esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Protocol
### Autonomic Estimation Algorithm
However, the same technique may be used for the autonomic calculation of other parameters within the WSN (e.g., variance in the cluster sizes, available energy percentage). For the density estimation, each sensor node calculates the number of its neighbours and thus the converged parameter is considered to be the average network density. For the size estimation, one or more predefined nodes in the network initialize the parameter _Network_Size_ to 1 (at step 0) while the rest nodes initialize the parameter _Network_Size_ to 0. When the averaging protocol converges, the estimated value is 1/(\(N\cdot k\)) where \(N\) is the network size and \(k\) the number of nodes that initialized the parameter _Network_Size_ to 1 [48]. The following adaptation is proposed in NEURON in order to estimate the _Network_Size_ autonomically (without the need to predefine specific nodes that initialize the parameter _Network_Size_ to 1). All nodes have a random number generator and express their initiative to initialize their _Network_Size_ parameter to 1 with a certain probability. We address this probability as \(P_{init}\). The critical part of the adaptation is that all nodes respect the same probability. \(P_{init}\) varies from 0.1 to 0.3. When the _Network_Size_ parameter converges the converged value is approximately 1/(\(N\cdot P_{init}\)). By inversing the converged value, an approximation of the network size is available. However, this adaptation provides a parameter's estimation with some variance. In case that we desire to have more precise estimations, each node that chooses to initialize its _Network_Size_ parameter to 1 has to accompany the broadcasted message with an additional field called _SolicitatedGroup_. In this field the MAC address of the node is placed. Each node maintains a cache that contains all the solicitated MACs and in parallel, during each exchange of messages, solicits the contents of its cache. Then, in addition to the convergence criteria that were formulated previously,each node is not meant to be converged if the number of MACs that exist in its cache is not equal to the number of MACs that are solicited by its neighbours. Following this adaptation the converged value is exactly (not approximately) [1/(_N_ \(\cdot\)_NoMacs_)] where _NoMacs_ stands for the number of solicited MACs. The advantage of this adaptation is that it generates extremely precise results, albeit at the expense of a larger amount of messages exchanged until convergence is achieved.
# A Gradient-Based Cuckoo Search Algorithm for a Reservoir-Generation Scheduling Problem
## 4 Gradient Cuckoo Search for Reservoir Scheduling
### Constraints Handling  
Cascade reservoir operation is a complicated optimization problem with varieties of constraints. According to ICS, the initial population is generated following Equation (16):  
\[x_{i}=x_{i}^{\min}+rand\cdot\left(x_{i}^{\max}-x_{i}^{\min}\right) \tag{16}\]  
where \(x_{i}^{min}\) and \(x_{i}^{max}\) are, respectively, the lower and upper boundaries limit of reservoir \(i\); and \(rand\) is a random number between 0 and 1. However, there is a high probability that the randomly generated solutions do not satisfy all the constraints displayed in Section 2.2. In the initialization stage, if the generated solution is not feasible, it needs to be replaced by a new feasible solution.  
In the random search stage, the newly generated unfeasible solution cannot be discarded. It is adjusted to the feasible range as follows:  
\[x_{i+1}=\left\{\begin{array}{l}x_{i+1}^{\min},if\ x_{i+1}<x_{i+1}^{\min}\\ x_{i+1}^{\max},if\ x_{i+1}>x_{i+1}^{\max}\end{array}\right. \tag{17}\]  
The feasible range of water level \(x_{i+1}\) is defined as:  
\[x_{i+1}^{\max}=\min\Bigl{(}x_{i}+\Delta z,Z\Bigl{(}x_{i},Q_{i}^{\min}\Bigr{)}, Z\Bigl{(}x_{i},Q_{i}^{\min}\Bigr{)},z^{\max}\Bigr{)} \tag{18}\]  
\[x_{i+1}^{\min}=\max\Bigl{(}x_{i}-\Delta z,Z(x_{i},Q_{i}^{\max}),z^{\min}\Bigr{)} \tag{19}\]  
\[Z(x_{i},Q_{i})=Z(V(x_{i})+(I_{i}-Q_{i})\Delta t) \tag{20}\]  
where \(Q_{t}^{min}\) and \(Q_{t}^{max}\) are, respectively, the minimum and maximum outflows of the reservoir; \(Q_{t}^{min}\) is the minimum outflow for a guaranteed output; \(z^{min}\) and \(z^{max}\) are minimum and maximum water levels; \(\Delta z\) is the limitation of the water-level change; and \(Z(V)\) and \(V(Z)\) are the relationship between the water level and the storage.  
In the adjustment of the unfeasible solutions, a two-way solution correction strategy is employed. First, the water level is adjusted to the feasible range from period 1 to \(T\). If the correction fails at some unpredictable period, then the water level is adjusted from period \(T\) to the breakpoint. If the correction is still not successful, the solution would be considered as an infeasible solution.
# Boosting the Learning for Ranking Patterns
## 4 AHP-Based Learning Approach for Ranking Patterns
### AHPRank Algorithm
```
1 foreach\(\Delta_{ij}\in\Delta\)do\(\Delta_{ij}\leftarrow\texttt{0}\);
11\(\mathcal{S}_{k}\leftarrow\texttt{SensitivityBasedHeuristic}(\mathcal{P},w^{t-1}, \mathcal{M})\) ; // Learn weights
12
13 end for
14
25 end for return\(w^{t}\)
```  
**Algorithm 4**\(\texttt{AHPRank}(\mathcal{M},\mathcal{S},\langle\mathcal{P},\theta,T\rangle)\). A Pattern Selection Heuristic for AHPRank  
Active learning is a challenging task since each learning step needs to enhance the learned function. The selection of appropriate patterns for the user to rank is the key to incrementally improve the learning process. However, generating the ideal set of patterns is a computationally demanding task and is known to be NP-hard [34]. To address this issue, we propose a heuristic (Algorithm 3) based on sensitivity analysis for AHP models [35]. Our heuristic aims to select a pair of patterns that are close in terms of their overall score predicted by the learned function relative to the sum of the measure gaps:  
\[\sigma(P_{i},P_{j})=\left|\frac{g_{w}(P_{i})-g_{w}(P_{j})}{\sum_{\ell=1}^{m}(M_ {\ell}(P_{i})-M_{\ell}(P_{j}))}\right|;i,j\in 1\dots k. \tag{7}\]A successful aggregation should have the property that if two patterns are close in overall predicted score, they should also be close in their measure values. Thus, the algorithm will be enforced into learning the correct preferences of the patterns when having the lowest \(\sigma\) value since the selected pair is surely close in terms of \(g_{w}\) values and very distant on the values of the measures. Example 2 illustrates the selection process of the Algorithm 3. **Example 2**. : _Suppose we have two patterns \(P_{1}\) and \(P_{2}\) with four interestingness measures \(\mathcal{M}=M_{1},M_{2},M_{3},M_{4}\). Let \(P_{1}=\langle 0.1,0.2,0.3,0.7\rangle\), \(P_{2}=\langle 0.7,0.3,0.2,0.1\rangle\), and let the current learned weight vector be \(w=\langle 0.25,0.25,0.25,0.25\rangle\). At this stage, \(g_{w}(P_{1})=0.26\) and \(g_{w}(P_{2})=0.26\). Although \(P_{1}\) and \(P_{2}\) have the same overall score, their measure values differ. This pair of patterns is an interesting candidate with \(\sigma(P_{1},P_{2})=0\), where retrieving the user's preference between them can improve the learning process and bring it closer to the user's ranking._
# Study on Early Warning Methods for Shipping Input Risks Under Consideration of Public Health Events
## 4 Cross-Border Risk Transmission Model in the Global Shipping Network
### Cross-Border Risk Propagation Model of Shipping Nodes  
From the complex shipping network constructed above, it can be observed that the nodes in the network not only spread risks to other nodes through aviation but also carry risk propagation factors into the country when engaging in trade activities with other nodes via maritime cargo transport. Moreover, the likelihood of different cargo categories carrying propagation factors varies. Therefore, this paper will construct a model of the coupling importance of shipping network nodes based on the differences in transportation methods and delivered items. In calculating the risk value of routes, weights are assigned to each node; as follows is the model of inter-coupling importance of shipping network nodes:  
Let \(\omega\) represent the contribution of the layer network, indicating the proportion of this layer network in the entire network, and \(q\) represent the importance of nodes within the layer network, indicating the degree of importance of the nodes in the flight and maritime networks. Let \(\omega_{1i}\) be the contribution of the flight layer, \(\omega_{2j}\) be the contribution of the maritime layer, hence the contribution of the flight passenger transport layer is \(\omega_{11}\), the contribution of the flight cargo transport layer is \(\omega_{12}\), the contribution of the maritime passenger transport layer is \(\omega_{21}\), the contribution of the maritime cold chain cargo transport layer is \(\omega_{22}\), the contribution of the maritime bulk cargo layer is \(\omega_{23}\), and the contribution of the maritime other cargo layer is \(\omega_{24}\). The corresponding node importance \(q_{n}\) for each layer is denoted as \(q_{11n},q_{12n},q_{21n},q_{22n},q_{23n},q_{24n}\), where \(n\) represents the sequence number of nodes in that layer network. Normalizing the node importance data is denoted as \(\overline{q_{11n}},\overline{q_{12n}},\overline{q_{21n}},\overline{q_{22n}}, \overline{q_{23n}},\overline{q_{24n}}\),  
\[\overline{q_{n}}=\frac{q_{n}-\text{min}(q)}{\text{max}(q)-\text{min}(q)} \tag{13}\]  
Thus, the importance of node mutual coupling can be expressed as follows:  
\[\sum_{ijn}\left(\omega_{1i}\times\overline{q_{1 in}}+\omega_{2j}\times\overline {q_{2 jn}}\right),i=1,2,j=1,2,3,4,n=1,2,\ldots,241 \tag{14}\]
# Design of Triple-Band (DSRC, 5G, 6G) Antenna for Autonomous Vehicle Telematics
## 3 Results
### Triple Stacked Patch Antenna  
The designed 6G array was stacked onto a dual-band, 5G/dedicated short-range communication (DSRC) in [7; 8] to achieve a triple-band operation. The integration was achieved by placing the antenna substrate and top conductor on the bottom of the dual-band antenna, using the bottom conductor of the design in [7; 8], without the defected ground structure as the ground was common for all three patch antennas.
# Low Complexity Equalization of Orthogonal Chirp Division Multiplexing in Doubly-Selective Channels
## 4 Numerical Simulations  
In this subsection, numerical simulation results are provided to show the bit error rate (BER) performance of the proposed UP-OCDM equalization algorithms over doubly-selective channels with exact channel information. We here consider a communication scenario where the symbol duration is \(T\) = 64 \(\upmu\)s for UP-OCDM, OCDM, and OFDM systems. Therefore, the symbol sampling period is \(T/N\) = 0.25 \(\upmu\)s. All information bits in simulations are encoded with a convolutional code of rate 1/2 before mapping, and are decoded by a Viterbi decoder at the receiver after equalization. We use wide sense stationary uncorrelated scattering (WSSUS) Rayleigh fading channels with Jakes Doppler spectrum in our simulations. The channel memory length is set to \(L=16\) in the simulation and each channel has an exponentially decaying power delay profile of losing 2 dB per tap. The simulated transmit signal is filtered through channels with varying normalized Doppler spread \(f_{d}T\). The channel impulse response (CIR) information is assumed known to the receiver. Then the noise which modeled as AWGN is added at the receiver, and the BER is evaluated by \(E_{b}/N_{0}\), which means the ratio of bit energy \(E_{b}\) to the noise power density \(N_{0}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **MMSE Equalization** & **Band MMSE Equalization** & **Iterative LSQR Equalization** \\ \hline Complexity & \(\mathcal{O}(N^{3})\) & \(\mathcal{O}(NQ^{2})\) & \(\mathcal{O}(iNMlogN)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computational Complexity of Proposed Algorithms.
## 4 Discussion  
The presented ECT method provides some advantages over traditional imaging methods for the detection of in-plane waviness. One such advantage is the lack of reliance on spatial resolution for accurate angular measurements. This provides flexibility for defining the scan region and allows for effective measurements using larger probes. A disadvantage with our method is the extended scanning time; where an imaging technique only requires one raster (or c-scan) over the surface, we must raster over the surface (albeit at a lower resolution) once for each angular datapoint. However, this disadvantage is hardware-based, and methods are being developed in-house to mitigate this limitation. One possible solution is through the use of multiple coils on the probe to measure several directions simultaneously, thereby reducing the number of passes (and thus scan time) by a factor of the number of simultaneous measurement angles in the probe. Such a probe is proposed in [20,21]. Further refinement of the data processing algorithm could also reduce the number of necessary datapoints, specifically by further capitalizing on the complex nature of the ECT data. While outside the scope of this work, these measures could potentially reduce the scanning time to a single pass.  
The second primary disadvantage of this method in its current configuration is its limitation to tracking only one dominant fiber orientation. When multiple nominal orientations are present, e.g., a [0/45/90] unidirectional laminate or a woven CFRP laminate, each nominal direction can have its own independent waviness. Thus, woven CFRP laminates could be scanned with this method only if the waviness is not independent in both nominal orientations. Methods of discerning between multiple orientations and their associated depths are under investigation [5].  
Mitigating or resolving these limitations would increase the commercial potential of the proposed technique. In a commercial application, this method could be used exactly like other NDT scanning methods, such as UT, where a probe is mounted on a robotic arm or gantry system for scanning surfaces of various shapes. Instead of measuring depth data, this method would map the fiber direction across the surface of the object, thereby detecting in-plane waviness that could negatively impact the mechanical performance of the object under examination.
## 3 Simulation Results and Discussion
The battery charges at 50 W load and discharges when the load is 100-150 W. The overall efficiency of the system is 93.5 to 95.3%. The performance under varying load conditions is shown in Figure 15.2 \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Load** & \multicolumn{3}{c}{**50 W**} & \multicolumn{3}{c}{**100 W**} & \multicolumn{3}{c}{**150 W**} \\ \hline \multirow{2}{*}{**Parameter**} & **Voltage** & **Current** & **Power** & **Voltage** & **Current** & **Power** & **Voltage** & **Current** & **Power** \\  & **(V)** & **(A)** & **(W)** & **(V)** & **(A)** & **(W)** & **(V)** & **(A)** & **(W)** \\ \hline PV & 17.4 & 5.71 & 99.5 & 17.4 & 5.71 & 99.5 & 17.4 & 5.71 & 99.5 \\ \hline Battery & 13 & \(-\)3.48 & \(-\)45.3 & 12.9 & 0.371 & 4.79 & 12.8 & 4.32 & 55.2 \\ \hline Load & 24 & 2.08 & 50 & 24 & 4.16 & 100 & 24 & 6.25 & 150 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: System performance under varying load conditions. Figure 24: Battery power, current, and voltage for varying load conditions. Figure 25: Load power, current, and voltage for varying load conditions. The best locations of JSO were 2.8281 and 2.5565 for k\({}_{\rm p}\), and k\({}_{\rm i}\), respectively. Also, the best fitness of JSO was 1.4079. The number of search agents was selected as two (for k\({}_{\rm p}\), and k\({}_{\rm i}\)) and the number of the Jelly fish was selected as 30. In this article, we focused exclusively on normal irradiance conditions, which involve uniform irradiance without factoring in partial shading effects. Under these uniform irradiance conditions, the proposed method demonstrates effective performance.
# A New Architectural Approach to Monitoring and Controlling AM Processes
## 5 Case Study: In-Process Melt-Pool Monitoring
### Melt-Pool Size: A Near-Real-Time Control Problem  
The current case study focuses on controlling the size of the melt pool, which as noted, is a part of real-time loop in our architecture. We collected data from two different layers during the build, called layer1 and layer2. The total number of real-time, melt-pool samples consists of 2269 and 2215, respectively. Control decisions are based the results of melt-pool size monitoring and defect detection. If the process is stable, we assume that the part quality, which is determined from the specifications provided by the user, is acceptable. In the remainder of this section, we discuss our proposed, automatic, two-staged detection method to estimate the melt-pool size using that data automatically.  
The defect detection process is shown in Figure 4. After capturing the melt-pool images, the first step is to remove any noise in the images. We chose the Gaussian filter, which is a well-known noise-removal process. The next step is to find the structural boundary of the melt pool, which we used the canny edge detection method [21]. After the boundary of the melt-pool is known, the width, length, and area of the melt-pool can be calculated.  
Based on the area, we use a K-means clustering techniques to find any defects.
# Buckling Analysis of Interspersed Railway Tracks
## 2 Methodology
### Material Properties and Boundary Conditions
For the antisymmetrical buckling shape, the governing equations are identical to those derived for the symmetrical buckling shape except for the boundary condition at mid-track. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Characteristic Value** & **Unit** \\ \hline  & Rail (UIC60) & \\ \hline Gauge, \(g\) & 1.435 & m \\ Modulus, \(E_{r}\) & 2e5 & MPa \\ Poisson’s ratio, \(v_{r}\) & 0.25 & - \\ Density, \(d_{r}\) & 7850 & kg/m\({}^{3}\) \\ Thermal expansion & 1.17e-5 & 1/\({}^{c}\)C \\ \hline  & Concrete sleeper & \\ \hline Modulus, \(E_{s}\) & 3.75e4 & MPa \\ Shear modulus, \(G_{s}\) & 1.09e4 & MPa \\ Density, \(d_{r}\) & 2740 & kg/m\({}^{3}\) \\ Lateral resistance & 200–2000 & N/mm \\ Torsional resistance & 28.5–84.7 (Nominal value = 56.4) & kNm/rad \\ \hline  & Timber sleeper & \\ \hline Modulus, \(E_{s}\) & 1.02e4 & MPa \\ Shear modulus, \(G_{s}\) & 3.93e3 & MPa \\ Density, \(d_{r}\) & 1100 & kg/m\({}^{3}\) \\ Lateral resistance & 120–1200 & N/mm \\ Torsional resistance & 71.2–211.8 (Nominal value = 141.0) & kNm/rad \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Material properties. Figure 3: Typical buckling shape of the railway track and support constraints: (**a**) symmetrical and (**b**) antisymmetrical buckling shapes. It should be noted that the buckling shape and buckled region in the actual track can be changed due to changes in track constraints or boundary conditions. In this study, 100 m, 60 m, and 30 m tracks were investigated to study the effect of track length and unconstrained length, which is the length in the buckled region. It should be noted that the buckling length is normally between 10 m and 30 m, so an unconstrained length of 30 m was chosen, while beyond this length was considered the adjoining zone. According to the analytical solution [37], the unconstrained length and boundary conditions have a slight effect on buckling temperature but play a role in buckling shape. In this study, fixed supports were applied at both ends. To investigate the adjoining zone (\(l\leq x\leq l_{0}\)) after buckling, roller supports, which allow the rail to move in a longitudinal direction, were applied to the rails. The buckled zone (\(0\leq x\leq l\)) is a weaker area that allows the track to move laterally and be buckled so that it represents the buckling length of the track.
# Time-Frequency Analysis of Daily Activities for Fall Detection
###### Abstract  
Fall events in elderly populations often result in serious injury and may lead to long-term disability and/or death. While many fall detection systems have been developed using wearable sensors to distinguish falls from other daily activities, detection sensitivity and specificity decreases when exposed to more rigorous activities such as running and jumping. This research uses time-frequency analysis of _accelerometer-only_ activity data to develop a strategy for improving fall detection accuracy. In this study, a wireless sensor system (WSS) consisting of a three-axis accelerometer, microprocessor and wireless communications module is used to collect daily activities performed following a script in the laboratory setting. Experiments were conducted on 36 healthy human subjects performing four types of falls (i.e., forward, backward, and left/right sideway falls) as well as normal movements such as standing, walking, stand-to-sit, sit-to-stand, stepping, running and jumping. In total, 1227 different activities were collected and analyzed. The developed algorithm computes the magnitude of three-axis accelerometer data to detect if a critical fall threshold is passed, then analyzes the power spectral density within a critical fall duration window (500 ms) to differentiate fall events from other rigorous activities. Fall events were observed with high energy in the 2-3.5 Hz range and distinct from other rigorous activities such as running (3.5-5.5 Hz) and jumping (1-2 Hz). Preliminary results indicate the power spectral density (PSD)-based algorithm can detect falls with high sensitivity (98.4%) and specificity (98.6%) using lab-based daily activity data. The proposed algorithm has the benefit of improved accuracy over existing time-domain only strategies and multisensor strategies.  
full detection; wearable sensors; accelerometer; biomechanics; medical informatics 20212211-12[[https://doi.org/10.3390/signals2010001](https://doi.org/10.3390/signals2010001)]([https://doi.org/10.3390/signals2010001](https://doi.org/10.3390/signals2010001))
## 4 Simulation Implementation
### Smart Office Building Floor Plan
#### 4.2.2 Latency  
According to [55], the application loop occurs in the following sequence: client microservices--pre-processing microservices--decision-making microservices--client microservices. Various methods are used in module placements in the iFogSim2 network simulator, including edgeward placement and others. Latency is measured in milliseconds, which represents how long it takes for the data to be transmitted from the source to the destination. The implication of latency in the fixed scenario is that latency is consistent; however, latency could be higher when the cloud computing layer is used for processing due to the proximity of the cloud layer to the device layer. On the other hand, the implication of the scalable scenario highlights the benefit of Fog computing processing and computation closer to the edge of the network and the device layer. Compared to the Cloud computing layer, the Fog computing layer is closer to the device layer, thereby optimising latency. The latency measurement is the time it takes for processing requests between the start point at the device, all the way to the edge devices, fog nodes, and Cloud data centre layer and back to the device start point.  
The following equation represents latency measurement:  
\[TL=\sum_{m}CAL \tag{3}\]_TL_--represents total latency, which is dependent directly on VM allocations in the Fog nodes where tasks are allocated for processing;  
m--represents the host for \"m\" servers;  
_CAL_--represents the total current average delay in each VM within the host.  
\[CAL=CC-ET \tag{4}\]  
_CC_--represents the simulator clock;  
_ET_--represents the execution time of the tuple.  
When the request is sent, the simulator clock CC records the simulation time when the device receives the response to the request, how long it took to execute the request, and how long it took the Fog node to execute the request when received; hence, the difference represents the total latency for each tuple. This process also includes all potential delays along the network pathway, such as queueing, propagation, transmission, and processing delays [55].