esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Damping Model of the Specimens
The latter assesses the level of significance of the regression model and, for the models here considered, basically coincides with the \(p\)-_value_ of \(\lambda_{1}\), since there is only one independent variable (i.e., the frequency). The regression model explains almost 45% of the variability in the damping ratios of the \(PY\) panel (\(R^{2}=0.444\)) and appears sufficiently reliable (\(p\)-value \(=9.81\times 10^{-4}\)). Hence, it can be considered as a satisfactory approximation of the \(PY\) damping trend. Regarding the \(PN\) panel, the regression model accounts for only 15% of the data variability, but the approximation still appears acceptable. Additionally, the linear regression model clearly appears to approximate better the data, when compared to the Rayleigh damping hypothesis. The comparison between the two computed regression lines further confirms the dampening capabilities of the tested viscoelastic material, over a quite wide frequency range. It is worth noting that the regression model of the \(PY\) panel represents the behavior of the composite laminate and not the intrinsic characteristics of the viscoelastic material. The usefulness of its implementation may be thus limited, but identifying the damping properties of only a single viscoelastic layer through EMA (which this study is based on) is objectively impossible, due to its extremely low thickness. Nonetheless, it may be used as a reference for predicting the dynamic response of CFRP laminates featuring overall characteristics similar to the panels targeted in this study (which are assumed reasonably quite common in a number of applications). For instance, it may be adopted to perform structural dynamics analyses of CFRP laminates within a FE code, in case the composite can be modelled with an equivalent material (see Section 2.4). Therefore, the proposed damping model may usefully support the investigation of new design solutions of components that include thin composite panels featuring the investigated viscoelastic material: in particular, when the target of elastodynamic simulations is the back-to-back comparison of different design solutions, the absolute accuracy of results is often of secondary importance.
_Applied_  
_sciences_  
Design of Anchorage Zones of Pretensioned Concrete Girders: A Comparison of Nonlinear 3D FEM Results with Measurements on a Full Scale Beam  
**[PERSON] 1,*\({}^{\text{\textregistered}}\),\({}^{\text{\textregistered}}\), [PERSON] 1, [PERSON] 1 and [PERSON] 1,2**  
1 Department of Structural Engineering and Building Materials, Chest University, 9000 Ghent, Belgium; [EMAIL_ADDRESS] (K.V.M.); [PERSON]ESS] (V.B.); [PERSON]RESS] (L.T.)
2 College of Civil Engineering, Tongji University, Shanghai 20092, China
* Correspondence: [PERSON]]; Tel.: +32-476-230-362 check for updates  
Received: 6 November 2020; Accepted: 18 November 2020; Published: 20 November 2020  
Featured Application: The end zones of pretensioned beams have been investigated for many years, and many simplified analysis methods have been proposed. Detailed nonlinear finite element analysis-based design, has only become available in the last years. The paper provides an experimentally validated nonlinear FE based technique which opens perspectives for a further optimization of these end zones.  
**Abstract:** Pretensioned concrete beams are widely used for constructing large load-bearing structures and bridging long spans. Crack formation may occur in the end zones of these elements due to tensile splitting, spalling and bursting actions. Investigation of these zones is typically done by means of analytical methods, strut and tie modelling, 2D linear or nonlinear analysis, or full 3D nonlinear analysis. Especially challenging in this last approach is the modelling of the force transfer from the strands to the surrounding concrete as it strongly influences the magnitude of the tensile stresses. This paper presents a 3D nonlinear analysis of the anchorage zone of a pretensioned girder, and a comparison with experimental results (mechanical strain measurements, embedded strain gauges). Material modelling, steel-concrete interaction properties, as well as convergence problems are addressed systematically. The comparison indicates that a good agreement is found, both for concrete and rebar strains, and that a friction coefficient of 0.7 can be adopted, although the results for values from 0.5 to 0.9 do not differ that much. The results demonstrate that a 3D nonlinear analysis provides an excellent insight in the behavior of the end zones of pretensioned girders which opens perspectives for an optimization of the end zone design based on this type of analysis.  
**Keywords:** pretensioned girders; anchorage zones; nonlinear FEM; ABAQUS
## 3 Impact of external errors
### Impact of environmental effects  
The range value between the target in the swath and the master antenna \(r_{l}\) is used during the ground altimetric process (see I.1). This range is affected by several sources, principally the ionospheric and tropospheric delays.
## 2 Materials and Methods
### Characterization Techniques  
A PANanalytical X'PERT POWDER diffractometer (Malvern Panalytical Ltd., Malvern, UK) that uses Cu (Ka) radiation with 1.541874 A wavelength was used to collected X-ray powder diffraction (XRD) patterns at room temperature.  
Nitrogen adsorption measurements were performed on a Micromeritics ASAP 2010 physisorption analyzer at 77 K. Brunauer-Emmett-Teller (BET) and Barret-Joyner-Halenda (BJH) methods were used for the surface area and pore size distribution determination using N\({}_{2}\) adsorption data. Samples were previously outgassed for at least 12 h at 150 \({}^{\circ}\)C.  
FTIR measurements were carried out in a Two Perkin-Elmer UATR Two spectrophotometer between 400 and 4000 cm\({}^{-1}\).  
High resolution transmission electron microscopy (HRTEM), images, electron diffraction (ED) patterns and EDX spectra, were acquired in a JEOL 300 FEG. The samples were prepared by crushing the powders under n-butanol and dispersing it over copper grids covered with a holey carbon film.  
Scanning electron microscope (SEM) images and corresponding EDS spectra were obtained with a JEOL JSM 6335 F microscope. Before SEM examination, the powdered samples were mounted over a carbon film supported onto brass holders and metallized with Au.  
For the electrochemical measurements, the anode materials were prepared by mixing 70 wt% of the active material (0.5-3 mg) with 20 wt% of carbon super P (from Imerys Graphite & Carbon, Bironico, Switzerland) and 10 wt% of sodium alginate (from Sigma Aldrich, Madrid, Spain) with a small amount of distilled water and then coated directly on the metal support of a Swagelok cell and dried for a couple of hours. In a dry box filled with argon, all of the Swagelok cell pieces were flocked together starting by the as-prepared coated electrode followed by the fiber sheet from Whatman borosilicate glass (grade GF/D) filled with drops of 1 M LiPF6 in ethylene carbonate (EC) and dimethyl carbonate (DMC) (1:1 in weight) as the electrolyte, and lithium metal as a counter electrode. Electrochemicaltesting was conducted using a Biologic 815 potentiostat. Cyclic voltammetry (CV) and galvanostatic charge and discharge (GCD) tests were conducted in a potential window between 0.01 and 2.5 V. Then, a 0.1 mV/s scan rate was applied for CV testing, and several different currents were used for GCD testing in each case. The applied current densities were normalized to the mass of cerium oxide in the electrode.
# Validation of Acoustic Emission Waveform Entropy as a Damage Identification Feature
## 2 Background  
AE can be described as a phenomenon where a material emits elastic waves as a result of a source activation [13]. Source activation could be in the form of damages such as plastic deformation, crack initiation and crack propagation, etc. The elastic waves generated from the source propagate spherically outward in the material. When the elastic waves reach the surface, they are captured using piezoelectric sensors as an analogue signal. The analogue signal is amplified and passed on to the AE data acquisition system. The AE data acquisition system digitises the analogue signal and calculates some features (i.e., rise-time, duration, energy, peak amplitude, count, centroid frequency, etc.). The strength and magnitude of the features are representative of the source. The digital waveform and the features are stored in a computer for post-processing. Figures 1 and 2 illustrate the working principle of AE and the definition of the widely used traditional AE features.
## 5 Sub-Areas of Traffic Prediction Applications Using RNNs
### Occupancy Prediction  
Occupancy prediction involves forecasting the number of passengers or vehicles occupying a transportation service or facility at a given time. This prediction is significant for transportation because it aids in optimizing resource allocation, improving service quality, and enhancing operational efficiency. Accurate occupancy predictions enable better scheduling of services, informed infrastructure planning, and effective crowd management. This results in reduced waiting times, improved passenger comfort, and overall enhanced performance and reliability of the transportation system. RNNs are widely employed for occupancy prediction, and among them, LSTMs emerge as a popular choice due to their capability to capture temporal dynamics and long-term dependencies. [PERSON] et al. [130] proposed a probabilistic vehicle trajectory prediction method utilizing LSTM to analyze the temporal behavior and predict future surrounding vehicle coordinates. Furthermore, the use of stacked LSTM models for parking occupancy rate prediction, as demonstrated by [PERSON] et al. [131], outperforms traditional models and validates the proposed predictive model. [PERSON] et al. [132] stacked the GRU and LSTM together, utilizing historical parking data, occupancy, weather conditions, and other diverse data to predict parking volume and parking space availability across various periods. [PERSON] et al. [133] optimized the traditional LSTM model by integrating it with a feedforward neural network to form a hybrid LSTM network for predicting the occupancy rate of electric vehicle charging and making certain contributions to charging infrastructure management.
# Transmission Lines Small-Target Detection Algorithm Research Based on YOLOv5
###### Abstract  
The images captured using UAVs during inspection often contain a great deal of small targets related to transmission lines. These vulnerable elements are critical for ensuring the safe operation of these lines. However, due to various factors such as the small size of the targets, low resolution, complex background, and potential target aggregation, achieving accurate and real-time detection becomes challenging. To address these issues, this paper proposes a detection algorithm called P2-ECA-EIOU-YOLOv5 (P2E-YOLOv5). Firstly, to tackle the challenges posed by the issues of complex background and environmental interference impacting small targets, an ECA attention module is integrated into the network. The module effectively enhances the network's focus on small targets, while concurrently mitigating the influence of environmental interference. Secondly, considering the characteristics of small target size and low resolution, a new high-resolution detection head is introduced, making the network more sensitive to small targets. Lastly, the network utilizes the EIOU_Loss as the regression loss function to improve the positioning accuracy of small targets, especially when they tend to aggregate. Experimental results demonstrate that the proposed P2E-YOLOv5 detection algorithm achieves an accuracy P (precision) of 96.0% and an average accuracy (mAP) of 97.0% for small-target detection in transmission lines.  
power line inspection; object detection; small targets; attention mechanisms; loss function Article Transmission Lines Small-Target Based on YOLOv5
## 4 Adjustment of the Propagation Model Using Least Squares Linear Regression
Finally, the adjusted model obtained is shown as follows:  
\[fittedmodel_{logarithmic}(x)=-43.79-8.961\log(x). \tag{14}\]  
Figure 13 presents the predicted power as a function of the distances referenced in Table 6. It is observed that as the distance increases, the power decreases, resulting in a downward curve. Figure 14 provides a comparative analysis of the graphs representing linear regression, logarithmic regression, and the empirical data of Wi-Fi repeater transmission as a function of distance. It is noteworthy that both regression models exhibit a trend that closely aligns with the real data. \begin{table}
\begin{tabular}{c c c} \hline
**L (m)** & **Prx (dBm)** & **Mean Path Loss (dB)** \\ \hline
10 & \(-\)52.7510 & 73.751 \\
20 & \(-\)55.4485 & 76.4485 \\
30 & \(-\)57.0265 & 78.0265 \\
40 & \(-\)58.1461 & 79.1461 \\
50 & \(-\)59.0145 & 80.0145 \\
60 & \(-\)59.7240 & 80.724 \\
70 & \(-\)60.3239 & 81.3239 \\
80 & \(-\)60.8436 & 81.8436 \\
90 & \(-\)61.3020 & 82.302 \\
100 & \(-\)61.7120 & 82.712 \\ \hline \end{tabular} The Raspberry Pi 3 has a transmission power of 125.9 mW, which corresponds to 21 dB.  
\end{table} 
Table 6: Input data for the linear model [Equation (14)]. Figure 13: Model fitting graph based on the generation of a probabilistic logarithmic function [Equation (14)].
## 1 Introduction
The daily consumption of a household is predicted using and comparing various forecasting models and the energy demand for the next few days over a specific period of time is determined. The proposed RNN-GBRT hybrid model shows more accurate forecasting performance than other models. Thereby, creating awareness among the consumers and the utilities with regard to their energy usage is the major focus. The second contribution is towards identifying the power theft by constantly tracking the data consistency to detect any kind of abnormal and sudden change in the energy meter reading. The rest of the paper is organized as follows. Section 2 presents the literature survey, wherein various papers are analyzed with respect to the proposed research work. Additionally, some of the limitations of the papers are highlighted. Section 3 presents the algorithms for demand forecasting and power theft detection, while their implementation is described in Section 4, which presents various simulation results and demonstrates the superiority of the proposed prediction model and the efficacy of the power theft strategy. Finally, Section 5 concludes the work and highlights the future scope.
# A QoS-Enabled Medium-Transparent MAC Protocol for Fiber-Wireless 5G RAN Transport Networks
## 3 The QoS-Aware MT-MAC Protocol
### The QoS-Enabled MT-MAC
\begin{table}
\begin{tabular}{c c} \hline \hline
**Step \#** & _Action_ \\ \hline Step 1: & _Identify each flow \(F_{i}\ =\ \{IFG_{i}\}\), where \(IFG_{i}\) = Inter Frame Gap of flow \(F_{i}\) (in slots)._ \\ Step 1: & _The duration of the Superframe denoted as TSF is calculated as the Lowest Common Multiple (LCM) of IFGx values of all flows._ \\ Step 2: & _Flows are arranged in an array based on the ascending order of IFG._ \\  & _Lowest IFG flow inserted first at slot \(1\) IS = I (IS = Initial Slot of flow \(i\) in SF). Slots of the form \(IFG:\ m\ +\ IS_{i},\ m=0,1,\ldots\) are removed from the set of available slots._ \\  & _Next flow offset to next available IS slot._ \\ Step 4: & _Next flow in ascending order is inserted at IS\({}_{i}\). Step 4 is repeated until all flows have been inserted into the PS._ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The CEXP-Fit algorithm. Figure 4: Polling Sequence creation algorithm for qMT-MAC. Each CEXP flow \(F_{i}\) is characterized by its Interframe Gap \(IFG_{i}\) measured in slots. Flows are arranged in ascending order of \(IFG_{i}\). The duration of the SF \(T_{SF}\) is calculated based on the Least Common Multiple (LCM) of all \(IFG_{i}\). Flow \(F_{i}\) with the lowest \(IFG_{i}\) is inserted in slot 1 of the PS. The first slot of every flow \(F_{i}\) is termed its Initial Slot \(IS_{i}\) Consequently, slots of the form \(IFG_{i}\cdot m+IS_{i}\), \(m=0\), \(1\)... are removed from the set of available slots of the PS. The algorithm continues with the next flow, which is shifted to the first available slot of the PS, until all CEXP flows have been inserted into the PS. For the CEXP flows, we assume the following:  
\[\sum_{i=1}^{N}\frac{1}{IFG_{i}}\leq 1,\textit{ where }N\ =\ \textit{number of CEXP flows} \tag{1}\]  
\[\textit{For every two flows }F_{i},F_{k},\textit{ it must hold }:IS_{i}-IS_{k}\ \
eq\ n\cdot(IFG_{i}\ -\ IFG_{k}). \tag{2}\]  
Equation (1) ensures that the network is not oversubscribed, whereas Equation (2) states that for every two flows \(F_{i}\) and \(F_{k}\), the difference of their initial slots \(IS_{i}\) and \(IS_{k}\) should not be a multiple of the difference of their IFGs since this would result in a collision. Figure 5 shows an example of the result of the CEXP-Fit algorithm described above for three flows \(F_{1}\), \(F_{2}\), and \(F_{3}\) with IFGs of 4, 8, and 16 slots, respectively.
## Materials and Methods
### Prediction Models
#### 2.3.1 Stepwise Linear Regression (SLR) Model  
The SLR model was used to construct the relationships between SOC and time series NDVI data [44]. The SLR model can be expressed as follows:  
\[Z(x_{0})=\beta_{0}+\sum_{i=1}^{n}\beta_{i}*p_{i}+\varepsilon, \tag{2}\]  
where \(Z(x_{0})\) is the estimated variable of SOC, \(\beta_{0}\) is a constant term, \(\beta_{i}\) represents the regression coefficients, \(p_{i}\) denotes the independent variables of time series NDVI data, and \(\varepsilon\) indicates the residuals. The SLR model was operated on SPSS statistics 23.
## 4 System modeling
### Partial least square regression (PLSR)  
PLSR is another modified LSR, extracting uncorrelated latent variables from the original data. The difference between PLSR and PCR is the extraction of latent variables in PLSR not only employs independent variables **X** but also take into account dependent variables **Y**.  
A general linear model is **Y** = **XB** + **E**, where **B** is the regression coefficients and **E** is the residual matrix. PLSR generates a transforming matrix **W** to transform **X** to **T** ( **T** = **XW**, called score matrix or PLS components). Let **A** = **WB**, then the PLS regression model turns to be **Y** = **T**A** + **E**. Thus, the regression of **X** against **Y** becomes the regression of PLS components **T** against **Y**. One standard PLS algorithm is nonlinear iterative partial least squares (NIPALS). The details of this algorithm are discussed in the literature [52].  
Both PCR and PLSR were designed for quantitative estimation problems and classification problems as well. As an effective linear function approximation tool, PLSR has been widely applied to E-nose design [53, 54, 55].
## 5 Conclusions  
In this paper, a 3D array for enhancing the FoV of phased arrays for mobility applications is presented. FoV is computed by considering both directivity and axial ratio with a requirement of 3 dB of scan loss and maximum axial ratio of 3.5 dB.  
Figure 14: (**a**,**c**) Directivity and (**b**,**d**) axial ratio map vs. steering in elevation and azimuth for the (**a**,**b**) planar array and for the (**c**,**d**) 3D array T2. The contour highlights the requirement \(-3\) dB for the directivity and 3.5 dB for axial ratio. The red square highlights the achieved FoV.  
Figure 15: Spherical directivity at \(\theta_{s}=70^{\circ}\) and \(\varphi_{s}=45^{\circ}\) for (**a**) planar array and (**b**) 3D array for T2 configuration. The zoom is normalized with respect to its maximum along all the steering angles.  
Conformal array are an easy and efficient solution for improving the FoV. While the ideal conformal shape is challenging to manufacture, faceted solutions are investigated with the goal to enhance the FoV while keeping minimized the number of facets. An analysis on different linear faceted arrays permits to find the best candidate for improving FoV. The case for \(N_{f}=3\) is chosen for building a 3D array.  
The proposed 3D solution permits to improve the directivity and total FoV from \(\pm 24^{\circ}\) to \(\pm 70^{\circ}\) with a reduced number of facets (\(N_{f}=5\)). Also, axial ratio FoV is significantly improved from \(\pm 31^{\circ}\) of the planar reference case to \(\pm 76^{\circ}\) of the 3D array. Overall, the total FoV is increased by \(\pm 46^{\circ}\). The solution can be manufactured through planar PCBs avoiding complex and costly dedicated manufacturing.  
As proof-of-concept, the 3D array is demonstrated for the Ka-band in receive mode, but analogous solutions can be achieved in other frequency bands. Specially, multi-band antennas able to support both the transmitter and receive part would be beneficial for avoiding shadow between the arrays and maximally exploiting the benefits of the 3D arrangement.  
Conceptualization, F.B., G.F.R. and S.C.; Data curation, F.B., G.F.R. and S.C.; Formal analysis, F.B., G.F.R. and S.C.; Investigation, F.B. and G.F.R.; Methodology, F.B., G.F.R. and S.C.; Project administration, S.C.; Software, F.B. and G.F.R.; Supervision, F.B. and S.C.; Validation, F.B. and G.F.R.; Visualization, F.B. and G.F.R.; Writing--original draft, F.B.; Writing--review and editing, F.B., G.F.R. and S.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data can be shared upon request.  
The authors declare no conflict of interest.
# Field Trials of SC-FDMA, FBMC and LP-FBMC in Indoor Sub-3.5 GHz Bands
## 4 Experiment Results
### BER Comparison According to Timing/Frequency Offsets
In [6], the Vehicular-A channel model in the international telecommunications union-radio communication (ITU-R) sector was used. Thus, it is revealed that there is more inter-user interference in the real indoor channel than in the Vehicular-A channel. Note that this trend is most emphasized in the lowest carrier frequency, i.e., \(F=600\) MHz in our field test. This implies that the inter-user interference becomes more detrimental as the frequency selectivity increases. Meanwhile, the BERs of FBMC and LP-FBMC are almost invariant to \(\tau\) and \(\epsilon\). In particular, no matter how large \(\tau\) and \(\epsilon\) are given in the considered region, LP-FBMC achieves the identical BER performance to that of perfectly synchronized SC-FDMA. Again, the BER performance gains of LP-FBMC over FBMC and SC-FDMA are biggest in the lowest carrier frequency, i.e., \(F=600\) MHz.