esa-sceva/satcom-chunk-collection huggingface dataset:
# On Edge Pruning of Communication Networks under an Age-of-Information Framework
## 6 Periodic Case  
In this section, we consider the case at which link \(i\) gets activated at a certain frequency with a time period \(\tau_{i}\), where the time periods are always integers.  
We will represent the activation signal with a time period of \(\tau\) by the activation function \(g(\tau)\). This means that we can represent the activation function \(g\) as following:  
\[g(\tau)=\Sigma_{n=0}^{\infty}\delta(t-n\tau) \tag{25}\]  
where \(\delta(.)\) is the Dirac delta function [36].  
This will yield the following cases of reduction in the network:  
1. When we have multiple links in parallel, they may be reduced to one link with an activation signal that is simply the summation of the activation signals of all the links. The following equation represents the aggregated function in the parallel case. \[g(\tau_{\text{total}})=g(\tau_{1})+g(\tau_{2})+\cdots+g(\tau_{n})\] (26)
2. When we have multiple signals added together in series, the reduced link of all of them is the activation function of the least common multiple (LCM) of the time periods of the links. The following equation represents the aggregated function in the series case. \[g(\tau_{\text{total}})=g(LCM\{\tau_{1},\tau_{2},\ldots,\tau_{n}\})\] (27)  
Those simple reductions result in the possibility of pruning the links in the deterministic case depending on their activation functions. For example, in Figure 4, the fourth link is activated in a periodic way by which the activation occurs at time \(t\), where \(t\) is an integer multiple of 2 or an integer multiple of 3. This results in the case that the fourth link will be activated always at the time at which the first or the second link is activated.  
Theorem 4.1: _In the periodic case, we may reduce any SP network into one link using Equations (26) and (27). This reduction can be used to determine the weakest link in the network._  
Proof of Theorem 4.1.: In this case, the delivery times of the messages an SP network supports at each time are independent Bernoulli-distributed random variables as well characterized with an average of \(\tau\) that is the result of applying both the parallel and series reductions.  
Furthermore, a message waiting at the source node will be transmitted to the destination node instantly once the first one of the \(n\) links gets activated. That is it, the distribution of the wait time, in this case, will follow the distribution \(\min\{X_{1},X_{2},\ldots,X_{n}\}\), where \(X_{i}\) is the distribution of the wait time for link \(i\) characterized with Equation (26).  
However, in the case of series connection, the distribution of the wait time will follow the distribution \(X_{1}+X_{2}+\cdots+X_{n}\), where \(X_{i}\) is the distribution of the wait time for link \(i\) characterized with Equation (27). Including both the series and parallel cases would include all the possible cases since we restricted the study to SP graphs. This reduces directly to the ability to reduce the whole network into one link.
# Narrowband Internet of Things (NB-IoT):
## 2 Narrowband-IoT Standard and Releases
### Release 15 Enhancements
#### 2.3.2 Semi-Persistent Scheduling  
To enable better support of voice messages for the corresponding use cases, in Release 15, Semi-Persistent Scheduling (SPS) feature is introduced. In general, SPS is comprised of persistent scheduling for initial transmissions and dynamic scheduling for retransmissions. The base station assigns specific resource units to be used for NB-IoT UE voice messages with specific interval to save control plane overhead and hence optimize the radio resource usage. By principle, the base station preconfigures the UE with the Radio Network Temporary Identifier (SPS-RNTI) which is used to specifically differentiate one NB-IoT UE from another, or one radio channel from another. This SPS enables the NB-IoT data reception at a regular configured periodicity.
## 2 Results and Discussion
### Volatile Compounds  
Volatiles identified and semi-quantified in the four cherry cultivars using SPME are given in Table 2. A total of 18 compounds belonging to the groups: alcohols, aldehydes, ketones, hydrocarbons/terpenes and esters were identified and semi-quantified using 4-methyl-2-pentanone as the internal standard. Almost all compounds identified have been reported previously in fresh sweet cherry fruit [3; 5; 10; 16; 21]. Carbonyl compounds showed the most abundant signals present in sweet cherry aroma corresponding to amounts ranging from 14.75 \(\upmu\)g/kg in the Lapins cultivar to 34.62 \(\upmu\)g/kg in the Ferrovia cultivar. Carbonyl compounds identified were linear and aromatic. The most abundant carbonyl compound was 2-propanone followed by 2-hexenal and acetaldehyde. 2-Propanone ranged from 5.15 \(\upmu\)g/kg for the Lapins cultivar to 15.12 \(\upmu\)g/kg for the Ferrovia cultivar. 2-Hexenal ranged from 2.16 \(\upmu\)g/kg for the Canada giant cultivar to 10.39 \(\upmu\)g/kg for the Skeena cultivar. Finally, acetaldehyde ranged from 2.27 \(\upmu\)g/kg for the Skeena cultivar to 5.55 \(\upmu\)g/kg for the Ferrovia cultivar. Carbonyl compounds are known to be among the most important aroma compounds of sweet cherry fruit [5; 10; 16; 21]. 2-hexenal and hexanal are known as \"green leaf volatiles\" and have a very low perception threshold [22]. In contrast to the results of [PERSON] _et al._[3] 2-propanone was the most abundant volatile compound identified in all four cherry cultivars. Alcohols gave the second most abundant signals, corresponding to amounts ranging from 5.56 for the Ferrovia cultivar to 22.21 \(\upmu\)g/kg for the Skeena cultivar. Alcohols identified were linear, branched and aromatic. The most abundant alcohol was ethanol: 7.99 \(\upmu\)g/kg in the Canada giant cultivar, 6.98 \(\upmu\)g/kg in the Skeena cultivar, 6.66 \(\upmu\)g/kg in the Lapins cultivar and 3.59 \(\upmu\)g/kg in the Ferrovia cultivar. In a similar study, [PERSON] _et al._[3] identified (_E_)-2-hexen-1-ol as the main alcohol present in Picato type and Sweetheart sweet cherries in Spain. 2-Hexen-1-ol was also identified in the present study in rather large amounts (7.57 \(\upmu\)g/kg) only in cherries of the Skeena cultivar. 2-Hexen-1-ol is related to green notes and fresh green odours associated with fruit and vegetables [23]. Along with hexanal and 2-hexenal they predominate flavour volatiles in sweet cherries [16]. Of the aromatic alcohols, benzyl alcohol was found only in the Skeena cherry cultivar. This compound may thus, be used as a marker for the differentiation of the Skeena cultivar from the rest three cultivars. Esters were found in very small concentrations in all cherry cultivars. Finally, other minor compounds such as alkenes (2-methyl-1,3-butadiene) and terpenes (D-limonene) were found in very small amounts in individual cherry cultivars.
## 4 Discussion
Delamination and microscopy tests indicated that a range of EP and AP values could be adequate to procure a good glue line quality, which is directly related to adhesive viscosity, as it was mentioned above. In this study, it can be established that no adhesive that penetrates deeply into wood will have a better effect of fixing or strong glue bond. The quality of the glue line bond depends on several properties such as the wood density, physical and chemical wood properties, adhesive characteristics, pressing cycle (temperature and time), and use conditions, among others [8; 69]. It is important to mention that adhesive penetration must be enough, which means an overpenetration can affect negatively the glue line: this is why finding an adequate penetration range is very relevant to procure a good-quality bond between the adhesive and substrate [70]. At pressing times of 60 min and 120 min, it must be indicated that wood specimens glued with adhesive samples in dry conditions showed an expected trend, since a longer pressing time led to higher strength values. While wood failures were obtained, tests that involved high temperature and humidity (elevated temperature and vacuum-pressure conditions) did not present this pattern. At elevated temperature and vacuum-press test, all strength shear and wood failure values were smaller at 120 min pressing time than 60 min. This could be explained due to the elastic recovery of wood, which can be observed as \"shape memory effect\" or \"spring-back\". Even though both \"shape memory\" and \"spring-back\" effects are similar, the first one occurs when a piece of wood was already deformed (environmental factors). After a pressing period, wood can keep a new shape for a while as long as the environment stays the same as the beginning, which means dry conditions [71]. When conditions change, wood tends to recover its original shape [72]. The second one happens specifically in wood boards, generating an irreversible thickness swelling as soon as the pressure load is removed, causing strength loss in the adhesive bond [71; 73]. According to the present results, elastic recovery is closer to the \"shape memory effect\", which could be occurring in a directly proportional way to pressing time, where a longer pressing time is applied on wood specimens, the greater its elastic recovery and its elastic recovery is bigger, affecting negatively the glue line strength. The elastic recovery phenomenon is favored by humidity, steam water (elevated temperature test), and direct water contact (vacuum-press test). So, a more extensive pressing time may imply a higher potency of elastic recovery released, causing greater weakness in the adhesive bond [73]. This could be the reason why wood specimens glued and tested at elevated temperature and vacuum-pressure conditions showed a lower mechanical performance at 120 min pressing time than at 60 min.
## 2 Materials and Methods
### Data Fusion
#### 2.4.1 Preparation of Data for Data Fusion  
Before the fusion, the data needed to be prepared, undergoing several steps of preparation: noise reduction, coordinate matching, registration/interpolation, and normalization (see Figure 10). It was important to align the data obtained from different NDE techniques before the implementation of the data fusion. The raw data obtained from the experimental investigation of the single lap adhesive joint using ultrasonic and radiographic testing were aligned based on different parameters, such as noise removal, coordinate matching, interpolation, and normalization. The multi-step algorithm describing the pre-processing on the experimental data of two different techniques of non-destructive testing is shown in Figure 10.  
The evaluation of bonding quality in the aluminum single-lap joint was conducted using separate experimental set-ups, and as such the data obtained had a local coordinate system. To perform data fusion on such varied systems, the coordinates of the experimental data from the investigation had to be matched. The data were aligned with the position of the bonding edges of the adhesive joint to match the coordinates. The data from immersion ultrasonic inspection had to be flipped to match the radiographic testing data and further interpolated to match the coordinates of the newly registered data shown. Finally, the values of the radiographic and ultrasonic data were normalized using linear scaling (0 to 1).
# Coastal Wind Measurements Using a Single Scanning LiDAR
## 3 Results
### Validation of 10-Minute Mean Wind Speeds and Wind Directions
#### 3.2.1 Sensitivity to the Azimuth Angle Settings

Table 3: Statistics of the 10-minute wind speed from 100s for Cases 1–9. Figure 6: Scatter plots of the V1 and 100s wind speeds for (**a**) Case 1, (**b**) Case 2, (**c**) Case 3, (**d**) Case 4, and (**e**) Case 5.  
was used to increase the number of radial wind speeds data in the curve fitting. It was found that the results for Case 5 were relatively improved compared to Case 4, but the points for this case were still more scattered than those for Case 1 and Case 2. Figure 7 shows the wind direction plots. Here, \(\overline{D}_{\rm V1}\) and \(\overline{D}_{100\rm s}\) indicate the mean wind directions for V1 and 100s, respectively. The statistics for the mean wind direction for all of the experiments are described in Table 4. In calculating the error metrics for wind direction \(\overline{\varepsilon}_{D}\), \(\sigma(\varepsilon_{D})\), \(y=\text{m}x+\text{b}\), and \(R^{2}\)), the direction difference \(\varepsilon_{D}\) was limited to values between \(-180^{\circ}\) and \(+180^{\circ}\) to cancel the effect of periodicity between \(0^{\circ}\) and \(360^{\circ}\). Similar to the results obtained for wind speeds, the wind direction accuracy of the 100s depended strongly on the azimuth range. When the azimuth ranges were less than \(45^{\circ}\), the wind direction accuracy of the 100s decreased. A comparison between Case 4 (\(\omega=3^{\circ}\)/s) and Case 5 (\(\omega=1^{\circ}\)/s ) showed that decreasing the scan rate so that more radial wind speed data can be used for curve fitting did not improve the wind direction accuracy of the 100s. Figure 7: Scatter plots of the V1 and 100s wind directions for (**a**) Case 1, (**b**) Case 2, (**c**) Case 3, (**d**) Case 4, and (**e**) Case 5. Figures 6 and 7 indicate that a wider azimuth angle produced better results for scanning LiDARs with the VVP method. The question arises as to how large of an azimuth angle is reasonable for wind resource assessment. In terms of LiDAR accuracy, previous studies [19; 29] established acceptance criteria based on the International Electrotechnical Commission (IEC) standard 61400-12-1 [28], which included a technical note for remote sensing devices used for the power performance testing of wind turbines. Strictly speaking, the details of the criteria presented in the literature differ slightly. However, it is commonly accepted that for both wind speed and wind direction accuracy, the slope of the regression line should be between 0.98 and 1.02 and the determination coefficient should at least be greater than 0.98. Case 1 and Case 2 satisfy these acceptance criteria. The indication is that an azimuth width of more than 45\({}^{\circ}\) would be preferable for offshore wind measurements using the 100s and the VVP method.
## 1 Introduction  
At present there are different techniques to solve optimization problems [1; 2; 3; 4]. This article focuses on the optimization of parameters to achieve the tracking of a trajectory applied to benchmark control problems. The proposed method is based on the original harmony search algorithm, which mimics the process of music improvisation, which has been widely used to solve different problems, as shown in [5; 6; 7; 8; 9]. The proposed method is called fuzzy harmony search algorithm (FHS) that performs an adaptation of parameters with Type-1 and interval Type-2 fuzzy logic and it is used to optimize fuzzy tracking controllers so that they follow desired trajectories for benchmark control problems. Other metaheuristics that have been used to solve optimization problems based on fuzzy logic are [10; 11; 12; 13; 14; 15]. In previous works this HS algorithm has been used for different optimization problems, as can be found in [16; 17; 18; 19].  
The operation of non-linear plants with traditional logic or Type-1 fuzzy logic can be complex for a person who does not have exact knowledge of each of the characteristics that compose it, and using fuzzy set concepts and the principles of fuzzy logic can model the functioning of a plant in a more natural way with the use of linguistic variables as they could have valves like little, much, etc. so that anyone could understand the operation of such plants. In 1965 [PERSON] proposed the concepts of a fuzzy set in [20], fuzzy logic in [21], and the concept of a membership functions in [22]. Based on these concepts, complex problems can be solved by using fuzzy rules to represent an expert knowledge of a problem in a more natural way using linguistic variables.  
[PERSON] also proposed interval Type-2 fuzzy logic in [22], to achieve better management of the uncertainty of a problem, and this type of system is used to solve complex problems where there is uncertainty on the degree of membership of a value in a particular fuzzy set. Applying the concepts of Type-2 fuzzy logic, we can design a better fuzzy system since with the handling of uncertainty we can approximate the exact behavior of the real world; this is done using a footprint of uncertainty in the membership functions, which consider a range of membership degrees and not only one particular membership degree.  
The main contribution of this paper is the optimization of the values of the membership function of the fuzzy system of the three benchmark control problems presented and the stability obtained in the trajectory tracking of each case. The proposed method uses the harmony search algorithm with dynamic parameter adaptation with Type-1 and interval Type-2 fuzzy logic.  
This article is organized as follows: Section 2 gives a description of the original harmony search algorithm, Section 3 shows the fuzzy harmony search algorithm with dynamic parameter adaptation, Section 4 describes the benchmark control problems, Section 5 describes the simulation results, Section 6 shows the statistical comparison, and Section 7 presents conclusions.
## 2 Operation Principle
### Square Waveform  
As we all know, the Fourier expansion of the square waveform can be expressed as:  
\[f_{\rm{sq}}(t)=DC_{\rm{sq}}+A_{\rm{sq}}\sum\limits_{n=1,3,5,\ldots}^{\infty} \frac{1}{n}\sin(n\omega t)\approx DC_{\rm{sq}}+A_{\rm{sq}}\!\left(\sin(\omega t )+\frac{1}{3}\sin(3\omega t)+\cdots\right) \tag{4}\]  
If we set \(\theta\
eq 0\), \(\Delta\phi=\pi/2\) and neglect the higher order harmonics, which means the DDMZM working at the quadrature point and the input signal of its two arms have a phase difference of \(\theta\), and considering:  
\[\begin{array}{ll}\cos(x\cos\varphi)=J_{0}(x)+2\sum\limits_{m=1}^{\infty}(- 1)^{m}J_{2m}(x)\cos(2m\varphi)\\ \sin(x\cos\varphi)=-2\sum\limits_{m=1}^{\infty}(-1)^{m}J_{2m-1}(x)\cos[(2m-1) \varphi]\end{array} \tag{5}\]  
then, Equation (3) can be simplified as:  
\[\begin{array}{ll}I_{out}(t)=\frac{\Re|E_{\rm{in}}|^{2}}{2}\bigg{\{}2+4\sum \limits_{m=1}^{\infty}(-1)^{m}J_{2m-1}(2\beta\sin\frac{\theta}{2})\cos\!\left[ (2m-1)(\omega_{RF}t+\frac{\theta}{2})\right]\bigg{\}}\\ \approx\frac{\Re|E_{\rm{in}}|^{2}}{2}\Big{[}2-4J_{1}(2\beta\sin\frac{\theta}{ 2})\cos(\omega_{RF}t+\frac{\theta}{2})-4J_{3}(2\beta\sin\frac{\theta}{2})\cos (3\omega_{RF}t+\pi+\frac{3\theta}{2})\Big{]}\\ =\frac{\Re|E_{\rm{in}}|^{2}}{2}\Big{[}2-4J_{1}(2\beta\sin\frac{\theta}{2}) \sin(\omega_{RF}t+\frac{\pi+\theta}{2})-4J_{3}(2\beta\sin\frac{\theta}{2}) \sin(3\omega_{RF}t+\frac{3(\pi+\theta)}{2})\Big{]}\end{array} \tag{6}\]  
where \(J_{2m-1}\) denotes the \((2m-1)\)th order of the first kind of Bessel function. Here, \(r_{1/3}=J_{1}(2\beta\sin\frac{\theta}{2})/J_{3}(2\beta\sin\frac{\theta}{2})\) is defined as the coefficient ratio of the fundamental and third harmonic signals. As can be seen from Equation (6), only odd order harmonics exist, and even order harmonics are effectively suppressed. Comparing Equation (4) with Equation (6), the relationship between frequency and phase satisfies the requirement for square waveform generation. On the condition that \(r_{1/3}\) is approximately 3:1, the output photocurrent may present a square waveform in the time domain. In the proposed approach, the desired ratio \(r_{1/3}\) is 3 for the generation of the square waveform which  
Figure 1: Schematic diagram of the proposed full-duty-cycle square and triangular pulses generator based on a DDMZM. LD: Laser diode; DDMZM: Dual-drive Mach–Zehnder Modulator; PD: Photodetector; OSC: Oscilloscope; ESA: Electrical spectrum analyzer.  
will be achieved for setting the appropriate \(\beta\) with different \(\theta\). Figure 2 gives the relationship between the modulation index \(\beta\) and the coefficient ratio of the fundamental and third harmonic signals \(r_{1/3}\) with different \(\theta\). As can be seen from Figure 2, the optimized operation for \(r_{1/3}=3\) will be easily realized when \(\beta\) is set to be 2.29 rad, 1.6 rad, and 1.15 rad by properly setting the input RF power at the \(\theta\) of \(\pi\)/3, \(\pi\)/2 and \(\pi\), respectively. Since the square waveform generation needs no phase shift, it can be obtained at the \(0^{\circ}\) output port of the \(90^{\circ}\) hybrid coupler.
# Low-Power LoRa Signal-Based Outdoor Positioning Using Fingerprint Algorithm
## 2 Related Work
### TDoA-Based Positioning  
The TDoA-based positioning method requires time synchronization only between gateways and not between a gateway and an end-device. Hyperbolas are formed by the set of locations for which the difference in distance from gateways is constant. The location of an end-device can be obtained by calculating their intersection point.  
Figure 4 shows the TDoA-based positioning method. A LoRa end-device, indicated by the blue circle in the figure, sends an uplink packet to LoRa gateways. Each gateway records the packet arrival time by using a GPS time-synchronized gateway processor. The Geolocation Solver in the network calculates the differences between these arrival times. The location of the end-device is then determined by determining the point where hyperbolas overlap [6].  
Semtech Corporation is the market-leading vendor of LoRa chips such as the SX1272 and SX1301 chips. These chipsets provide time information with microsecond-order accuracy only to licensed users. Semtech Corporation also offers a novel Geolocation Capability that provides  
Figure 4: Semtech Corporation’s TDoA-based geolocation architecture [6].  
Figure 3: Link budget calculation to measure RSSI values [2].  
encrypted nanosecond-order accuracy information using the Advanced Encryption Standard (AES) algorithm [6; 15]. Only licensed gateway producers using the Geolocation Capability can provide nanosecond-order accuracy and precise positioning.  
[PERSON] et al. used Semtech Corporation's SX1272 chips to apply the TDoA-based positioning method [9]. Although their study reported positioning error exceeding 300 m, it was meaningful in that it showed that TDoA-based positioning was possible with LoRa signals. [PERSON] et al. reduced the positioning error to ~100 m by adopting an iterative algorithm in their method [3].
## 3 Flexural Capacity Prediction Methods and Applicability Analysis
### Widely Used Methods for Predicting Flexural Capacity
#### 3.1.3 Deformation-Based Method  
The deformation-based method was built based on failure mechanism analysis. [PERSON] et al. [43] assumed that the elongation of the un-bonded tendon mainly occurred within the plastic region, and the sectional curvature was assumed constant within the plastic region of length \(l_{\mathrm{P}}\). Their original equation is shown in Equation (4), where \(L_{0}\) denotes the initial tendon length. Following the recommendation of [PERSON] and [PERSON] [49], they took the plastic hinge length \(l_{\mathrm{P}}=10.5\,c_{\mathrm{u}}\), which was obtained from the observation of tests of monolithic beams prestressed with steel tendons. \(E_{\mathrm{P}}=200\) GPa and \(\varepsilon_{\mathrm{cu}}=0.003\) were substituted into Equation (4) to simplify it for application to steel tendons. The simplified equation is shown in Equation (5), where \(L_{0}\) is the initial tendon length. Equation (5) was adopted by AASHTO LRFD [50]. In this paper, Equation (5) is rewritten as Equation (6) to consider the elastic module difference between steel and CFRP tendon.  
\[\Delta\sigma_{\mathrm{P}}=E_{\mathrm{P}}l_{\mathrm{P}}\frac{\varepsilon_{ \mathrm{cu}}}{c_{\mathrm{u}}}\big{(}\big{(}d_{\mathrm{P}}-c_{\mathrm{u}}\big{)} /L_{0}\big{)} \tag{4}\]  
\[\Delta\sigma_{\mathrm{P}}=6200\big{(}\big{(}d_{\mathrm{P}}-c_{\mathrm{u}}\big{)} /L_{0}\big{)} \tag{5}\]  
\[\Delta\sigma_{\mathrm{P}}=0.031E_{\mathrm{P}}\big{(}\big{(}d_{\mathrm{P}}-c_{ \mathrm{u}}\big{)}/L_{0}\big{)} \tag{6}\]