esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Method and Data
### Heat Transport Pipeline Data
#### 2.2.2 Basic Unit Setting  
The collected attribute information of heat transport pipelines is classified according to the arbitrary standards of the operator. It is necessary, however, to construct a dataset by setting basic units with certain standards to improve the efficiency of predicting the damage probability of heat transport pipelines. Therefore, basic units were set based on the attribute information of heat transport pipelines. Preprocessing was performed to construct the data required to set the basic units of heat transport pipelines. Most of the data did not contain missing or abnormal values, but data discretization was performed for attribute information that included some missing and abnormal values. The basic units were set considering priorities according to four standards. A total of 324,494 basic units were generated based on the 132,210 heat transport pipeline data collected through basic unit setting.  
Combination of Heat Transport Pipelines Based on Attribute Information  
For basic unit setting, when the attribute information of connected heat transport pipelines (operator, pipe function (supply or return pipes), pipe diameter, heat supply status, installation date (period of use), sensor wire condition, and key management sections) was identical, they were combined into one basic unit. The attribute information refers to factors that are expected to have direct or indirect impacts on the structural stability of pipes and damage to them. Among the information, the attribute information included in the damage history provided by the operators (operator, pipe function, pipe diameter, and installation date) was subjected to correlation analysis subsequently.  
Maximum and Minimum Length Setting  
The minimum and maximum lengths of the basic units of heat transport pipelines were set. The minimum length was set to 1 m and the maximum length to 36 m. The heat transport pipeline data, however, contained pipelines with a length of less than 1 m. They were combined with connected pipelines of the same attribute information. In the case of pipelines with a length over 36 m, their length was divided by 36 and they were split into basic units in the quantity of the rounded result. For example, as shown in Figure 1, a 50 m long pipeline was split into two basic units based on the division by 36. This process prevented basic units from exceeding 36 m.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attribute Information** & **Data Characteristics** \\ \hline GIS location information & Shape file \\ Equipment ID & Numeric string \\ Operator & Character string \\ Pipe function & Character string \\ Pipe diameter & Numeric string \\ Installation date & Numeric string \\ Sensor wire condition & Character string \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data characteristics of heat transport pipelines.
## 3 System Requirements for BVLOS Flight Operations
### Extended Communication Links  
For VLOS flight operations,\"rubber ducky\" type omni-directional antennas are typically used for all RF communications. The operational range of these antennas is approximately 3 km for the 900 MHz C2 link and only 500 m for the 5.8 GHz video link which is insufficient for BVLOS flight operations. As shown in Figure 3, two high-gain directional grid-dish antennas described in Table 1 are used to extend the RF communication range.  
Ground range tests are performed between the ground devices and the airborne avionics. The GCS, the FPV receiver and two high-gain antennas are sited on the runway, and the autopilot, the FPV camera, and the FPV transmitter are installed on the top of a hill located 2.5 km east of the runway and 100 m higher in elevation. The results show that 20 dB of attenuation can be applied to the 900 MHz C2 link and 10 dB of attenuation can be added to the 5.8 GHz video link before both links are lost. In other words, the 900 MHz C2 link has an operating range of approximately 25 km and the 5.8 GHz  
Figure 2: System configuration for small UAS DAA system tests in BVLOS flight operations.  
video link has a range of approximately 7.5 km. Therefore, both antennas are suitable for the 7 km BVLOS flights displayed in Figure 1.
## 4 FWA Design Considerations, Challenges, and Solutions
### PA Technology Selection  
The FWA front-end technology is contingent on the required system antenna gain, EIRP, and related noise figure. These factors are functions of the BF gain. Moreover, the BF gain depends on the array size. Furthermore, the front-end of choice could be based on silicon-germanium (SiGe) or GaN. Meanwhile, apart from the 28 GHz and 39 GHz bands that have been defined for 5G, the Federal Communications Commission (FCC) is also exploring other spectrums between 4 GHz and 24 GHz [12]. Table 10 presents the EIRP limits in the U.S. (imposed by the FCC) for 28 GHz and 39 GHz spectrum for BSs and mobile devices.  
According to an analysis in [264], the required active number of channels with SiGe PA is considerably higher than that of the GaN front end technology. Besides the fewer number of active channels, employment of GaN technology in the FWA front end offers a number of advantages such as lesser aggregate power dissipation, better reliability, and reduced complexity. These benefits make GaN a suitable solution for the front-end compared with SiGe. On the other hand, it has been shown that to realize a specific EIRP using a uniform rectangular array, the per-channel required PA output power reduces with an increase in the number of elements. Consequently, with a very large array with active elements, the output power for each element turns out to be sufficiently small to encourage implementation of the SiGe PA [264].  
In general, the chosen technology depends on the application scenario. For instance, an application that demands a small physical footprint can be exploited very small PAs which are based on SiGe/Si semiconductor technology and offer low power output. Alternatively, high power density semiconductor technology like GaN which exhibits over 100 times the SiGe/Si power density can also be employed [12].
# Real-Time Detection of Insulator Defects with Channel Pruning and Channel Distillation
## 1 Introduction
Pruning channels achieve a more lightweight network with fewer parameters and computational loads, and its effectiveness is evaluated. 3. **Channel Distillation for Accuracy Enhancement**: We employed the YOLOv7 model as a teacher model to perform channel distillation on the pruned model. This method enhances the accuracy of insulator defect detection without increasing the number of parameters, providing a feasible solution for deploying insulator defect detection models on mobile devices like drones, ensuring the precise identification of insulator defects. The remainder of the study is organized as follows: Section 2 describes the development of object detection technology. Section 3 details the improvements made to the insulator defect detection model, including channel pruning and channel distillation. Section 4 presents the experimental details and results, covering data, evaluation metrics, and model performance analysis. Finally, Section 5 summarizes the findings of this research.
## 2 System Identification Methods
### Nonlinear ARX  
The NARX models can be used to describe different nonlinear dynamic behavior [18]. It is used to predict behavior of structures equipped with passive controller systems. It is worth mentioning that, as the system order increases, the number of candidate terms becomes very large [18; 19; 20] which leads to increased complexity in the analysis [21]. The ARX is described detail in [21; 22]. A prediction of the NARX model takes the form of following nonlinear regression equation [20]:  
\[y\left(t\right)=f\left(y\left(t-1\right),\ldots\ldots,y\left(t-n_{y}\right),u \left(t-n_{k}\right),\ldots\ldots,u\left(t-n_{k}-n_{u}+1\right)\right) \tag{1}\]  
where \(f\) is a nonlinear function, \(u(t)\) and \(y(t)\) are the input and output sequence, and \(n_{u}\) and \(n_{y}\) are the maximum numbers of input and output measurements involved in the model, respectively, \(n_{k}\) is the input delay.  
The common representation for the NARX model in Equation (1) is artificial neural networks, which takes the function \(f(.)\). In this study, the sigmoid and wavelet neurons are applied and assessed. However, the neural networks method is used to identify and adjust the model parameters. Identifying a NARX model requires two steps: (1) the parameter estimation and (2) structure detection. Those can be divided into model order selection and the parameters which are selected to be used in the model. The model order selection is considered as a part of structure detection. Determination of the model order depends on the trail and errors method and it restricts the choice of terms to be considered. For NARX models, the system order can be defined as:  
\[O=\left[n_{y}\;n_{u}\;n_{k}\right] \tag{2}\]  
The considered prediction \(\left(y_{\text{s}}\left(t\right)\right)\) is computed from measured outputs up to the time instant \(t-1\).
## 3 Converter Topology for the Power Modules  
Non-isolated MPCs are usually built based on combinations of basic pulse width modulation (PWM) DC-DC converters (buck, boost or buck-boost) or based on a reduced form of the combined converter [15]. The most common topology for MPCs is the boost-boost topology (Figure 3a) which uses multiple boost converters to interface the different power sources to the power bus. However, this topology has some limitations if implemented in the proposed MMPC. The minimum output voltage of the PM based on this topology is limited to the battery voltage while the maximum output voltage is determined by the boost ratio of the PV-converter during the availability of PV power, and the battery converter when PV power is unavailable. If the maximum voltages for battery and PV are designed to be equal at a value of V\({}_{\rm pv}\), then the minimum achievable output voltage under this condition is V\({}_{\rm pv}\), while the maximum achievable output voltage will be more than V\({}_{\rm pv}\) depending on the boost ratio of the converters. Accordingly, if a boost ratio of two is considered (although feasible, this may affect efficiency), the operating voltage range of the converter will be from V\({}_{\rm pv}\) up to 2 V\({}_{\rm pv}\).  
The proposed topology for the MPC cell shown in Figure 3b has been developed based on the integration of a boost stage which interfaces to the PV-module and a buck stage which interfaces to the battery module. This topology enables a dual step-down and step-up voltage conversion for the battery voltage providing more flexibility for the choice of battery voltage. To achieve this, blocking diodes D1 and D2 are needed to decouple the PV boost from battery converter in order to extend power flow controllability of the developed MPC. To better analyze the operations of the proposed converter, the three different operating modes will be further analyzed under continuous conduction mode (CCM) neglecting the diode forward voltages.  
Figure 3: Suggested topologies of the multi-port converter (**a**) Boost-Boost MPC; (**b**) proposed topology.
## 3 Results and Discussion
### Numerical Model at Different Prescribed Velocities  
Figure 4 shows the typical results obtained from the numerical simulation of the Taylor impact test for the LPBF Ti6 Al4V (ELI) alloy at three imposed velocities. From Figure 4a, it can be seen that the lower velocity of 130 m/s produced a plastic strain of about 0.467 and the deformation of the model is characteristic of the Taylor impact test results, where the impacted end of the model deforms plastically while the free end is undeformed. Lateral bulging of the model at this velocity increased from the impacted to the free end.  
Remarkable deformations near the impact region were observed with an increase in prescribed velocity. Figure 4c,e clearly show that the higher velocities of 200 m/s and 250 m/s resulted in greater inelastic deformation (plastic strain > 0.9) which was concentrated near the impacted end and decreased in magnitude away from it. It is  
Figure 4: Typical distribution of equivalent von Mises stresses and strains obtained after numerical simulation of LPBF Ti6 Al4V (ELI) specimens at impact velocities of (**a,b**) 130 m/s, (**c,d**) 200 m/s, and (**e,f**) 250 m/s. Image (**g**) shows the status of the model at the beginning of simulation prior to impact.  
evident that, at these higher velocities, the impacted surface of the projectile deforms into a mushroom shape, which can be ascribed to large plastic flow in the highly strained regions.  
Figure 4g depicts an undeformed model of a Taylor test specimen with a length of 15 mm and a uniform cross section diameter of 5 mm (the radius of a quarter rod being 0.25). A comparison between the undeformed and deformed Taylor impact models shows that the cylindrical rod shortens as material flows out radially due to plastic flow. The diameter at the impact end increased during the simulation due to the induced tensile hoop stress caused by the compressive plastic wave. The parameters of the final shape of the deformed model are illustrated in the schematic diagram shown in Figure 5.  
The radial ratio, expressed as \(\mathrm{R_{f}/R_{o}}\), and the length ratio, expressed as \(\mathrm{L_{f}/L_{o}}\), for the simulation of LPBF Ti6 Al4V (ELI) were extracted from the simulation results in Figure 5 and are presented in Table 3.  
From this table, it can be seen that the \(\mathrm{R_{f}/R_{o}}\) ratio increased as the velocity imposed on the model increased. Conversely, the \(\mathrm{L_{f}/L_{o}}\) ratio decreased as the imposed velocity increased. It can be inferred from a study of the values in this table that the deformation configuration that characterised the numerical Taylor impact tests was the same as observed in other studies [19; 20]. The foregoing discussion suggests that the typical features of the Taylor impact model were numerically predicted using the VUHARD subroutine.
## 1 Introduction
Third, when the measured signal contains some signals with strong amplitude, the strong signal's side lobe overwhelms the weak signal's main lobe, affecting the accurate WSAW frequency estimation. Finally, the periodogram is not suitable for non-stationary signals, but the WSAW signal we receive is a cyclostationary signal. For the cross-media acoustic and electromagnetic integrated communication technology, estimating the underwater sound source frequency is mandatory. The RELAX algorithm is a spectral estimation algorithm that effectively solves some problems that the periodogram cannot solve. [PERSON] introduced the RELAX algorithm in 1996 for target feature extraction [29]. Specifically, [PERSON] applied the RELAX algorithm to detect breathing and heartbeat and successfully estimated and separated their frequency [26]. In principle, the RELAX algorithm is a super-resolution algorithm with a high-frequency resolution that effectively solves the first and second problems in the periodogram. However, since it subtracts the estimated maximum value, the side lobe is not suppressed, and therefore applying the RELAX algorithm to the third problem fails. Finally, the cross-media communication scheme sends at least two different frequencies and encodes them, creating a cyclostationary signal. Therefore, we conclude that RELAX is inappropriate for processing cyclostationary signals. The above-mentioned spectral estimation algorithm is inappropriate for WSAW signals. Hence, to overcome the above problems, this work develops an improved RELAX algorithm. Specifically, we perform nonlinear filtering on the RELAX kernel function to separate similar frequencies. Furthermore, the non-uniformity detection based on the generalized inner product is utilized to obtain the different frequency signals interval so that non-stationary signals can be processed. The remainder of this paper is organized as follows. Section 2 introduces the underwater sound source excitation model and the detection principle. Section 3 proposes the spectral estimation method based on an improved RELAX algorithm. Section 5 presents the simulation and experimental results, while Section 5 analyzes and discusses the performance obtained. Finally, Section 6 concludes this paper.
## 2 Materials and Methods
### Sequence Alignment  
The hA\({}_{1}\) and hA\({}_{2\text{A}}\) AR crystallographic structures (SUEN and 3 PWH, respectively) were aligned and superposed by MOE Sequence Editor. Structural domains were annotated basing on visual inspection of each TM region. The canonical fasta sequence of the hA\({}_{3}\) adenosine receptor (ID P0 DMS8-1) was retrieved from the UniProt database [30] and aligned on the sequences of hA\({}_{1}\) and hA\({}_{2\text{A}}\) AR (see Table 1). Sequence identity and similarity percentages were calculated for all domains using BLOSUM62 matrix (see Results section). To perform a more robust comparative analysis, we calculated the deviation values between either sequence identity (SI) or sequence similarity (SS) of each domain of the hA\({}_{3}\) AR calculated on the hA\({}_{1}\) (A3/A1) from the one calculated on the hA\({}_{2\text{A}}\) AR (A3/A2a), as reported in Equations (1) and (2):  
\[\Delta SI=\frac{(SI_{A3/A1}-SI_{A3/A2a})}{SI_{min_{(A1,A2a)}}}.100 \tag{1}\]  
\[\Delta SS=\frac{(SS_{A3/A1}-SS_{A3/A2a})}{SS_{min_{(A1,A2a)}}}.100 \tag{2}\]  
Negative values are in favor of the hA\({}_{2\text{A}}\) subtype, while positive values are of hA\({}_{1}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **A3/A1 Sequence Alignment** & **A3/A2A Sequence Alignment** \\ \hline
**TM1** & \({}^{10}\)AAYIGEVLIALSVVRGNVLYIWAVKOV\({}^{36}\) & \({}^{15}\)AIAVLAILGNVLVCWAWWLNSNLIQNVT\({}^{41}\) \\  & \({}^{13}\)VYTIMEFIGLCAIVGNVLVCUVKL\({}^{39}\) & \({}^{21}\)FIGCLCAIVGNVLYVCVKLNPSQLQITT\({}^{67}\) \\ \hline
**IL1** & \({}^{37}\)NQALRD\({}^{42}\) & \({}^{42}\)NYFVVS\({}^{47}\) \\  & \({}^{40}\)NPSLQT\({}^{45}\) & \({}^{48}\)FYFIVS\({}^{53}\) \\ \hline
**TM2** & \({}^{43}\)ATCFIVSLAVADVVAGALYIPLAILIN\({}^{71}\) & \({}^{48}\)LAAADIAVGVLAPIFATITSTGFCAACHG\({}^{76}\) \\  & \({}^{46}\)TTFFIVSLAADIAVGVLVMPLLAIVVSL\({}^{74}\) & \({}^{54}\)LALADIAVGVLMVPLALIVSGLITHFYS\({}^{82}\) \\ \hline
**EL1** & \({}^{72}\)GFQTY\({}^{76}\) & \({}^{77}\)CLFIA\({}^{81}\) \\  & \({}^{75}\)GITH\({}^{79}\) & \({}^{83}\)CLFM\({}^{467}\) \\ \hline
**TM3** & \({}^{77}\)FHTCLMVACPVILITOSSILALALIAUDRYLIRJK\({}^{110}\) & \({}^{82}\)CFVLVLTTOSSIFSLAIAIDRYIAIRIRLPRYNGLL\({}^{115}\) \\  & \({}^{80}\)FYSCLFMTCLLIFTHASIMSLLAIVDRYLIRJK\({}^{13}\) & \({}^{88}\)CLLLIFTHASIMSLLAIVDRYLIRJK\({}^{13}\) \\ \hline
**IL2** & \({}^{111}\)IPLKYKMVVT\({}^{120}\) & \({}^{116}\)VTGTRAKGIIT\({}^{125}\) \\  & \({}^{114}\)LTVRYKKRVTT\({}^{123}\) & \({}^{122}\)ITHRRIWLAL\({}^{131}\) \\ \hline
**TM4** & \({}^{121}\)IPRRAVAIAGCWLSFVVGLTPMF\({}^{144}\) & \({}^{125}\)AICHVLSFAGLTPMLGWNNNCGQP\({}^{149}\) \\  & \({}^{124}\)HRIRWLALGLCWLVSFVGLTPMF\({}^{147}\) & \({}^{132}\)GLCWLVSVFLVGLTPMF\({}^{147}\) \\ \hline
**EL2** & \({}^{145}\)GVNNNLAVERAANANASMGCPIVKCEFEKV\({}^{156}\) & \({}^{150}\)KGLKSHQGC
# A Study of the Bond Strength and Mechanism between Basalt Fibers and Asphalt Binders
tested and evaluated six silane coupling agents for modifying the rheological properties of basalt-fiber-reinforced asphalt at different elevated temperature conditions and detailed the correlation between surface components and rheological properties in terms of temperature response [25]. The cited study provided guidance on the use of silane coupling agents in combination with mineral fillers to improve the overall performance of asphalt mixtures. [PERSON], [PERSON] et al. described how the pneumatic adhesion tensile testing instrument (PATTI) was used to examine the mechanism by which fibers influence the pull-off tensile strength of asphalt mastics [26]. The results showed that fibers led to an increased pull-off tensile strength of the mastics and changed the failure mode from cohesive to hybrid, implying an improvement in the cohesive strength of the mastic. [PERSON], [PERSON] et al. developed the compression pull-off test (CPOT) to study the cohesive and adhesive bond strength of binders [27]. They concluded that the COPT was useful for investigating the phenomenon of cavitation and fibrillary concentration in the cohesive bonds of asphalt and mastics. Most investigations show that it is the bond strength between basalt fibers and asphalt binders that determines the performance of basalt fiber-reinforced asphalt mixtures [28; 29]. The literature on basalt fiber asphalt binders has highlighted several properties, such as high- and low-temperature rheological properties [30], crack resistance [31], shear resistance [32], fatigue performance [33], and so on. However, there is still a lack of easily performable approaches that can be used to assess the bond strength between basalt fibers and asphalt binders. This study aimed to develop a fiber-asphalt binder bond strength tester and evaluate the influence of the embedment depths of basalt fibers and asphalt binders on bond strength. Additionally, the adhesion mechanism between basalt fibers and asphalt binders was studied by using a scanning electron microscope and the contact angle test.
## 5 Pre-Treatments for Anaerobic Digestion of Food Waste
### Thermal Pre-Treatments  
Exposing substrate to temperature changes over a period of time, is known as thermal pre-treatment [257]. These pre-treatments could be included in a physical category, however, due to its feasibility and suitability, are usually considered in a separated category. The thermal pre-treatment purpose is to disintegrate cell walls and membranes to increase solubilization of OM [258]. Subjecting FW and substrates to temperatures between a range of 50-160 \({}^{\circ}\)C results in an increase of biogas production around 30-35% [191; 240; 259] or even 50% [241]. For temperatures that are out of the range, AD performance and biogas yield are decreased compared with the biogas yield obtained with non-treated substrate, as inhibitory and refractory compounds appear in the digester. That is exactly what happens when thermal pre-treating slaughterhouse waste [260], lipids were turned into VFA acid, and the AD process resulted in foaming inside the reactor, leading to an overload of the system.  
Feasibility of thermal pre-treatments came also from other benefits of applying them, for example, pathogen removal [261] or dewatering performance [241], which reduced the viscosity of digestate, making it more feasible for later transportation.  
Freezing and thawing is also considered as a thermal pre-treatment, but, in this case, submitting substrate to sub-zero temperatures (freezing) and afterwards recovering its temperature (thawing). In this case, biogas yield increased around 30% [113], due to cell disruption that is caused by the temperature change. This pre-treatment has not been widely used, mainly because of its high operational cost, but it is extremely adequate and feasible for FW as freezers are always present in agri-food industry facilities.