceramic inserts. This phenomenon may result from BUE formation on the flank face, which is in direct contact with the machined surface. No individual high ridges were observed on surfaces machined with S205 inserts, where a BUE formed on the rake face.
3. The conducted research confirmed that both S205 and 6160 inserts can achieve a high-quality machined surface. The _Sa_ parameter did not exceed 0.45 \(\upmu\)m for any tested cutting conditions. Therefore, using these inserts in finish turning of Inconel 718 can be a viable alternative to grinding operations.
4. The effective cutting edge radius of S205 inserts decreases compared to the new inserts. In contrast, for 6160 inserts, the effective cutting edge radius increases. This is influenced by BUE, which forms on the flank face.
5. EDS analysis shows that the TiN coating is removed from the cutting insert, whereas the Al\({}_{2}\)O\({}_{3}\) layer is more wear-resistant and provides a protective barrier. At the same time, the Al\({}_{2}\)O\({}_{3}\) layer has a greater tendency for adhesion to the machined material.  
Conceptualization, P.S.; data curation, P.S.; formal analysis, P.S. and S.L.; funding acquisition, P.S. and J.P.; investigation, P.S. and J.P.; methodology, P.S. and S.L.; resources, P.S., S.L. and N.U.; software, P.S. and N.U.; supervision, S.L. and N.U.; validation, P.S. and S.L.; visualization, K.S. and B.K.; writing--original draft, P.S.; writing--review and editing, P.S. and S.L. All authors have read and agreed to the published version of the manuscript.  
This research received no funding.  
Not applicable.  
Data Availability StatementData are contained within the article.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Quantitative Analysis of Soil Microstructure
#### 3.2.3 Pore Circularity  
The circularity is defined as the degree to which the measured pore shape resembles a circle, with a value range of [0, 1]. As the particle becomes more rounded, it becomes more  
Figure 7: The curve of plane porosity under different consolidation times.  
circular. Figure 8 shows the curve of pore circularity under different consolidation times. When compared to before consolidation, the circularity of the pores in the soil sample increased during consolidation compression. The pores of the soil sample became more regular and closer to a circle after consolidation. This is because, during the consolidation compression process, the soil particles rearranged and formed a more stable equilibrium condition. At this time, the pores were smaller, the soil particles were more intimately coupled and the arrangement was more directed, which improved the homogeneity of the pores. Similar results have also been presented by [PERSON] et al. [43], who proposed that, under the initial consolidation stress, structural units are compressed and structurally adjusted, and the shape becomes oblate.  
In addition, the authors discovered that, at the initial stages of consolidation, particle damage is greater, and growth is more obvious, whereas at the later stage, particle shape and pore shape are more stable, and the curve is flatter. This demonstrates that recombination and aggregation occur among structural units under the consolidation pressure, and the degree of homogeneity of the pore tends to approach with time [43]. This conclusion is supported by the results of equivalent diameter and plane porosity.
## 4 A Transmission Strategy for Minimizing Energy Consumption  
As explained in Section 3, our goal is to optimize the transmission strategy for the localization process with the aim of obtaining certain accuracy in the distance estimations while minimizing the energy consumption. In this section, we first include a theoretical analysis of the energy consumption during the localization procedure in terms of the transmission strategy parameters (transmission power and packet transmission rate) and the channel behavior (characterized through the \(PRP\)) in order to obtain general expressions valid for any wireless network. Some results are also presented for the particular models described in Section 3 and a minimum consumption transmission strategy for the localization process that guarantees a given accuracy in the distance estimations is proposed. Then, two modifications to this ideal strategy are introduced in order to make it practical and effective in real applications by counteracting the random effects of radio propagation.
# Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas
## 5 Algorithm Development: GNSS/Vision Integration  
In this paper, we are proposing the integration of the GNSS raw measurements with the vision motion estimation in a tightly-coupled manner. The proposed approach diagram architecture is depicted in Figure 10.  
The acquired images (from the vision equipment box) are rectified and used in two different ways: firstly, the images are not modified in order to implement the visual odometry and obtain the camera pose, as well as its relative position; secondly, the \"copy\" of these images is processed (segmented following the steps presented in Figure 7) to implement the multipath mitigation algorithm. Only the selected satellites' (LOS satellites) raw measurements are used in the filter.
## 2 Structure Design
### Bending Internal Force and Deflection
The deflection curve equation of each superposition diagram is as follows:  
\[y_{D1}=y_{1}+\theta_{A1}\cdot l_{3}=\frac{q(l_{1}+l_{2})^{4}}{8 EI}+\frac{q(l_{1} +l_{2})^{3}}{6 EI}\cdot l_{3}=\frac{3q(l_{1}+l_{2})^{4}+4q(l_{1}+l_{2})^{3}l_{3}} {24 EI}, \tag{1}\]  
\[y_{D2}=y_{2}+\theta_{A2}\cdot(l_{2}+l_{3})=-\frac{q(l_{1})^{4}}{8 EI}-\frac{q(l_ {1})^{3}}{6 EI}\cdot(l_{2}+l_{3})=-\frac{3q(l_{1})^{4}+4q(l_{1})^{3}(l_{2}+l_{3} )}{24 EI}, \tag{2}\]  
\[y_{D3}=y_{3}=\frac{F(l_{1}+l_{2}+l_{3})^{3}}{3 EI}, \tag{3}\]  
By superimposing Equations (1)-(3), we can obtain  
\[y_{D}=y_{D1}+y_{D2}+y_{D3}=\frac{3q(l_{1}+l_{2})^{4}+4q(l_{1}+l_{2})^{3}l_{3}} {24 EI}-\frac{3q(l_{1})^{4}+4q(l_{1})^{3}(l_{2}+l_{3})}{24 EI}+\frac{F(l_{1}+l_{ 2}+l_{3})^{3}}{3 EI}, \tag{4}\]  
were E = the elastic modulus of the material, \(N/mm^{2}\); I = the cross-sectional area of the material, \(mm^{2}\); and q = standard values for distributed loads, \(kN/m\). As shown in Table 2, after Solidworks software simulation, the free end force q and the average distributed load F were obtained as follows:  
It is made of aluminum alloy nonferrous metal with excellent comprehensive performance and its brand name is 7075-t3510. According to the data, the elastic modulus of 7075 aluminum alloy is \(E=71.7 Gpa\). The coarse-fine composite structure adopted the calculation method of moment of inertia of circular section. All known parameters are substituted into the equation of the deflection curve derived from the superposition. Figure 1:0  
The coarse-fine stage composite structure is the overall plane bending of the main structure. Therefore, the allowable deflection is less than H/1500. The calculated result is  
Figure 1:1  
To sum up, the deflection of the coarse-fine composite structure is checked to meet the specified deviation requirement. According to the internal force diagram of bending moment and shear force, the bending internal force of the cantilever beam under the action of external force is within the normal range. \begin{table}
\begin{tabular}{c c} \hline
**Length** & **Value/mm** \\ \hline \(l_{1}\) & 40 \\ \(l_{2}\) & 43 \\ \(l_{3}\) & 33 \\ Span H & 116 \\ \hline \end{tabular}
\end{table} 
Table 1: Structure distance data obtained by Solidworks software. \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline F & 0.00022 kN \\ q & 0.0085 kN/m \\ \hline \end{tabular}
\end{table} 
Table 2: Structure free and force and average distributed load data by Solidworks software.
## 4 SRAM Start-Up Behavior
For visualization purposes, the floor plan is split in two parts (from rows 0 to 127, and from rows 128 to 255). Cells with a \(\mathrm{SUP_{1}}=1\) are represented in pure red, and cells with a \(\mathrm{SUP_{1}}=0\) are represented in pure yellow. Cells with \(\mathrm{SUP_{1}}\) values between 0 and 1 are colored using a mixture of red and yellow, with only those few cells with a \(\mathrm{SUP_{1}}=0.5\) being orange. Observe that most cells are reddish or yellowish since there are a few cells in region B of Figure 3a. Additionally, it can be seen that the pattern of \(\mathrm{SUP_{1}}\) is spatially random and shows no location dependency. Matrixes from other SRAM parts also show a completely random pattern but the pattern is different from the one shown in Figure 3b. By contrast, the probability distribution of \(\mathrm{SUP_{1}}\) is essentially equal from memory to memory. For this reason, even knowing the probability distribution of \(\mathrm{SUP_{1}}\), it is not possible to predict the actual cell start-up values in a given sub-set of memory cells, which is the basis for a good PUF. This fact has been previously quantified in Table 2, for instance with the uniqueness parameter, which shows a value very close to the ideal one. From these results, it turns out that the main issue with this SRAM PUF is not its uniqueness or its bit aliasing, but its reliability: the fact that some cells start up to a value that it is not fully repeatable in time. Bit error rate (BER) is defined as the average ratio of bit errors occurring each time PUF is challenged. We can compute BER of a single cell as the percentage of times that it will not start up to its preferred start value. For instance, a cell with a \(\mathrm{SUP_{1}}=0.99\) will have a bit error rate of 0.01, and a cell with a \(\mathrm{SUP_{1}}=0.01\) will have a \(\mathrm{SUP_{0}}=0.99\), and thus also a bit error rate of 0.01. If we compute the average value over all cells, we obtain 0.045 (4.5%). Obviously, this is an average: most cells will be very reliable and have a much better parameter, and only a few cells will have a parameter close to 50%, which are the undesired cells that can cause problems in the PUF. Consequently, it is important to estimate in the early stages of a design how many of these cells will be present, which is one of the goals of this paper. Furthermore, it is worth remarking that if error correction codes are used, the definition of a reliable cell can be more or less relaxed depending on the minimum cell reliability needed for the code to meet design specifications.
# Opportunistically Exploiting Internet of Things for Wireless Sensor Network Routing in Smart Cities
## 4 The Integration Platform
### Sensor Activity Modes in WSN Layer  
There are two activity modes for Sensors; (1) Normal mode, (2) IoT mode. The sensors select between the communication modes using a gossiping protocol [34]. The main idea is that by default a sensor remains in Normal mode which uses maximum energy. When there are ample IoT's in the environment to take the communication load from sensors, sensors switch to IoT mode that uses lesser energy. The algorithm for switching between the two modes is discussed in Section 4.5. When appropriate, IoT mode is turned On for some time.  
The time is divided into frames of 500 ms, and each frame is subdivided into slots as shown in Figure 5. In each frame, there is an initial listening slot (ILS), in which all sensors keep their receivers on regardless of which mode they are in. When in IoT mode, there are 10 equal slots for communication so that (1) Radio remains On only in relevant time; (2) minimal signal interference from neighbors while message passing; and (3) the neighbors do not face contention. The slots are assigned among sensors using a simple method, node id modulo number of slots in a frame.  
\[ReceiveSlotNo=SNId/noOfSlotsInFrame \tag{1}\]  
In this way each node is aware of its own communication slot and its neighbors communication slots.  
In normal mode, the radio Rx is On for the whole time. When there is any data to send then it goes to Tx mode and after transfer switch back to Rx mode. It is ensured that Rx is on at ILS in the beginning of each frame. In IoT mode, the Rx turns On only at ILS and if any data is expected, it keeps Rx ON in the sensors own communication slot. The Tx is turned ON according to the Rx slot of the next hop.  
For IoT discovery and negotiation the sensor's own communication slot is used. For the rest of the frame sensors the radio is in the sleep mode for saving energy.
## 4 Discussion
### Discussion of Lateral Ultimate Capacity
#### 4.1.2 Predictive Equations for Determining Lateral Ultimate Capacity for Monopiles  
By curve fitting the data in Figure 11, equations are obtained for determining the lateral ultimate capacity of monopiles for various E\({}_{\text{p}}\)*/E\({}_{\text{50}}\) ratios between 10,280 and 128 and L/D ratios between 3.33 and 13.33 for the case of e/D\({}_{\text{t}}\) of 6.83. Second-order polynomials were chosen based on the regression analyses. Equations (6)-(11) describe the ultimate capacity of MP in a clayey medium under eccentric loading of e/D\({}_{\text{t}}\) = 6.83 as representative of medium-depth water (i.e., water depth between 10-40 m). Figure 15 demonstrates excellent agreement between the predicted ultimate-capacity-employing Equations (6)-(11) and those calculated from the FE analysis (0.91 < R\({}^{2}\) < 0.999).  
Figure 14: Bending moment versus normalized depth for stiff soil (clay 6) profile.  
Figure 13: Bending moment versus normalized depth for MP in soft soil (clay 1) profile.  
For (E\({}_{\rm p}\)*/E\({}_{50}\) = 10,280):  
\[\frac{H_{u}}{S_{u}D^{2}} = -0.1897*\left(\frac{L}{D}\right)^{2}+5.2326*\left(\frac{L}{D} \right)-12.823\ \ \ \ \ \left({\rm R}^{2}=0.9916\right) \tag{6}\]  
For (E\({}_{\rm p}\)*/E\({}_{50}\) = 4536):  
\[\frac{H_{u}}{S_{u}D^{2}} = -0.1161*\left(\frac{L}{D}\right)^{2}+2.5244*\left(\frac{L}{D} \right)-3\ \ \ \ \ \ \left({\rm R}^{2}=0.949\right) \tag{7}\]  
For (E\({}_{\rm p}\)*/E\({}_{50}\) = 984):  
\[\frac{H_{u}}{S_{u}D^{2}} = -0.0796*\left(\frac{L}{D}\right)\,^{2}+1.6913*\left(\frac{L}{D} \right)-1.5355\ \ \ \ \ \left({\rm R}^{2}=0.9583\right) \tag{8}\]  
For (E\({}_{\rm p}\)*/E\({}_{50}\) = 499):  
\[\frac{H_{u}}{S_{u}D^{2}} = -0.0315*\left(\frac{L}{D}\right)^{2}+0.6905*\left(\frac{L}{D} \right)+0.256\ \ \ \ \ \left({\rm R}^{2}=0.9633\right) \tag{9}\]  
For (E\({}_{\rm p}\)*/E\({}_{50}\) = 256):  
\[\frac{H_{u}}{S_{u}D^{2}} = -0.1203*\left(\frac{L}{D}\right)^{2}+1.0909*\left(\frac{L}{D} \right)\ \ \ \ \left({\rm R}^{2}=0.9165\right) \tag{10}\]  
Figure 15: Comparison of predicted and calculated ultimate capacity.  
For (E\({}_{\rm P}\)*/E\({}_{50}\) = 128):  
\[\frac{H_{u}}{S_{u}D^{2}}=-0.0053*\left(\frac{L}{D}\right)^{2}+0.1044*\left(\frac {L}{D}\right)+0.6661\ \ \ \ \ \left({\rm R}^{2}=0.9229\right) \tag{11}\]