esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Transfer Reinforcement Learning with Space Decomposition
### Knowledge Update  
According to the learning mechanism of Q-learning, the knowledge matrix can be updated based on the executed state-action pair with the feedback reward. By combining the space decomposition, the knowledge matrix of each searching space layer can be updated as [28]:  
\[\begin{split}\mathbf{Q}_{i,k}^{l}\Big{(}s_{i,k}^{l},a_{i,k}^{l}\Big{)} =&\mathbf{Q}_{i,k}^{l}\Big{(}s_{i,k}^{l},a_{i,k}^{l}\Big{)}\\ +& a\Bigg{[}R_{i,k}^{l}\Big{(}s_{i,k}^{l},s_{i,k+1}^{ l},a_{i,k}^{l}\Big{)}+\gamma\underset{a\in A_{i}^{l}}{\text{max}}\mathbf{Q}_{i,k}^{l} \Big{(}s_{i,k+1}^{l},a\Big{)}-\mathbf{Q}_{i,k}^{l}\Big{(}s_{i,k}^{l},a_{i,k}^{l} \Big{)}\Bigg{]}\end{split} \tag{8}\]  
where \(\mathbf{Q}_{i}^{l}\) represents the knowledge matrix of the \(l\)th layer' searching space for the \(i\)th controllable variable; \(\Big{(}s_{i,k}^{l},a_{i,k}^{l}\Big{)}\) is the state-action pair executed at the \(k\)th iteration, with \(k=1,2,\,\ldots,\,k_{\text{max}}\); \(k_{\text{max}}\) represents the maximum iteration number; \(\alpha\) is the knowledge learning factor, with \(\alpha\in(0,1)\); \(\gamma\) denotes the discount factor, with \(\gamma\in(0,1)\); \(R_{i}^{l}\) is the reward function; and \(\mathbf{A}_{i}^{l}\) means the action space of the \(l\)th layer's searching space, respectively.  
It can be seen from Equation (8) that at each iteration, only one element of each knowledge matrix can be updated since the conventional Q-learning employs a single RL agent for exploration and exploitation in a dynamic environment. Consequently, it will lead to a slow learning rate; thus a high-quality optimal solution cannot be rapidly obtained for a real-time control of PV systems. Hence, a cooperative swarm is employed to further accelerate the learning rate, as it can simultaneously update multiple elements of each knowledge matrix with multiple state-action pairs. Similar to (8), each knowledge matrix of TRL can be updated by [30]  
\[\begin{split}\mathbf{Q}_{i,k}^{l}\Big{(}s_{i,k}^{l,m},a_{i,k}^{l,m} \Big{)}&=\mathbf{Q}_{i,k}^{l}\Big{(}s_{i,k}^{l,m},a_{i,k}^{l,m}\Big{)} \\ &\quad+\alpha\Bigg{[}R_{i,k}^{l,m}\Big{(}s_{i,k}^{l,m},s_{i,k+1}^ {l,m},a_{i,k}^{l,m}\Big{)}+\gamma\underset{a\in A_{i}^{l}}{\text{max}}\mathbf{Q}_{ i,k}^{l}\Big{(}s_{i,k+1}^{l,m},a\Big{)}\\ &\quad-\mathbf{Q}_{i,k}^{l}\Big{(}s_{i,k}^{l,m},a_{i,k}^{l,m}\Big{)} \Bigg{]},\qquad m=1,\,2,\,\ldots,\,M.\end{split} \tag{9}\]  
where \(M\) represents the population size of the cooperative swarm.
## 1 Introduction
Mobile crowd sensing is one of the significant paradigms that plays a major role in mobility, especially for urban environments. Present day mobile devices are designed with a variety of sensors that can be used to develop most essential applications for urban environments using crowd sensing. Some of the applications are traffic control and monitoring, pollution control, crime control, and helping to develop a better platform to solve the issues related to pandemic diseases like COVID-19, etc. In our research work, we are proposing a feasible model for mobile crowd sensing with better quality of Information (QOI) for sensing and communication. This work considered significant parameters that effectively improve the QOI. We have developed an efficient mechanism to categorize the mobile users for crowd sensing who can be involved in different applications that demand different levels of QOI. We identified the most relevant and effective parameters such as reliability based on distance (\(R_{bd}\)), transmission range to sink (\(T_{ri}\)), time required to send to sink/destination (\(T_{ri}\)), and quality of sensor devices used in mobile devices (\(M_{qs}\)). These parameters introduce remarkable QOI for various applications of crowd sensing. This paper is organized as follows. A literature review is presented in Section 2. Section 3 presents the methodology. The detailed performance evaluation is presented in Section 4. The conclusions are given in Section 5.
# Time Modulated Arrays: From their Origin to Their Utilization in Wireless Communication Systems
###### Abstract  
Time-modulated arrays (TMAs) are electromagnetic systems whose radiated power pattern is controlled by the application of variable-width periodical pulses to the individual elements. The nonlinear nature of the array operation causes the appearance of radiation patterns at the harmonic frequencies of such periodic pulses. The technique can be used for improving the side-lobe level (SLL) topology of the radiation pattern at the central frequency and/or to profitably exploit the harmonic patterns in order to supply smart antenna capabilities. Among the latter features, the TMA harmonic beamforming takes on special importance due to its attractive trade-off performance-hardware complexity. From this perspective, TMAs are sensors capable of transforming the spatial diversity of a communication channel into frequency diversity, thus improving the performance of a wireless communication. In addition to a walk through the origins of the concept, and a brief analysis of the mathematical fundamentals, this paper organizes the prolific state of the art of TMAs in two major thematic blocks: (1) TMA design from an antenna perspective; and (2) TMA design from a signal processing perspective.  
antenna arrays; digital communications; time modulated arrays +
Footnote †: journal: Sensors
# A Review on Food Safety: The Case of _Citrobacter_ sp., Fish and Fish Products
## 4 _Citrobacter_ sp.  
The _Citrobacter_ genus is found within the so-called coliform bacilli, along with the _Escherichia_, _Klebsiella_, _Enterobacter_, and _Serratia_ genera, which include overt and opportunistic pathogens responsible for a wide range of infections [35; 36; 37]. The genus _Citrobacter_ spp. are bacilli belonging to the _Enterobacteriaceae_ family and are Gram negative, 0.3-1.0 \(\upmu\)m wide \(\times\) 0.6-6 \(\upmu\)m long, flagellated, aerobic, not sporulated, without capsule, facultative anaerobs, methyl red and catalase positive, fermenters (glucose and lactose), gas producers, citrate positive (due to their ability to use citrate as their only carbon source and to which they owe their name), capable of reducing nitrate to nitrite, lysine decarboxylase, Voges-Proskauer and oxidase negative. They also have an optimal growth temperature of 37 \({}^{\circ}\)C, are considered members of the normal intestinal microbiota of humans and animals, and can be isolated from various environmental sources (water, wastewater, soil, and food) [21; 35; 36; 37; 38; 39; 40; 41; 42; 43]. These bacteria, like other enterobacteria, have specific differentiation antigens, such as \"O\", which are lipopolysaccharideaccharides, or cell-wall proteins; \"H\" antigens, which are flagella proteins that can act as virulence factors, standing out lipopolysaccharide saccharide with endotoxin activity, in addition to \(\alpha\)-hemolysin cytotoxoxins and enterotoxins [44].  
The genus Citrobacter comprises 11 species: _Citrobacter freundii_; _Citrobacter aseri; Citrobacter amalonaticus_; _Citrobacter farnei_; _Citrobacter sallakii_; _Citrobacter youngae_; _Citrobacter reontium_; _Citrobacter gillenti_; _Citrobacter junthiaae_[37; 41; 43]. From which _Citrobacter freundii_, _C. diversus_, and/or _C. amalonaticus_ have been associated with various infections of the gastrointestinal system, urinary tract, respiratory tract, wounds, intra-abdominal area, bone, respiratory tract biliary (calculi or obstruction), surgical wounds, and central nervous system. They are also able to lodge in other organs and tissues [21; 36; 41; 45].  
_Citrobacter_ is also associated with cases of septicemia that can occur in patients with multiple predisposing factors, such as being immunocompromised; they can also cause meningitis, septicemia, and lung infections in newborns and young children [35; 44; 46]. Infections by _Citrobacter_ sp. can be fatal at an estimated 33-48% overall mortality rate, including a 30% mortality rate for children. The most prone population are children, people with immunodeficiency, and the elderly [21]. Therefore, these bacteria are considered an important issue in public health, labeled as opportunistic pathogens in the incidence of foodborne diseases [21; 43].
# A Placement Method of the 5G Edge Nodes Based on the Hotspot Distribution of Mobile Users
## 1 Introduction
### Our Contributions  
For rapidly deploying the 5G edge networks, the first addressed problem is how to select the placement location of 5G edge nodes. Because 5G network architecture is more complex, there are more factors to be considered when 5G edge nodes are configured. How to deploy 5G edge nodes reasonably with low cost and high service quality is an NP-hard problem.  
Considering the relationship between the 5G edge node and user hotspot, this paper proposes a placement method for 5G base stations. Our major contributions include:  
1. We first extract the user trajectory data from the given area and cluster all locations from these trajectories to get the cluster areas.
2. We calculate the characteristics, such as the number of users and duration time in each cluster area, and extract the hotspots from all cluster areas based on the threshold of the number of users and the threshold of the time slices.
3. We define two parameters, the high load utilization rate of the base station and the bandwidth reduction rate of the core network, and take the weighted sum of two parameters as the optimization objective to build a mathematical model and design a greedy algorithm to solve this model.  
The remainder of this paper is organized as follows. Section 2 gives our placement framework of the 5G edge nodes. Section 3 describes the extraction algorithm of cluster areas based on DBSCAN, the hotspot extraction algorithm based on the sliding time window, and the location selection algorithm of the 5G base station based on the hotspot and benefit constraints. In Section 4, we conduct a series of experiments to evaluate our proposed methods. Section 5 provides the conclusions.
## 4 Discussion  
### Algorithm  
We introduced an automated accelerometer-based movement reduction algorithm (AMARA) that combines MARA, developed by our group [34], and integrated ideas from [PERSON] _et al._[63]. Furthermore, AMARA adds new features to the artifact detection and reconstruction process. MAs were detected by the MSD, as in MARA. In addition, movements within the accelerometer signal were detected in a similar way. This method of movement detection is different to the one used by [PERSON] _et al._[63], which determined whether the acceleration change exceeds 1.3 g/s (implemented here as 0.2-0.55 g/s). This definition assumes that artifacts in the NIRS signal are only related to relatively fast movements. Artifacts due to slow movements were neglected, e.g., when the subject slowly rotated the head and then was lying on the sensor. This induced changes in light coupling due to a different pressure on different parts of the sensor or due to movement of the whole sensor. By calculating the MSD of the acceleration signal, any kind of movement could be detected, if it exceeded the noise level (see Step 2, Section 2.1.1 Movement detection). A threshold for the acceleration change was not needed. Instead, automated threshold determination was achieved by the Zack triangle algorithm [64]. This dramatically reduced the time effort to find the optimal threshold values and enabled automatic adaptation of the threshold throughout a recording. The automated threshold detection was particularly practical for long-term recordings such as our sleep measurements, where the noise level of the signal (and hence the MSD) varied during the recording. For example, we often observed that the respiration component of the NIRS signals depended on the position of the subject. In one position, the sensor may be freely attached to the subjects' head while, in another one, the sensor may be trapped between the subjects' head and the bed. In the latter case, the amplitude of the respiration signal was larger. Consequently, the noise floor in the MSD was increased. Therefore, it was not possible to set an optimal constant threshold for an entire recording. Either smaller MAs were missed or the number of false positives increased when the threshold was set too low. On the other hand, AMARA may have missed small MAs close to large MAs, depending on the parameters applied (MSD window length (\(2q+1\),) moving Zack triangle window length (\(w_{size}\)) and step size (\(w_{step}\))). The reason is that the threshold was calculated for 5 min windows and large MAs in the MSD would raise the threshold and therefore mask smaller MAs. This could be corrected by applying smaller windows, which would, however, lead to problems with the detection of large artifacts.
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
###### Abstract
Nanogrids take the concept of local energy generation, storage, and consumption one step smaller by drawing the system boundary around only a few or even just one load and associated generation and storage equipment. Some nanogrid implementations (and of most interest to militates) are connected to microgrids via one or more switches that allow a nanogrid to share its generation and storage capabilities with the larger microgrid during normal operations, and disconnect from the microgrid to preserve the ability to serve local loads during a microgrid disruption. DoN and other navies have experience operating shipboard power systems that are similar in scope and goal to nanogrids in the form of zonal distribution systems that localize generation and consumption of power aboard a ship. One of the goals of zonal distribution is providing uninterrupted power to critical loads and/or zones in a ship so that the ship's mission may continue even after a disruptive event (e.g., equipment failure, battle damage and compartment flooding, cyber-attacks, etc.) has occurred. The surface Navy has implemented zonal distribution systems where Direct Current (DC) zonal shipboard electrical systems are configured on ships with the sole purpose of ship survivability which is a similar concept to resilience in shore-side electrical systems. System survivability of zonal distribution focuses on detection and isolation of disturbances which is done by reconfiguration management [11]. Because of the success of zonal distribution, DoN and DOD are both interested in implementing the concept ashore using nanogrids. Specific ContributionsThis paper contributes a systems engineering method to analyze existing military installation (e.g., a DoN ashore installation, an Army base, etc.) microgrid infrastructure from the perspective of improving energy resilience for critical loads specifically usingnanogrid technology. The method helps to elucidate potential benefits of targeted nanogrid deployments to critical loads at military installations which are important to national security. Additional benefits may be realized by implementing nanogrids such as improving installation energy independence among others.
## 3 Analysis and Results
### Quantitative Analysis
#### 3.1.1 Comparative Analysis of Investment Cost
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **BIM Task** \\ **Categories** \\ \end{tabular} } & \multirow{2}{*}{**Code**} & \multirow{2}{*}{**Descriptions**} & \multicolumn{2}{c}{**Non-BIM (Unit: USD)**} & \multicolumn{2}{c}{**BIM (Unit: USD)**} \\ \cline{3-6}  & & & **Project 1** & **Project 2** & **Project 1** & **Project 2** \\ \hline \multirow{2}{*}{\begin{tabular}{c} 5D Simulation of \\ Cost Estimation \\ \end{tabular} } & \multirow{2}{*}{e} & \multirow{2}{*}{Labor Costs} & \multirow{2}{*}{61,976} & \multirow{2}{*}{51,646} & \multirow{2}{*}{12,395} & \multirow{2}{*}{17,600} \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} BIM Coordination in \\ Construction Projects \\ \end{tabular} } & \multirow{3}{*}{f} & Costs of Resolving Clashes and Preventing Delays & \multirow{3}{*}{45,965} & \multirow{3}{*}{24,445} & \multirow{3}{*}{30,643} & \multirow{3}{*}{15,838} \\  & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} BIM Coordination Projects \\ \end{tabular} } & \multirow{3}{*}{g} & Costs of Resolving Clashes and Preventing Delays & \multirow{3}{*}{45,965} & \multirow{3}{*}{24,445} & \multirow{3}{*}{30,643} & \multirow{3}{*}{15,838} \\  & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Total \\ Average total cost \\ \end{tabular} } & \multirow{3}{*}{37,185} & \multirow{3}{*}{66,623} & \multirow{3}{*}{24,790} & \multirow{3}{*}{44,415} \\ \cline{1-1} \cline{5-5}  & & & & & \\ \hline \hline \end{tabular}  
* Note: The measured amount, converted to US dollars based on real-time exchange rates, may incur discrepancies.  
\end{table} 
Table 4: Comparative measurement cost between participation in BIM consultancy and non-BIM participation. \"Cost Measurement with BIM Consulting,\" each cost is defined as an investment cost in the construction phase. In \"Cost Measurement by Contractors,\" each cost is defined as a basic cost in the construction phase. The percentage increase in costs can be calculated by dividing basic costs by investment costs. As shown in Table 5, the investment percentage for Project 1 is 214.0%, resulting in a cost efficiency improvement of 114.0%. For Project 2, the percentage is 183.3%, leading to an 83.3% improvement in cost efficiency. In terms of the average total costs, with the participation of a BIM consulting company, the calculated average investment cost in the construction phase is approximately USD 72,840, including labor costs, costs of resolving collision issues and preventing delays, and rework costs when design changes and errors occur. On average, if constructed solely by contractors, the average basic cost in the construction phase is approximately USD 143,920. When a professional BIM consulting company is involved, the average total investment cost percentage is 197.6%, resulting in a cost efficiency improvement of about 97.6%.
# Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades
## 1 Introduction
### Approach and Objective  
In our approach, we focus on establishing so-called _vibration-based fingerprints_ along different positions of a turbine blade. By mounting accelerometers on the outside of the blade, local vibrations can be monitored at any position. In addition, mounting restrictions due to support structures inside the blades can be avoided. Since actuator mounting is not feasible for existing turbines, we only consider wind excitation. Thereby, our method shall be applicable to existing turbines in retrofit. Vibration measurements at multiple positions along the blade are then used to extract characteristic vibration-based fingerprints, as visualised in Figure 1.  
An analysis of spectrograms was proposed by [PERSON] et al. [23] for the analysis of laser-vibrometer measurements. Dominant frequencies were extracted for a four-cylinder engine during different operational conditions. Their method was extended [24] by using a moving average predictive method to consider the history of vibrations. Both methods worked well for detecting homogeneous changes in spectra; however, a strategy for  
Figure 1: Proposed approach: The local vibration characteristic of the blade is measured by distributed sensors on the blade (**left**). Vibration measurements (**middle**) are used to extract characteristic fingerprints with reference to a reference variable, e.g., rotation speed (**right**).  
identifying inhomogeneous changes is missing, i.e., a dynamic change versus a constant behaviour for adjacent frequencies.  
In this study, we propose a method for identifying dominant frequencies in vibration spectra for both homogeneous and inhomogeneous frequency shifts. Extracted dominant frequencies represent both eigenfrequencies and locally increased vibrations, and thereby form characteristic vibration-based fingerprints. An algorithm for efficiently analysing those vibration-based fingerprints is developed and allows for comparing vibrational characteristics at different positions along the blade.  
The remainder of this paper is organised as follows. The sensor system and the measurement campaigns are described in Section 2. The proposed algorithm is presented in Section 3, including the detection of vibrations with low and high operational variability in Sections 3.3 and 3.5, respectively. The results from two test turbines are presented in Section 4. The results are discussed and conclusions are drawn in Sections 5 and 6, respectively.
# Cluster-Based Transmission Diversity Optimization in Ultra Reliable Low Latency Communication
## 3 System Architecture
### Path Loss Model  
The signal power propagation in an intra-vehicular environment frequently exposes some losses. Establishing an accurate model for path loss precision is challenging. Power in tier architecture, subcarrier bandwidth characteristics, antenna size, and distances from sensors to the receiver are observed. Considering all of these challenges, we choose a simplified path loss model [47] and calculate the received power for the 200 bits data packets. Transmission power \(P_{TP}\) has received \(P_{RP}\) amount of power in dBm unit by selecting a fixed path loss exponent. The received power equation can be expressed as:  
\[P_{RP}=P_{TP}\times C\times\left(\frac{d}{d_{0}}\right)^{\gamma} \tag{17}\]  
Here, \(d_{0}\) is the antenna far-field reference distance and \(d\) is the transmission distance. Due to scattering occurrences in the antenna near-field, the model is only valid at transmission distance, \(d\ referreddistance\), \(d_{0}\), where \(d_{0}\) is generally implied to be 1-3 m for an environment such as ours. Furthermore, \(\gamma\) is the path loss exponent, and it can vary from 1.6\(\sim\)3.3. Considering free-space medium and intra-vehicular environments in [48], they take \(\gamma=2\) and \(d_{0}=1\) m as referred distance. Here, \(C\) is the average channel attenuation, having a constant value for a particular bandwidth. The channel characteristic has an impact on the average channel attenuation. For inexact empirical measurement techniques, set \(C=1\) to the free space path gain at a distance \(d_{0}\), to assume omnidirectional antennas:  
\[C=20\ log_{10}\ \frac{\lambda}{4\pi d_{0}} \tag{18}\]  
Here, \(\lambda\) is the wavelength. The average channel attenuation is \(-47.87\) dB for the 5.9 GHz band. As there is always some noise \(u\) in analog signal transmission, we consider a \(u\) of 0.1\(\sim\)1 dB range. So the dBm attenuation can be expressed for received power as:  
\[P_{RP}=P_{TP}+C-10\times\gamma\times log_{10}\ \bigg{[}\frac{d}{d_{0}}\bigg{]}-u \tag{19}\]  
For a vehicle length of 3 m, the transmit power can be varied from \(-\) 5 dBm\(\sim\)\(-\)20 dBm. We evaluate a cutoff value of \(-84\) dBm received power at the highest 3 m distance, considering the path loss exponent \(\gamma=2\). If the packet has less power than the cutoff value, the receiver will discard the packet immediately.
## 1 Introduction
To overcome the abovementioned challenges, this paper proposes an RBDO technique that incorporates the complex-step (CS) derivative approximation into the SORM for the sensitivity analysis. An overview of the structural reliability, FORM/SORM, and RBDO is presented, followed by an explanation of the proposed RBDO/CS procedure. The performance and applicability of the proposed method are demonstrated by numerical examples.