esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Internet of Things Power Meter for Industrial and Domestic Applications
## 4 Results
### Numerical Results
Calibrating each measured electrical quantity would also reduce the overall error (1.22%), but this optimization was not applied as we aimed to determine the device's real, unaltered performance. Another important factor that negatively affects the measurement error is the inability to synchronize the sampling moment of the power meters. Each device has a different average interval for the measured values. Moreover, delays in updating the measured data in the database are introduced by the communication protocols and conversion steps described in Section 3.2.3. Table 4 presents a set of reactive power measurement samples gathered from the industrial meters and our device. As can be observed, the values measured by our device, Janitza, and Siemens meters are very similar, while the data collected from the Phoenix Contact power meter show a greater deviation. This behavior results from the way the devices scale and provide the measured values through the Modbus protocol. Our device and the Janitza and Siemens meters provide the reactive power value unscaled, as a float variable, stored in two Modbus Holding registers. The Phoenix Contact Meter offers the value scaled as kVAr/100, stored as an Int32 variable in two Modbus Holding registers. Since we aimed to store the data in the same format in the database, we converted the value received from the Phoenix Contact meter into VAr. As can be observed in the first column of the table, after the conversion the decimals are lost in most of the cases, affecting the measurement precision. We also noticed that this meter updated the reactive power value less frequently than the other devices. All these behaviors can explain the differences between the measurements of this device and those of the other meters used, including our device. We consider these issues to be limitations of the Phoenix Contact device's firmware, which could not be updated as we have not identified a new available version.
###### Abstract  
Oxy-fuel combustion technology can capture carbon dioxide (CO\({}_{2}\)) in the large-scale and greatly lower nitrogen oxides (NO\({}_{x}\)) emission in coal-fired power plants. However, the influence of inherent minerals on NO\({}_{x}\) reduction still remains unclear and the impact of oxy-fuel combustion on the transformation of different nitrogen functional groups has yet to be fully understood. The present work aims to obtain a further understanding of the NO\({}_{x}\) reduction during oxy-fuel combustion using synthetic coals with pyrrolic or pyridinic nitrogen. Compared to pyridinic nitrogen, more of the pyrrolic nitrogen in synthetic coal was converted to NO\({}_{x}\). The conversion ratio of nitric oxide (NO) first increased significantly with the rising oxygen content and then trended to an asymptotically constant as the oxygen (O\({}_{2}\)) content varied between 10-50%. The nitrogen dioxide (NO\({}_{2}\)) formation was roughly proportional to the oxygen content. The NO\({}_{2}\) conversion was increased with particle size but the case of NO showed a non-monotonic variation. The catalytic effects of sodium carbonate (Na\({}_{2}\)CO\({}_{3}\)), calcium carbonate (CaCO\({}_{3}\)), and ferric oxide (Fe\({}_{2}\)O\({}_{3}\)) on the transformation of pyridinic nitrogen to NO were independent of the combustion atmosphere, while the alteration from air to the oxy-fuel combustion led to a change of mineral catalytic effect on the oxidation of pyrrolic nitrogen within the coal matrix.  
oxy-fuel combustion; NO\({}_{x}\) reduction; pyridinic nitrogen; pyrrolic nitrogen; synthetic coal; mineral effect
# Optimizing Airfoil Aerodynamic Characteristics by Using Proposed CSA-KJ Method
###### Abstract  
Combined with the cuckoo search algorithm (CSA) and the Kutta-Joukowski (KJ) theorem, a CSA-KJ optimization method was proposed to improve the airfoil aerodynamic characteristics in this work. The fourth-order constant-free polynomial function was employed to describe the airfoil profile. The KJ loop lift of the airfoil was taken as the objective function, and the CSA was applied to iteratively update this method, which was used to optimize the NACA4412 airfoil. The results demonstrate that the optimized effect of the CSA-KJ method on the lift-drag ratio becomes increasingly more significant with the increase of incoming wind speed, and it has the best performance at an angle of attack of 0\({}^{\circ}\). Compared with the NACA4412 airfoil, the average and the maximum lift-drag ratio coefficients of the CSA-KJ4412 airfoil have increased. Meanwhile, the pressure difference distribution is improved, and the aerodynamic characteristic is better. From this, it can be seen that the CSA-KJ method can provide an effective way to optimize the aerodynamic performance of other airfoils.  
Kutta-Joukowski theorem; cuckoo search algorithm; airfoil; aerodynamic optimization
# Energy Efficient Management of Pipelines in Buildings Using Linear Wireless Sensor Networks
###### Abstract  
The efficient and safe management of air conditioner (AC), Piped Natural Gas (PNG) and water pipelines in large buildings is a major challenge for the safety of these buildings. In recent years, Linear Wireless Sensor Networks (LWSN) are being used extensively for monitoring of long natural gas, water, and oil pipelines. LWSNs can also be used for efficient and safe management of AC, PNG and water pipelines in large buildings. In this paper, a scheme for optimal placement of sensors and base stations in a linear fashion to monitor the various pipelines used in large buildings has been proposed. The proposed scheme utilizes the Lion Optimization Algorithm (LOA) and has been compared with three strategies, namely Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Greedy Approach with respect to throughput, lifetime and end-to-end delay. The simulation results show that the proposed scheme exhibits better performance in comparison to the other three considered techniques for all the three parameters. The most striking feature of the proposed approach is that optimization is more effective when the length of the pipeline is more as far as end-to-end delay is concerned. The lifetime of the network is significantly improved using the proposed approach, especially when the length of the pipeline is of medium size, which makes the proposed scheme suitable for energy efficient buildings.  
meta-heuristic technique; node placement; routing; wireless sensor networks +
Footnote †: journal: Accepted: 30 July 2018; Published: 10 August 2018
## 3 Experimental Results
### Focus Measure
The spatial frequency method calculates focus metrics directly in the spatial domain by measuring intensity variations between rows and columns, eliminating the need for frequency-domain transformations. This spatial-domain approach still involves pixel-wise intensity comparisons and multi-step calculations, resulting in higher memory usage and the longest computational time. The proposed algorithm involves two Gaussian filtering operations to construct the difference of Gaussians image, followed by image decomposition and reconstruction. Its computational time is slightly higher than that of the Laplacian energy method but significantly lower than that of the spatial frequency method. The proposed focus measure algorithm, based on difference of Gaussians and Laplacian pyramid fusion, demonstrates significant advantages in focus information extraction for microscopic images through multi-scale feature fusion and noise suppression strategies. By reconstructing the top layer of the Laplacian pyramid using the high-frequency details enhanced by the difference of Gaussians image, the method retains the traditional Laplacian operator's sensitivity to cellular edges and textures, while the multi-scale Gaussian kernels effectively suppress noise interference. This approach addresses the shortcomings of traditional methods, such as noise amplification and the loss of low-frequency information, while also balancing computational time consumption. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Gray Level** & **Energy of** & **Energy of** & **Spatial** & **Proposed** \\
**Variance** & **Gradient** & **Laplacian** & **Frequency** & **Proposed** \\ \hline Time (ms) & 6.2 & 19.6 & 20.5 & 96.7 & 30.4 \\ \hline \end{tabular}
\end{table} 
Table 1: Computation time of gray level variance, gradient energy, Laplacian energy, spatial frequency, and proposed algorithm.
## 5 Research Challenges and Future Directions
### Multiple Cell Association  
Network distribution, load balancing, optimization metrics, and traffic models are all crucial to the success of user association schemes in HetNets. As a newer approach, multicell user association allows users to associate with numerous SBSs simultaneously, increasing their potential for both data throughput and system efficiency. Hence, more study into multicell association methods is required. Classifying users before and then associating them with the MBS or SBS to increase the efficacy of UA is another exciting study field [149]. Joint optimization with additional characteristics such as interference minimization, load distribution, and fairness are another research area where user association techniques may be improved.  
Implementing and conducting field testing of the suggested methods is crucial for determining their viability in real-world contexts. The appropriate technique may be used to implement the UA scheme. The field testing results also provide light on the real performance of HetNet in real-world applications and the actual performance improvement that a user may obtain by using coordinated multipoint transmission based on HetNet [150]. Moreover, in the case of actual situations where the network characteristics are unknown and alter over time, UA strategies and machine learning methods for online learning-based policies might be explored. Furthermore, real-world experiments should be used to prove the effectiveness of the queue-aware resource allocation strategy with hybrid coordinated multipoint under fronthaul constraints. Based on those mentioned above, further work is needed to construct experimental prototypes and real-world measurement-based analyses to verify the results of most theoretical investigations.
## 2 Stability Analysis of Haptic Interaction
### Virtual Spring-Mass  
If the haptic interaction consists of an elastic force with stiffness coefficient \(K\) plus an inertia with virtual mass \(M\), then the characteristic equation of this system will be a fourth-order polynomial:  
\[1+\left(K+M\frac{(z-1)^{2}}{T^{2}z^{2}}\right)\frac{z(e^{-\delta}-1+\delta)+1- (1+\delta)e^{-\delta}}{(z-1)(z-e^{-\delta})\frac{b^{2}}{m}}=0 \tag{29}\]  
\[T^{2}z^{2}(z-1)(z-e^{-\delta})\frac{b^{2}}{m}+\left[KT^{2}z^{2}+M(z-1)^{2} \right]\left[z(e^{-\delta}-1+\delta)+1-(1+\delta)e^{-\delta}\right]=0 \tag{30}\]  
Figure 3: Stability boundaries for virtual mass-damper effects.  
When applying the Routh-Hurwitz criterion, two conditions must be met at the same time for the system to be stable. The first is simply \(K>0\), although the second is a very long expression. When defining the following parameters,  
\[a_{0} =(e^{\delta}-1)KT^{3}b \tag{31}\] \[a_{1} =2(e^{\delta}-1)(T^{2}b^{2}-KT^{2}m)+2 KT^{3}be^{\delta}\] (32) \[a_{2} =2(e^{\delta}-1)(2 MTb-KT^{2}m)+2T^{2}b^{2}(3e^{\delta}-1)+2 KT^{3}b\] (33) \[a_{3} =2(e^{\delta}-1)(KT^{2}m-4 Mm)+2T^{2}b^{2}(3e^{\delta}+1)+8 MTb-2 KT^ {3}be^{\delta}\] (34) \[a_{4} =2(e^{\delta}-1)(4 Mm+KT^{2}m)+(e^{\delta}+1)(2T^{2}b^{2}-4 MTb-KT^{ 3}b) \tag{35}\]  
this second stability condition is:  
\[a_{1}a_{2}a_{3}-a_{4}a_{1}^{2}-a_{0}a_{3}^{2}>0 \tag{36}\]  
This analytical result is new in literature. Figure 4 shows the exact stability boundary of the haptic system in the (\(\alpha\), \(\gamma\))-plane. The abscissa axis for \(\alpha=0\) meets condition (21), while the ordinate axis for \(\gamma=0\) meets condition (9).  
In [18], the poles of this characteristic equation are numerically solved by substituting experimental values \(m\), \(b\), and \(\delta\) from different devices. Consequently, several experimental stability boundaries are obtained, although the exact analytical stability condition is not reported.
# Effect of Admixtures on Selected Properties of Fly Ash-Based Geopolymer Composites
## 1 Introduction
An increase (by approximately 20%) in the strength properties of fly ash and blast furnace slag-based geopolymer concrete is also observed with the use of liquefaction admixtures based on naphthalene in an amount of approximately 0.8% by weight of the binder [21]. [PERSON] et al. [22] tested the effect of naphthalene- and polycarboxylate-based superplasticizers in an amount ranging from 0.1 to 2.4% by weight of the binder. The contents of the admixtures were determined to achieve the appropriate fluidity. Both admixtures improved the workability of geopolymer slurries based on fly ash and slag powder. However, results can be found for geopolymer composites based on fly ash alone. [PERSON] et al. [23] investigated the effect of a polynaphthalene sulfonate (PNS) superplasticizer on the properties of fly ash and cement-based pastes. The admixture was added during mechanical activation of the fly ash (5% by weight of FA) or with the mixing water (0.5% by weight of binder). More favorable results were obtained by adding a superplasticizer during mechanical activation compared to the pastes with SP added by mixing water. [PERSON] et al. [24] found that the third-generation water reducer based on high-range polycarboxylic ether (1.5% by weight of fly ash) improved the workability of concrete based on fly ash, but with a lower molar mass (4 M) NaOH solution that was used. [PERSON] et al. [25] studied the effect of six types of superplasticizers (with a dose of 1% by mass of fly ash) on geopolymer pastes, two naphthalene-based, three modified polycarboxylate admixtures, and one melamine-based powder. Based on the results, it was found that the effect of admixtures depends on the type of superplasticizer. In all cases, there was a reduction in compressive strength. However, the most favorable results were obtained by using admixtures based on a modified polycarboxylate. Increased knowledge of the effects of the use of different admixtures in geopolymer composites will allow mixtures to be designed with selected properties. This is particularly important when producing geopolymers based on regionally available materials, especially local fly ash.
## 4 Technology
### Metering  
In order to efficiently implement a smart grid, a smart metering infrastructure is essential. Traditional metering devices, provided by energy distribution companies for their customers, typically measure energy consumption only in terms of total energy consumed. If they could also provide information about when and how energy is consumed, energy provision would be more intelligent, finely tuned to suit specific customer needs--and optimizing energy distribution over the entire network would be easier. In this context, the AMI (Advanced Metering Infrastructure) represents the reference for defining next generation metering technologies. AMI features include: [14,15]  
* Two way communication to the electric meter to enable information interchange;
* Self-registration of metering points;
* Auto-configuration after a failure in communications;
* AMI system interconnection to utility billing, outage management systems, and other applications.  
[PERSON] [14] names the integration between smart grid and AMI as AGI, which stands for \"Advanced Grid Infrastructure\". The AGI has the following enhancements [14]:  
* **Outage**: _Improved Customer Service \"Utilizing the AMI infrastructure, a utility can know when an outage occurs. The AMI system can notify the trouble call system automatically, facilitating rapid crew deployment and reduced outage times\"._
* **Loss Detection**: _Improved Network Operation \"By connecting information nodes at key points of the medium voltage distribution lines and distribution transformers, it is possible to directly calculate the system technical and non-technical losses. This enables better tracking and efficiency on the distribution network\"._
* **State Estimation**: _Integration of Renewable Sources \"By utilizing information from the customer site, medium voltage lines, and transformers, accurate load models can be computed allowing accurate load estimation on the distribution grid. This information is critical to understanding the impact and benefit of connecting renewable energy sources to the distribution grid\"._  
For further information, [PERSON] _et al._[16] give a more detailed view of the AMI infrastructure.
## 4 Results and Discussion
### Estimation of the Measurement Uncertainty
#### 4.1.2 Direct-Energy-Loss Method Measurement Uncertainty  
With \(\Delta Q_{loss}\) found according to the formulas in Section 2.2, the measurement error for the direct-energy-loss method is calculated as follows (Equations (12) and (13)),  
\[|\Delta R_{i}|=\left|\frac{\partial R_{i}}{\partial Q_{loss}}\right|\Delta Q_ {loss}+\left|\frac{\partial R_{i}}{\partial I}\right|\Delta I_{m} \tag{12}\]  
\[|\Delta R_{i}|=\left|\frac{1}{I_{m}^{2}t_{m}}\right|\left(\left|I_{chg}t_{chg} \right|\Delta U_{chg}+\left|U_{chg}t_{chg}\right|\Delta I_{chg}+\left|I_{dchg} t_{dchg}\right|\Delta U_{dchg}+\left|\frac{-2Q_{loss}}{I_{m}^{3}t_{m}} \right|\Delta I_{m} \tag{13}\]  
The time \(t_{m}\) stands for the duration of the current flow during measurement and is constantly 900s. The current \(I_{m}\) is the amount of current based on the selected C-rate. The duration of charge time \(t_{clg}\) and discharge time \(t_{ddg}\) are always equal. \(U_{chg}\) and \(I_{clg}\) represent the charging voltage and the charging current, while \(U_{ddg}\) and \(I_{dchg}\) represent the discharging voltage and the discharging current.
# A Novel Connectivity-Based LEACH-MEEC Routing Protocol for Mobile Wireless Sensor Network
## Proposed Framework
### Mobile Sensor Distribution  
We consider that the \(N\) sensor nodes are deployed randomly in a square field (100 \(\times\) 100 m\({}^{2}\)) following Gaussian distribution method. Equation (1) defines the probability density function (PDF) of node distribution.  
\[f(N_{xi},N_{yi})=\frac{1}{2\pi\sigma_{N_{xi}}\sigma_{N_{yi}}}e^{-\left(\left( \frac{(N_{xi}-N_{xi})^{2}}{2\sigma_{N_{xi}}^{2}}\right)+\left(\frac{(N_{yi}-N_{ yi})^{2}}{2\sigma_{N_{yi}}^{2}}\right)\right)}, \tag{1}\]  
where \(N_{xj}\) and \(N_{yi}\) are the positioning point, and \(\sigma_{N_{xi}}\) and \(\sigma_{N_{yi}}\) are the standard deviations for \(N_{xi}\) and \(N_{yi}\) dimension, respectively[45]. Each sensor node follows a normal distribution with different means and variances, due to different locations and random movement dependent upon mobility models, as shown in Figure 1. Let \(N_{xj},N_{yj}\) deployment point be (0,0). Then, Equation (1) can be re-written as,  
\[f(N_{xi},N_{yi})=\frac{1}{2\pi\sigma_{N_{xi}}\sigma_{N_{yi}}}e^{-\left(\left( \frac{(N_{xi})^{2}}{2\sigma_{N_{xi}}^{2}}\right)+\left(\frac{(N_{yi})^{2}}{2 \sigma_{N_{yi}}^{2}}\right)\right)} \tag{2}\]
## 2 Testbed Setup and Measurement Methodology
### Experimental Setup for Available Bandwidth Estimation
#### 2.2.2 Network Topology for AvB Estimations  
Experiments on AvB estimation with different types of timing acquisition and with different load profiles are performed on a testbed shown in Figure 3.  
The laboratory environment of a 10 Gbps network consists of:  
* a Netropy 10G WAN Emulator, for emulating wide area network using different inferences such as packet loss ratio up to 100%, delays of up to 100 s, and various types of packet corruption and duplication;
* two instances of servers used for the cross traffic for an additional impact on AvB estimation equipped with 12\(\times\) Intel Xeon(r)CPUX5690 3.47 GHz, 32 GB RAM, and 10G Chelsio NIC;
* one instance of server used for measurement traffic in the 10G network, equipped with 6\(\times\) Intel(r) Xeon(r);
* one instance of a physical server with VMs described in Section 2.1.2, periodically hosted VMs with XEN, QEMU, VirtualBox and ESXI equipped with a 1 GE Intel I-219 NIC; and  
Figure 3: AvB Network topology.  
* 10 GE network switches Summit x650 from Extreme Networks.
## 2 Validation, Evidence Based on Response Processes  
Validation can be defined as the process of collecting evidence to support specific interpretations and uses of test scores. In the framework provided by the American Educational Research Association, the American Psychological Association, and the National Council on Measurement in Education [33], five sources of evidence were identified, as follows: (1) test content; (2) RPs; (3) internal structure; (4) relations to other variables; and (5) consequences of testing. Moreover, evidence based on test content is complemented with evidence based on RPs.  
According to [PERSON] and [PERSON] [34], \"RPs refer to approaches and behaviors of examinees when they interpret assessment situations and formulate and generate solutions as revealed through verbalizations, eye movements, response times, or computer clicks. Such response process data can provide information about the extent to which items and tasks engage examinees in the intended ways\". [PERSON] and [PERSON] [35] defined RPs as \"the mechanisms that underlie what people do, think, or feel when interacting with, and responding to, the item or task and are responsible for generating observed score variation\".  
Validity evidence based on RPs involves understanding how examinees interact with a test and whether they engage the intended cognitive processes when answering items. Empirical data are needed to confirm this. RPs offer valuable insights into examinees' performance, helping test designers reduce the gap between observed responses and the constructs being measured. Although not a new area of research, the study of cognitive processes used by examinees in different tasks has a long history (e.g., [36]). Collecting this evidence involves various methods such as think-aloud interviews, cognitive interviews, keystroke and mouse click analysis, ET techniques, and RT analysis [33].
## 4 Communication Technologies for Seamless Connectivity in Smart Farming
### Availability and Challenges of Communication Technologies
#### 4.2.2 LPWAN Technologies
* **Sigfox:** Sigfox [70] is a LPWAN technology that takes advantage of Ultra-Narrow-Band modulation (UNB) which decreases the levels of noise then the communication rate will increase. It is proper for the lightweight and low data rate IoT based devices. The typical structure of the modulation is Binary Phase Shift Keying (BPSK). In addition, the down-link and up-link communications of each device are limited by Sigfox. Moreover, many countries have used Sigfox and there is no roaming involved.