esa-sceva/satcom-chunk-collection huggingface dataset:
# Parametric Analysis for Hybrid-Electric Regional Aircraft Conceptual Design and Development
## 3 Simulation Results and Discussion
### Battery Technology Impact on Hybrid-Electric Regional Aircraft Design
#### 3.1.2 Effect of Varying Design Range
With respect to the thermal benchmark, which exhibits an MTOW = 16,404 kg, an \(m_{fb}=1487\) kg, and a \(PREE=0.859\), reductions in \(m_{fb}\) of 6.7% (with an increase in MTOW of 77%) are obtained for BED = 500 Wh/kg, hence not introducing significant overall benefits in this regard, nor are there any benefits in terms of \(PREE\) (see the right part of Figure 10). Figure 10: \(m_{fb}\) vs. MTOW (**left**), \(PREE\) vs. MTOW (**right**) varying \(BED\), for optimisations with FoM \(\triangleq\)\(m_{fb}\) @800 nm. From the previous analyses and discussion, it is apparent that there are three distinct and interdependent boundaries that define a confined field for the effective implementation of hybrid-electric propulsion for regional transport aircraft. The first refers to battery technology development and points out that _BED_ values (at battery pack level) of 500 Wh/kg must be achieved if effective and efficient integration of hybrid-electric propulsion on transport aircraft is to be envisaged. This is not sufficient unless the aircraft design requirements are properly tuned. In particular, benefits in terms of fuel consumption begin to be achieved if design ranges up to 600 nm are considered. Reducing the range to 400 nm leads to marked improvements in fuel consumption on the standard mission, at the expense of reductions in the aircraft operating capabilities. However, even with these reduced design range values (i.e., 400 nm), most of the typical routes in the current regional sector would be covered. Increasing the design range above 600 nm, on the other hand, would compromise the effectiveness of the integration of hybrid-electric propulsion, as there are no advantages to be gained in terms of fuel consumption and, therefore, no benefit from the reduction of greenhouse gas emissions viewpoint, which is indeed the driver for the development of this technology. The third element required for an effective implementation of hybrid-electric propulsion relates to the selection of figures of merit steering the design process. Specifically, it is highlighted that the technological and weight limitations of batteries need to be overcome through a paradigm shift in design development, in which increases in aircraft weight and reductions in energy efficiency must be taken into account if fuel consumption and related direct greenhouse emissions are to be minimised.
# Seismic Performance of LRS-FRP-Concrete-Steel Tubular Double Coupling Beam
## 4 Seismic Performance
### Stiffness Degradation  
The stiffness reflects the ability of the member to resist deformation under loading. The stiffness of specimens reduces with the fracture of concrete, the cracking of LRS-FRP, the buckling of steel tubes, and slippages between steel and concrete. The secant stiffness was used to analyze the stiffness degradation of specimens, which can be calculated with Equation (3).  
\[K_{j}=\frac{\left|+F_{j}\right|+\left|-F_{j}\right|}{\left|+X_{j}\right|+\left| -X_{j}\right|} \tag{3}\]  
where, \(+F_{j}\) and \(-F_{j}\) are the positive and negative strength corresponding to the current maximum positive deformation \(+X_{j}\) and the maximum negative deformation \(+X_{j}\), respectively.  
The normalized stiffness is the stiffness ratio between each main cycle \(K_{j}\) and the first cycle \(K_{1}\). The normalized stiffness of all specimens is illustrated in Figure 13. Compared to the control specimen RC-DCB, the stiffness of the other strengthened specimens dropped slightly slower in the initial stage due to the effect of the external wrapped PET-FRP sheet or the inner steel tube. Since the limited constraint effect is provided by the PET-FRP material, the normalized stiffness of the LRS-DCB specimen dropped faster than those with embedded steel tubes in the initial stage. With the deformation increase, the increasing confined stress limited the stiffness degradation, which caused the slope of the curves to flatten out. The initial stiffness of the DST-CB specimens was mainly controlled by the inner steel tube, which dropped at a slower rate than that of the LRS-DCB specimen. With the increase of deformation, the inner steel tube yielded, and the PET-FRP material constraint effect controlled the stiffness degradation of the specimens.  
Figure 13: Stiffness degradation: (**a**) secant stiffness; (**b**) normalized stiffness.
# Inter-System Differencing between GPS and BDS for Medium-Baseline RTK Positioning
## 5 Discussion  
In the proposed inter-system differencing model, the GPS-BDS DISBs are real-time estimated from epoch to epoch. Some recent contributions indicated that a priori calibration of DISBs led to better AR and positioning performance [11; 13; 14; 15; 35]. Indeed, between GPS L1-Galileo E1 and other cases with overlapping frequencies, the fractional phase DISB can be easily obtained from fixed integer ambiguities. If the DISBs are verified to be stable, they can be directly applied to calibrate subsequent calculation. However, when calculating phase DISB with different frequencies, the influence of between-receiver SD ambiguity of reference satellite cannot be separated from phase DISB unless the SD ambiguity can be calculated in advance. The SD ambiguity is usually calculated approximately using pseudoranges. The error of the SD ambiguity will directly introduce the bias for the phase DISB. What is more, even though the phase DISB are precisely calibrated, one also needs to face the problem of SD ambiguity again in the subsequent calculation. Only if the SD ambiguity is precisely solved, can the DD ambiguities be fixed to integers. Considering that the SD ambiguity is hard to solve precisely within a short time, we thus adopt the strategy of real time estimation.  
Although the inter-system differencing model in this paper is proposed for relative RTK positioning, we think it can also be similarly used in the un-difference (UD) model, e.g., precise point positioning (PPP), as long as the UD inter-system biases are verified to be stable. In the UD model, one can estimate only one receiver clock parameter together with an ISB parameter between two systems. Then, the ISB parameter can be modelled with some constraints, e.g., as a constant or the random walk process, and thus the positioning performance could also be improved.
# Measurement of Head-Related Transfer Functions: A Review
## 2 Principle of HRTF Measurements
### HRTF as a Linear Time Invariant (LTI) System  
A HRTF represents a linear time invariant (LTI) system (an approximation under certain conditions) between an acoustic point sound source in the free-field and a defined position in the listener's ear canal (static scenarios). Theoretically, all methods for identifying the transfer function of an LTI system can be applied to HRTF measurements.  
Figure 1: An overall structure of the rest of this review paper.  
Figure 2 shows the basic principle of signal processing through an LTI system [6]. In the time domain, the output signal \(y(t)\) in response to the input signal \(x(t)\) is expressed as:  
\[y(t)=x(t)*h(t)=\int_{-\infty}^{\infty}x(\tau)\;h(t-\tau)\;d\tau, \tag{1}\]  
where \(*\) denotes the convolution operator, and \(h(t)\) represents the impulse response of the LTI system. Equivalently, in the frequency domain, the relationship between the input \(X(f)\) and the output signal \(Y(f)\) can be formulated as:  
\[Y(f)=X(f)\cdot H(f), \tag{2}\]  
where \(H(f)\) is the transfer function of the LTI system. The signal transformation from the time to the frequency domain can be realized with the Fourier transformation (FT). Based on Equation (2), the transfer function of an LTI system under testing can easily be calculated from the excitation signal and the system response in the frequency domain:  
\[H(f)=\frac{Y(f)}{X(f)}. \tag{3}\]  
The corresponding system impulse response, \(h(t)\), can be obtained by applying the inverse Fourier transformation (IFT) to \(H(f)\). In the digital domain, fast Fourier transformation (FFT) and its inverse (IFFT) are commonly used to efficiently convert discrete signals between time and frequency domains (the signal length should be a power of 2). Directly dividing the spectrum of the system response by the excitation signal (cyclic deconvolution) may cause aliasing errors (cyclic shifts in the time domain). Thus, prior to the division in the frequency domain, the excitation signal and the system response are zero padded to double their original lengths (or double their FFT length) to avoid the aliasing (linear deconvolution) [37; 38]. Furthermore, to avoid the division by small values, a suitable regularization method should be considered [39]. Alternatively, \(h(t)\) can be obtained by convolving the system response with the inverse of the excitation signal (\(x_{inv}(t)\)) [40]:  
\[h(t)=y(t)*\text{IFT}\left\{\frac{1}{X(f)}\right\}=y(t)*x_{inv}(t). \tag{4}\]  
Except for this classical deconvolution method, several other approaches have been developed based on the properties of excitation signals to be used. As an example, the time-reversed filter and circular cross-correlation methods are particularly suitable for deconvolving some sweep signals and pseudo random sequences, respectively (see Section 2.2) [37; 38; 40; 41].
## 3 Synthesis of Results
### Risk of Bias  
The risk of bias of the main articles reviewed is shown in Table 1. This review has a moderate risk of bias.  
The result of the search reveals that, with the MeSH terms \"microbiology\" AND \"dentistry\" there are 30,353 articles; with \"microbiology\" AND \"periodontology\" there are 6772 publications; with \"microbiology\" AND \"dental\" AND \"implants\", 1580 articles; with \"microbiota\" AND \"dentistry\", 2609 publications; with \"microbiota\" AND \"periodontology\", 994 articles; and with \"microbiota\" AND \"dental\" AND \"implants\", 268 publications.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Adequate** & **Allocation** & **Blinding** & **Incomplete** & **Registration** \\  & **Sequence Generated** & **Concalment** & **Outcome Data** & **Outcome Data** \\ \hline [PERSON] et al., 2015 [17] & & & & \\ [PERSON] et al., 2019 [21] & & & & \\ [PERSON] et al., 2021 [24] & & & & \\ [PERSON] et al., 2017 [26] & & & & \\ [PERSON] et al., 2016 [27] & & & & \\ [PERSON] et al., 2019 [28] & & & & \\ [PERSON] et al., 2016 [29] & & & & \\ [PERSON] et al., 2019 [30] & & & & \\ [PERSON] et al., 2013 [31] & & & & \\ [PERSON] et al., 2021 [32] & & & & \\ [PERSON] et al., 2012 [33] & & & & \\ [PERSON] et al., 2014 [34] & & & & \\ [PERSON] et al., 2015 [35] & & & & \\ [PERSON] et al., 2018 [36] & & & & \\ [PERSON] et al., 2018 [37] & & & & \\ [PERSON] et al., 2021 [38] & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Risk of bias of studies is represented by the green symbol, low risk of bias; and the yellow symbol, moderate risk of bias.  
Regarding the MeSH terms \"microbiology\" AND \"dental implants\", production mainly consists of original articles (>85%) and reviews (>11%). The other studies are conference paper (1.4%) and letter, book chapter, editorial, note, conference review, short survey and retracted (<1%). For the MeSH terms \"microbiota\" AND \"dental implants\", the main publications are original articles (>80%), reviews (>14%), and book chapter (>3%). Less cited are conferences and books (<1%).  
Figure 1 shows the trend of publications over the years regarding peri-implant bacterial population studies. Using the MeSH terms \"microbiology\" AND \"dental\" AND \"implants\", the first study present on Scopus dates to 1970 [39], however on PubMed (MEDLINE) until 1977 [40] there are no studies; with the MeSH terms \"microbiota\" AND \"dental\" AND \"implants\", the first study present on both Scopus and PubMed (MEDLINE) is from 1986 [41].  
There is a high increase in the number of publications with the MeSH term \"microbiology\", in contrast with \"microbiota\", since this term had not yet been introduced. From 2014 to 2015, an increase is seen with the MeSH terms \"microbiology\" and \"microbiota\", from 86 articles to 124 and from 11 to 25, respectively; from 2015 to 2021, the trend for \"microbiology\" is decreasing, from 124 to 67 articles, while for \"microbiota\" it is increasing, from 25 to 31 publications, since in the literature this term has been used recently to define resident microorganisms in the oral cavity.
# GHEFL: Grouping Based on Homomorphic Encryption Validates Federated Learning
## 2 Scheme Statement
### Design Goals  
One objective of this scheme is to protect the data privacy of participants, ensuring that the server cannot access the gradients and related information of individual participants, thereby preventing malicious servers from attempting to extract participants' local data. Simultaneously, it guarantees that participants cannot access the private information of other participants. Another objective is to ensure that participants obtain correct global aggregation results, allowing participants to verify the correctness of the aggregation results. This aims to prevent malicious servers from performing incorrect aggregation operations, which could disrupt the model training process.
# BFRVSR: A Bidirectional Frame Recurrent Method for Video Super-Resolution
## 3 Methods
### Bidirectional Frame Recurrent Video Super-Resolution (BFRVSR)
#### 3.1.3 Warping HR Image  
Use the obtained high-resolution optical flow diagram and the high-resolution image of the previous frame to estimate the high-resolution image of the current frame.  
\[\overline{I}_{t}^{HR}=\textit{Warp}\left(I_{t-1}^{HR},F_{t\to t-1}^{HR}\right) \tag{3}\]  
We implemented warping as a differentiable function using bilinear interpolation similar to [PERSON] et al. [42].
[PERSON] et al. [19] reported that the CPUAS had a poor droplet coverage rate, droplet density, and deposition uniformity, leading to a slightly lower control effect on pepper comparing with the electric air-pressure knapsack sprayers. [PERSON] et al. [20] reported the good control effects of aphids and spider mites of 63.7% and 61.3% when the FH were 1.5 and 2 m above cotton. [PERSON] et al. [21] and [PERSON] et al. [22] reported that when the FS was 5 m/s and the FH was 1.5 m, the maximum deposition volume could be obtained in the lower layer of rice canopy. [PERSON] et al. [23] suggested coarse droplet size and higher spray volume on wheat pests and diseases for better control effects with CPUAS. [PERSON] et al. [24] suggested small particle sizes droplets to improve the control effect of rice plant hoppers for CPUAS. The above studies focus on the selection of CPUAS application parameters, and the results have proved the feasibility of CPUAS for insect pests and diseases control of crops and promoted the popularization and application of CPUAS [7, 16, 25]. However, one factor is easy to be overlooked during the CPUAS application. As a kind of agricultural crop protection machinery with the inevitable trend for the intelligent development of green agriculture [26, 27], its application should fully combine with the agronomy, so it is critical to analyze and evaluate the application with agronomies for achieving ideal and expected control effects. The occurrence time and the position on the plant of the insect pests and diseases as well as the plant height and density should be considered into the actual pesticide application. As a result of different insect pests and diseases occurrence laws,the pesticide liquid droplet deposition should be targeted to achieve better control effects. Meanwhile, the operation parameters should be changed with different plant physiological characteristics in the field, correspondingly affecting the droplet deposition characteristics. This study aims to investigate the effects of the CPUAS operation parameters on the ESW and droplet deposition characteristics on the wheat canopy. Based on the investigations, the optimized parameters have been chosen to control the aphids, powdery mildew, and head blight combined with the occurrence laws and the wheat plant characteristics. The control effects have been evaluated. The studies would provide some references for the scientific application of the CPUAS.
## 1 Introduction
### Literature Review  
Since the SOC estimation is essentially based on the battery model for most SOC estimation methods, an accurate battery model and matched model parameters are the prerequisite for accurate SOC estimation. Obviously, model accuracy is closely related to model structure and model parameter identification algorithm. The equivalent circuit models (ECMs) are the most common battery models adopted in the actual BMS because of few computations and acceptable precision [9]. The popular and widely reported ECMs include the Rint model, Thevenin model, partnership for a new generation of vehicles (PNGV) model, and general nonlinear (GNL) model [10; 11; 12], which are all based on resistance-capacitance (RC) networks with different orders. The model structure and electronic component values of the ECM directly affect the features and accuracy of the model. [PERSON]. [13] investigated eleven ECMs and stated that the second-order RC models are the best choice owing to their balance of accuracy and reliability. However, ECMs are not empirical models, which means the model parameters have no clear electrochemical meaning [14]. Therefore, the model errors may be large, especially in low SOC area (SOC lower than 20%) [15]. Generally, the empirical model needs large computations and memories due to its complex equations, and currently it is still hard to use in a BMS for on-line estimation and real-time control. Based on the structure of ECM, [PERSON]. [15] proposed an extended equivalent circuit model (EECM) considering partial electrochemical properties. However, the reliability of parameter identification is doubtful because nine parameters need to be identified in proposed EECM.  
The most popular existing approaches for parameter identification of ECMs include the genetic algorithm (GA) [16], particle swarm optimization (PSO) algorithm [17], and the least-squares method [18]. The appropriate identification algorithm should match the battery model to pursue the balance between accuracy and computation. Moreover, the local optimization problem should also be avoided in the process of model parameter identification.  
We could conclude that an improved ECM considering partial electrochemical properties is a better choice to balance accuracy and computational burden for online application. Moreover, appropriate model parameter identification and SOC estimation algorithm are essential for improving the accuracy and robustness of SOC estimation.
# Digital Twins in Software Engineering--A Systematic Literature Review and Vision
## 4 Results
### RQ3: How Can a DT Be Built in the Context of the Software Industry?
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Usage** & **Title** & **Reference** \\ \hline \multirow{3}{*}{Insider Thread DT} & Creating a DT of an Insider Threat & \multirow{3}{*}{[87]} \\  & Detection Enterprise Using & \\  & Model-Based Systems Engineering & \\ \hline \multirow{3}{*}{Ethical DT} & Responsible-AI-by-Design: A Pattern & \multirow{3}{*}{[173]} \\  & Collection for Designing Responsible & \\  & AI Systems & \\ \hline DT State of Quality in & Concept of Quality DT in & \multirow{3}{*}{[162]} \\  & Agile Process & Agile Development & \\ \hline Architecture maintenance by DTs & Co-evolving digital architecture twins & [146] \\ \hline Cockburn procedure for soft dev: & Implementing DTs in & \multirow{3}{*}{[174]} \\  & User Stories \textgreater{} Scenarios & existing infrastructures & \\ \hline Continuous Monitoring of the & \multirow{3}{*}{The Digital Value Stream Twin} & \multirow{3}{*}{[165]} \\  & Value Stream & \\ \hline \multirow{3}{*}{Chaos Twins to create anomalies.} & Chaos Twin: A Chaos Engineering and & \multirow{3}{*}{[150]} \\  & DT Approach for the Design of & \\  & Resilient IT Services & \\ \hline \multirow{3}{*}{PADTCs} & Process-aware DT cockpit synthesis & \multirow{3}{*}{[167]} \\  & from event logs & \\ \hline Architecture Maturity Evaluation & Continuous engineering for Industry & \multirow{3}{*}{[114]} \\  & Improvement & 4.0 architectures and systems & \\ \hline \multirow{3}{*}{Cybersecurity} & DT for Cybersecurity Incident & \multirow{3}{*}{[129]} \\  & Prediction: A Multivocal & \\  & Literature Review & \\ \hline \multirow{3}{*}{Version Control DTs} & Integrated Version Control of physical & \multirow{3}{*}{[116]} \\  & and virtual artifacts & \\ \hline \multirow{3}{*}{SOC/Compliance} & Towards Process-Oriented IIoT Security & \multirow{3}{*}{[217]} \\  & Management: Perspective & \\  & and Challenges & \\ \hline DTs for TDD, test cases’ & ART for Agile: Autonomous Real-Time & \multirow{3}{*}{[152]} \\  & Testing in the Product & \\ \cline{1-1}  & Development Cycle & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Specific use cases for DTs in software engineering. Among the exposed cases, some DTs could combine efforts to cover more ground in ALM. This combination of multiple DTs could be a roadmap for organizations starting their journey with DTs as part of their digital transformation process. The evaluation and prioritization of this roadmap is a critical activity to understand the most beneficial path in terms of ROI. Every organization could have different needs based on its maturity and context.
# Research on Defect Detection Method for Composite Materials Based on Deep Learning Networks
## 4 Results and Discussion  
### Datasets  
The dataset is expanded here due to the limited amount of original composite data. The expanded section utilizes the steel dataset from Northeastern University, which shows a significant similarity to the composite image features. The composite material image dataset is derived from the CT scanning process of an industrial production company. The dataset consists of a total of 266 images. The steel dataset was selected to obtain 500 images, while the entire dataset consists of a total of 766 images. This includes both positive samples with defects and negative samples without defects, as shown in Figure 4. The size of the dataset images is 2048 \(\times\) 2048. After preprocessing, which includes denoising and feature enhancement, the image size is reduced to 512 \(\times\) 512. The images are all single-channel, 8-bit grayscale images. During processing, the dataset is divided into 606 sheets for the training set and 160 sheets for the testing and validation sets. The training set is used to train the model. The validation set is used to compare and evaluate the performance of the model.  
In order to validate the scalability of the classification model based on the Attention Ghost module, in this paper, the model's performance is tested using datasets from various industrial domains. These include the MT Defect Dataset [24], the ATEX Dataset, and the Bridge Crack Dataset [25; 26]. The three datasets record material defect data from various industrial domains. The MT Defect Dataset includes six different types of surface defects on tiles. From the MT Defect Dataset, 400 images of both positive and negative samples are selected in this paper. Among them, 60 images of positive and negative samples are randomly selected for the test set, while the remaining images are used for the training set. The Bridge Crack Dataset focuses on crack detection and showcases various surface features and crack patterns. From the ATEX Dataset and the Bridge Crack Dataset, 80 images with positive and negative samples are selected as the training set, while 20 images with positive and negative samples each are used as the test set in this paper, respectively. The positive and negative samples in the dataset are shown in Figure 5.