esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Glulam Regular Double-Tapered Beams for Agroforestry Constructions
## 1 Introduction
Moreover, the investigation in [29] also considered the influence of the laminate thickness on the optimization, as a discrete approach was followed, i.e., the optimization of the cross-section was limited by the characteristics of the commercially available glulam timber boards. The specific literature on glulam beams includes three studies worth citing. [PERSON] and [PERSON] [30] analyzed the geometry of glulam beams with different section heights and widths under point loading at the center of the beam. The authors ponder the importance of considering the variable geometry of the beam to utilize all the timber in a prismatic volume, where the offcuts can serve as new beams with variable sections. However, the impact of laminate thickness, load values (such as snow or wind) within those specified in the current building standards, and the interaction between purlin spacing and beam loading are not within the scope of this paper. [PERSON] [31] evaluated glulam beams on a cost basis using non-linear programming methods under more realistic scenarios in terms of beams, but set generic load values of 1 and 2 kN/m\({}^{2}\) and considered four spans ranging from 10 to 25 m. As in the previous research work, the author did not address the interaction between purlin and beam spacing that provides the three-dimensional perspective of the investigation, the effect of the laminate thickness on the height of the element, or the use loads established by the building standards. Similar limitations could be noticed in [PERSON] et al. [32], who addressed the optimization of different geometries of beams, as well as different qualities of glulam (i.e., GL22h, GL24h, GL26h, GL28h, GL30h and GL32h), by means of an Excel optimization module. For each beam typology, the authors proposed some general optimization rules under some assumptions regarding the slope, applied loads and purlin spacing. Note that, under the proposed design rules, the same dimensions of beams are considered valid for GL22h to GL32h glulam timber grades, and no statistical justification exists for this dimensioning approach. Therefore, the aim of this investigation is to provide a different insight into the three-dimensional optimization of roof structures comprised of GL32h glulam regular double-tapered beams and purlins and subjected to realistic loads conforming to Eurocode 5 [26; 27]. To achieve this objective, three specific research stages were concatenated. Initially, a numerical calculation program able to verify the structural performance of the different timber roofs was developed. Then, an optimization tool specifically tailored to the numerical calculation program was implemented to analyze the different geometries and identify the most cost-effective one through genetic algorithms. Finally, through the statistical analysis of the obtained results, some predictive equations are suggested as optimized design rules for both the dimensions of the structural elements and their arrangement within the structure.
## 2 The Proposed VA-RAIM
### Vision Model with Calibration
#### 2.2.2 Calibration Method  
In this section, the GPS measurements are applied to calibrate the vision system to reduce the error and fault of vision measurements. First, the bias caused by the error and fault of the landmark locations, _i.e._, \(\delta\mathbf{d}\) and \(\Delta\mathbf{d}\), is removed. Then a modified vision equation with vision single fault is obtained. With \(N_{G(k)}\) GPS satellites in view at discrete time \(k\), the linear measurement equation is described as:  
\[\mathbf{z}_{G(k)}=\mathbf{H}_{G(k)}\mathbf{x}_{(k)}+\mathbf{\varepsilon}_{G(k )}+\mathbf{b}_{G(k)} \tag{11}\]  
where \(\mathbf{z}_{V(k)}\in\ \mathbf{R}^{N_{G(k)}}\) is the vector of measurements at time \(k\), \(\mathbf{H}_{G(k)}\in\ \mathbf{R}^{N_{G(k)},\omega}\) is the observation matrix, which reflects the geometrical configuration. \(\mathbf{\varepsilon}_{G(k)}\in\ \mathbf{R}^{N_{G(k)}}\) is the measurement noise vector, which are generally assumed to be normally distributed with zero mean and covariance matrix \(\mathbf{\Sigma}_{G(k)}\in\ \mathbf{R}^{N_{G(k)},\omega N_{G(k)}}\). \(\mathbf{b}_{G(k)}\in\ \mathbf{R}^{N_{G(k)}}\) is the measurement-fault vector to be detected. At the beginning of the approach and landing phase, such as \(\mathbf{x}_{(0)}\) in Figure 1, the satellite measurements are sufficient due to little obstruction in the high altitude environment. Under this condition, the fine GPS observations can be obtained and the traditional RAIM is available to compute the trustable positioning results from GPS. Specifically, the GPS positioning solution \(\tilde{\mathbf{x}}_{(0)}\in\mathbf{R}^{4}\) obeys normal distribution \(N(\mathbf{x}_{(0)},\ \Delta_{G(0)})\), where the covariance matrix \(\mathbf{\Delta}_{G(0)}=(\mathbf{H}_{G(0)}^{\tau}\ \mathbf{\Sigma}_{G(0)}^{-1}\ \mathbf{H}_{G(0)})^{-1}\in\mathbf{R}^{4 \times 4}\).
## 2 Methods
### Segment Model of Overhead Conductors under WDR Condition  
The overhead conductor is hung with suspension insulator strings between transmission towers. A segment model of the conductor is depicted in Figure 2. In this model, the conductor segment is simplified as a cylinder, and \(\alpha\) is the inclination of the segment; \(\beta\) is the wind angle towards the segment; \(U\) is the inflow wind velocity. The impact of WDR on conductor loads can be divided into two parts: the effect of raindrop impingement and the influence of rivulets on aerodynamic characteristic. According to [PERSON] [14], the wind-driven rain is sufficient to create upper and lower rivulets along the conductor, while the lower rivulet is assumed to make little difference to the aerodynamic coefficients of the conductor and is neglected in this paper.  
Figure 3 shows the cross section of the conductor. Under a certain rain-wind condition, upper rivulet occurs at the surface of the cylinder. Relative size of the height and the width to the diameter of the conductor are about 0.10 and 0.17 [17]. The balance angle of the upper rivulet is \(\theta_{0}\), determined by the coupled actions of gravity force, surface tension, and rain-wind loads. The unstable angle of the upper rivulet \(\theta\) oscillates around \(\theta_{0}\) and the vibration of the rivulet can be considered harmonic [17; 40]. Yet, for simplicity, we assumed that the relative position of upper rivulet and conductor surface is fixed, that is, \(\theta\equiv\theta_{0}\).  
The component of the wind velocity \(U_{0}\), perpendicular to the cylinder, can be expressed as  
\[U_{0}=U\sqrt{1-\cos^{2}\alpha\sin^{2}\beta}. \tag{13}\]  
The initial attack angle is defined as  
\[\varphi_{0}=\arctan(\sin\alpha\tan\beta). \tag{14}\]  
The along-wind direction aerodynamic force per unit length of the cylinder can be expressed as  
\[F(\lambda)=\frac{1}{2}\rho U_{0}^{2}dC_{\rm F}(\lambda), \tag{15}\]  
where \(C_{\rm F}(\lambda)\) is the aerodynamic force coefficient and \(\lambda=\theta+\varphi\); \(\rho\) is the air density; \(d\) is the diameter of the cylinder.  
Figure 2: Segment model of a conductor.  
The aerodynamic force coefficient \(C_{F}(\lambda)\) is derived as  
\[C_{F}(\lambda)=U_{r}^{2}(C_{drag}(\lambda)\cos\varphi-C_{lift}(\lambda)\sin \varphi)/U_{0}^{2}, \tag{16}\]  
where \(C_{drag}\) and \(C_{lift}\) are the aerodynamic drag and lift force coefficients, respectively. \(U_{r}\) is the instantaneous relative wind velocity, and its horizontal and vertical component can be computed as  
\[U_{r,z} =U_{0}\cos\varphi_{0}-\dot{z}, \tag{17}\] \[U_{r,y} =U_{0}\sin\varphi_{0}-\dot{y}, \tag{18}\]  
where \(\dot{z}\) and \(\dot{y}\) are the horizontal and vertical velocity of the cylinder, respectively.
# NOMA Clustering for Improved Multicast IoT Schemes
## 1 Introduction
We investigate the system's performance from both spectral and energy efficiency points of view, and we analyze the optimal system configurations with the help of simulations results. The rest of the paper is organized as follows. We review the state of the art related to works considering D2D and NOMA schemes in Section 2. In Section 3, we present the system model, the NOMA pairing scheme, the coalitions formation mechanism, and the transmission tree algorithms. We analyze the system's spectral and energy efficiencies in Section 4, and we illustrate the obtained performance with different simulation results, including a comparison with classical NOMA and OMA systems, in Section 5. Section 6 concludes the paper.
# Logistics Distribution Vehicle Routing Problem with Time Window under Pallet 3D Loading Constraint
## 4 Algorithm Design
### Population Initialization  
The heuristic algorithm will be influenced by the initial solution. The optimal solution closer to the process of the algorithm solution will obtain better results. The loading part of this study uses the cargo container volume sorted from largest to smallest as the initial population. Clustering is performed before the path optimization process to ensure that the path results for each cluster center are within a reasonable range. Random numbers are used as the initial solution in each cluster center, as follows.  
Step 1: determine the location information of logistics nodes 1, 2, 3, 4, 5, 6,..., \(n\) and the location information of distribution center 0.  
Step 2: set the location information of the distribution center as the coordinates of the origin and calculate the distance using the distance formula between two nodes to produce the distance matrix \(D\).  
Step 3: the nodes in the set are arranged in ascending order, and a random number is obtained by the Rand*n function as the node position for swapping the order before and after the chromosome, and the positions are swapped and used as a new chromosome.  
Step 4: repeat Step 3 to generate the initial population.  
In the case of 3D loading, full loading of the vehicle is used as the initial solution to obtain the best possible loading of the goods into the vehicle.
# Lightweight Path Recovery in IPv6 Internet-of-Things Systems
## 4 Design of LiPa
### Path Correlation Inference
If \(h(i)\) is equal to \(\mathit{hash}(o(i),p(i),\mathit{path}(k)-o(k))\), this recovery path can be assigned to \(path(i)\). An example is given in Figure 5c: In a collection cycle, node \(K\) generates packet \(K1\), and node \(C\) generates packet \(C3\). Since the grandparent node of \(C\) and the parent node of \(K\) are both \(G\), \(path(K1)\) with path \((K,G,H,I)\) can be used to recover \(path(C3)\), i.e., \(path(C3)=(C,D,G,H,I)\). **Case 4**: When \(len(i)\) is equal to \(len(k)\) and \(p(i)\) is equal to \(p(k)\), \(path(i)\) is the combination of \(o(i)\) and the part of \(path(k)\) that removes \(o(k)\). If \(h(i)\) is equal to \(hash(o(i),path(k)-o(k))\), this recovery path can be assigned to \(path(i)\). The example is given in Figure 5d: In a collection cycle, node \(K\) generates packet \(K1\), and node \(D\) generates packet \(D4\). Since the parent node of \(D\) and the parent node of \(K\) are both \(G\), \(path(K1)\) with path \((K,G,H,I)\) can be used to recover \(path(X4)\), i.e., \(path(D4)=(D,G,H,I)\). **Case 5**: When \(len(i)\) is equal to \(len(k)\), another possibility is that \(g(i)\) and \(g(k)\) are the same. In this case, \(path(i)\) is the combination of \(o(i)\), \(p(i)\), and the part of \(path(k)\) that removes \(o(k)\) and \(p(k)\). Similarly, this recovery path can be assigned to \(path(i)\) if \(h(i)\) is equal to \(hash(o(i),p(i),path(k)-o(k)-p(k))\). The example is given in Figure 5e: In a collection cycle, node \(K\) generates packet \(K1\), and node \(E\) generates packet \(E5\). Since the grandpa node of \(E\) and the grandpa node of \(K\) are both \(H\), \(path(K1)\) with path \((K,G,H,I)\) can be used to recover \(path(E5)\), i.e., \(path(E5)=(E,F,H,I)\). **Case 6**: When \(len(i)\) is one hop less than \(len(k)\), \(path(i)\) is the combination of \(o(i)\) and the part of \(path(k)\) that removes \(o(k)\) and \(p(k)\) if \(p(i)\) is equal to \(g(k)\). Then, this recovery path can be assigned to \(path(i)\) after determining that \(h(i)\) is \(hash(o(i),path(k)-o(k)-p(k))\). An example is given in Figure 5f: In a collection cycle, node \(K\) generates packet \(K1\), and node \(F\) generates packet \(F6\). Since the parent node of \(F\) and the grandparent node of \(K\) are both \(H\), \(path(K1)\) with path \((K,G,H,I)\) can be used to recover \(path(F6)\), i.e., \(path(F6)=(F,H,I)\).
## 2 Network Environment and Problem Formulation
### Network Environment  
Given a circular monitoring region \(M\), this paper assumes that all sensors are randomly deployed in \(M\). Let the radius of the monitoring region \(M\) be \(R\). Let the transmission ranges of the mobile sink and all sensors be identical and equal to \(r\). Herein, we assume that the mobile sink and all sensors are aware of their own location information by GPS or other location support system and each sensor also knows its neighbors' locations. As shown in Figure 2, to balance the data-relaying workloads of all sensors, the monitoring region \(M\) is geographically partitioned into \(n=R/2r\) circular tracks where the thickness of each track is \(2r\). Let \(K=\{k_{i}\ |\ 1\leq i\leq n\}\) and \(S=\{s_{i}\ |\ 1\leq i\leq m\}\) denote the sets of all circular tracks and all sensors in \(M\), respectively. Let \(S_{i}\) denote the set of the sensors located in track \(k_{i}\) and \(|S_{i}|\) denote the number of sensors belonging to set \(S_{i}\).  
All sensors execute the sensing task and then periodically report their readings to the mobile sink in every time period \(t\). To collect the readings generated by all sensors, the time duration for the mobile sink to traverse each track for one sweep repetition is not more than \(t\). Therefore, the mobile sink will move along different tracks with different velocities. Moreover, when a mobile sink moves along the track \(k_{i}\), the track \(k_{i}\) is called the _data collection track_\(k_{\textit{collect}}\). To concentrate our discussion on constructing a movement trajectory for the mobile sink such that the data-relaying workloads of all sensors can be totally balanced, this paper assumes that any sensor \(s_{j}\!\in\!S_{i}\) delivers its reading to the sensor \(s_{k}\!\in\!S_{i}\) in a multi-hop manner by applying the existing routing protocol [12, 13, 14, 15]. After that, sensor \(s_{k}\) forwards its own and the received readings to the mobile sink when the sink passes through its transmission range. Based on this rule, once a mobile sink completes the movement of one sweep repetition on track \(k_{\mathit{collect}}\), it can collect all readings generated by all sensors in \(M\).
## 3 Results and Discussion
### Associations between Ambient PAH and Cough
BaA is a carcinogenic chemical classified into Group 2A as \"prob  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pollutants** & **Lag** & **Coefficient (B)** & **95\% CI** & \(p\) **Value** \\ \hline Total PAHs & 0 & \(-0.011\) & \(-0.250,0.228\) & 0.929 \\  & 1 & 0.066 & \(-0.166,0.298\) & 0.575 \\  & 2 & 0.083 & \(-0.135  
ably carcinogenic to humans\" by the International Agency for Research on Cancer [30]; its metabolite BaA-dihydrodiols has been reported to induce oxidative DNA damage, resulting in carcinogenesis [31]. PAHs on combustion PM and in the gas phase are known to be involved in the respiratory disease pathogenesis through three main mechanisms with regard to its relationship with respiratory diseases other than cancer; (i) induction of local pulmonary inflammation through the exposure of inflammatory and resident cells to PAHs in airways, (ii) autonomic nervous system reflexes induction by stimulating a receptor group in the autonomic nervous system in the respiratory tract, and (iii) crossing the epithelial barrier to cause responses in remote tissues and cells [3]. In addition to IgE-mediated actions, oxidative stress pathways are known to be involved in the pathogenesis and asthma exacerbation induced by PAHs: in this model, PAHs metabolites by cytochrome P450 may activate inflammatory signaling pathways and lead to transcriptional gene upregulation involved in the regulation of immune responses, resulting in the development and exacerbation of both atopic and non-allergic asthma [4]. A previous cohort study of Japanese adults with chronic cough revealed that patients without BA were more likely to have increased cough prevalence related to ambient pollutants, including PAH, than those with BA supporting these combined mechanisms, with the largest associations observed in lag 2 and lag 02 [6]. Furthermore, the development of immune dysfunction in people with asthma has been suggested to be mediated through epigenetic remodeling [32] in addition to the transcriptional levels. Atmospheric BaA might contribute to the development and progression of airway inflammation through various pathways, which in turn leads to cough symptoms, although the detailed mechanisms by which specific PAHs cause respiratory diseases and concomitant cough symptoms are yet to be determined. For other air pollutants used as covariates in the GEE, significant associations were observed between SO\({}_{2}\) levels and cough in the lag 3 model (Table S3) and between PM\({}_{2.5}\) levels and cough in the lag 3 and lag 4 models (Tables S3 and S4, respectively). Although it is not clear why PM\({}_{2.5}\) was negatively associated with cough, this finding is consistent with a previous study that reported SO\({}_{2}\) to be associated with cough in an analysis using the lag model in adults with chronic cough [6]. With regard to associations with characteristics of study participants, no statistically significant associations were found between age, body mass or asthma and cough symptoms (Tables S3 and S4).
# Reliability and Risk Centered Maintenance: A Novel Method for Supporting Maintenance Management
## 4 Case Study
Consequently, the second question delineated by the decision diagram is whether there is a scheduled, applicable, effective, and technically feasible restoration or replacement task. However, the answer is negative as periodic replacement of the bearing housing proves economically unfeasible. Thus, the third question outlined in the diagram would be whether the loss of function caused by the failure mode itself would be evident to the operational team under regular operating conditions. Given that cracks in a bearing housing are not easily identifiable through inspection routes typically conducted in hydroelectric power plants, the answer to this final question is also negative. Identifying cracks in such cases usually requires the implementation of specialized non-destructive techniques executed by trained professionals. Moreover, the primary evidence of the existence of cracks in a bearing housing, in this case, the observation of oil leakage, is not always noticeable, especially when the oil flows into the river water flow. Accordingly, the subsequent question specified in the decision diagram examines whether there is a failure-finding task that is applicable, technically feasible, and effective to be performed. As stated earlier, techniques such as ultrasound or penetrant liquid testing, performed periodically by outsourced teams specializing in non-destructive testing, fulfill these criteria. Consequently, the answer to this fourth question is affirmative, thereby suggesting the most suitable failure mode management policy to be employed for this particular failure mode is failure finding.
## 2 Materials and Methods
### Methods
#### 2.2.2 Feature Extraction  
Algorithm 1 shows the feature extraction process of raw data (normal and AF data), as mentioned in Section 2.2.1. As can be seen from Algorithm 1, the DWT function extracts the ECG raw data by decomposing the ECG signal into several frequency sub-bands [51; 53].  
Figure 4: Raw data (normal and AF segments).  
The procedure for the systematic decomposition of signal resolution x [n] is shown in Figure 5. As shown in Figure 5, three levels of decomposition have been implemented to signal x [n]. At each level, the signal is decomposed using two types of DWBF-based filters, namely, g [n] and h [n]. Note that g [n] is a DWBF high-pass filter, while h [n] is a DWBF low-pass filter. Each output of these filters is then downsampled by 2. The downsampler output of the first high-pass filter is detail-1 (D\({}_{1}\)), while the downsampler output of the first low-pass filter is approximation-1 (A\({}_{1}\)). Then, at the second level, the same decomposition process is carried out on approximation signal-1 (A\({}_{1}\)), and this is repeated until the last level (Level 3).  
Figure 6 shows the results of Level 4 decomposition using DBWF (DB\({}_{2}\)) on ECG signal numbered 4015, AF signal. The signal at the top is the input signal, while all the signals below are the decomposition results, consisting of approximate signals and detailed signals.  
Algorithm 1 processes the approximation signals and the detail signals into a feature for detecting atrial fibrillation. According to the algorithm, a feature set is the result of the first approximation signal, which is convoluted on all detailed signals at all levels (known as the wavelet coefficient index--WCI). Table 3 lists the maximum, average, minimum, and standard deviation values of each WCI of the signal numbered 4015 (AF signal); the signal numbered 16,265 (normal signal) has a WCI, as shown in Table 4.  
Figure 5: DWT sub-band decomposition where g [n] is a high-pass filter and h [n] is a low-pass filter.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Coefficients** & **Max** & **Mean** & **Min** & **Stdev** \\ \hline A4 & 7.33 & 0.27 & \(-11.72\) & 3.10 \\ D4 & 3.61 & 0.01 & \(-3.73\) & 0.99 \\ D3 & 2.07 & 0.00 & \(-1.94\) & 0.39 \\ D2 & 0.74 & 0.00 & \(-0.74\) & 0.12 \\ D1 & 0.18 & 0.00 & \(-0.22\) & 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Max, mean, min and stdev features (AF).  
Figure 6: Decomposition Level 4 using DB\({}_{2}\) on the 04015 ECG signal. Red graphs and green graphs are approximate and detailed signals, respectively.