esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Referring to Remark 7 of [25], the PD controller can be reasonably used for discrete-time cases in the case of a high accuracy state observer. However, the observer-based fuzzy control was not developed in [25; 26] due to the computation complexity. The observer-based PD fuzzy control scheme is still an open problem for the discrete-time nonlinear systems to the best of the authors' knowledge. Thus, the motivation of this paper is to overcome the above difficulty and develop a reasonable observer-based PD feedback fuzzy control scheme for discrete-time nonlinear singular systems. For the advantages of T-S fuzzy models and the motivations mentioned above, the stabilization problem of observer-based control for discrete-time T-S fuzzy singular systems was studied in this paper. By using the T-S fuzzy modeling approach, the nonlinear singular systems can be approximated efficiently with some local linear models blending by membership functions. Meanwhile, the observer-based fuzzy controller was designed to reasonably use the PD feedback scheme in discrete-time cases. Then, according to Lyapunov theory and the transformation technologies [34; 35], the inverse problems were solved and sufficient stability criteria can be developed in terms of LMI, which can be conveniently calculated with convex optimization algorithms [36]. The main contributions of this paper can be summarized as follows: (i) The proposed observer feedback fuzzy control method is more generalized to conform to reality than the state feedback fuzzy control approaches [25; 26] when the system states cannot be measured. (ii) Extra freedom variables of the LMI stability conditions derived by projection lemma let designers get more degrees of freedom when solving the observer-based fuzzy control problems. (iii) With the fuzzy observer design and PD feedback scheme, an observer-based PD feedback fuzzy controller with novel stability conditions was derived to ensure the stability of discrete-time T-S fuzzy singular systems. To verify the effectiveness and application of the proposed observer-based fuzzy control method, the control problem of a bio-economic system was presented. Some comparisons between the proposed observer-based fuzzy control method and previous fuzzy control approaches were also provided in the simulations. The organization of this paper is presented as follows. The nonlinear singular system is represented as a T-S fuzzy model, and some necessary assumptions are proposed in Section 2. The sufficient stability conditions for the singular system are derived in Section 3. A biological economic system is proposed to verify the effectiveness of the proposed fuzzy control method in Section 4. Finally, the conclusions and future works are collected in Section 5.
# Effects of Wind Barrier Porosity and Inclination on Wind Speed Reduction
###### Abstract  
Wind barriers play a vital role in protecting sablings until maturity when planted as vegetative windbreak forests. Most previous studies have focused on the porosity of wind barriers, but no studies have simultaneously examined the effects of the porosity and inclination, despite the potential of the inclination to decrease wind speed. We tested three wind barrier cases in wind tunnel experiments: (1) Case A (porosity of 0% with inclinations (90\({}^{\circ}\), 80\({}^{\circ}\), and 70\({}^{\circ}\))), (2) Case B (porosity of 25% with inclinations (90\({}^{\circ}\), 80\({}^{\circ}\), and 70\({}^{\circ}\))), and (3) Case C (porosity of 50% with inclinations (90\({}^{\circ}\), 80\({}^{\circ}\), and 70\({}^{\circ}\))). The vertical and horizontal wind velocities were measured at three vertical and seven horizontal points behind the barriers. The results demonstrated a statistically significant decrease in the correlation between the distance and mean wind velocity for all cases, with up to a six-fold wind protection effect. The wind barrier with 0% porosity and a 90\({}^{\circ}\) inclination provided the highest degree of wind protection. However, the wind protection range was limited downwind, and recirculation of wind flow could occur in the leeward direction, potentially damaging sablings. A wind barrier with 50% porosity and 70\({}^{\circ}\) inclination sufficiently decreased the wind velocity and prevented recirculation of wind flow, demonstrating that both porosity and inclination considerably impacted the wind protection effect by reducing wind velocity. Our findings offer novel insights into the influence of wind barriers with varying porosities and inclinations and can provide valuable guidance for constructing efficient windbreak forests.  
wind barrier; wind barrier inclination; wind barrier porosity; windbreak forest; wind tunnel experiment; wind velocity reduction
# Leveraging Blockchain Technology for Secure 5G Offloading Processes +
## 2 State-of-the-Art
This need is already known for 5G general processes, as stated in [30], where several challenges are identified, such as the need for secure authentication, key management, and protection against various attacks. Despite the advancements in 5G authentication [31], several challenges remain, especially when dealing with a high number of connected devices. Additionally, as offloading process mean integration with various networks, secure and efficient cross-network authentication mechanisms are required [32]. Self-Sovereign Identity (SSI) [33] is emerging as a promising approach for authentication in 5G networks, offering enhanced privacy, security, and user control.
## Materials and Methods
### System Characterization
#### 2.3.1 Scattering Angle Characterization  
To characterize system's sensitivity to changes in scattering angle, an experiment was designed in which light was reflected back at known angles. The dual-axis MEMS mirror was placed just beyond the focal plane of the objective lens and raster-scanned to collect a range of pathlength intensities corresponding to bulk scattering angles (Figure 8). The incident beam was not scanned in this configuration, only the angle at which the light was reflected from the dual-axis MEMS mirror in the focal plane. Orientation of the dual-axis MEMS mirror varied by \(\pm\)4 degrees in the x- and y-directions; thus, several raster scans were collected with various tilt offsets and stitched together in post-processing.  
Depths corresponding to the MEMS mirror at Pathlengths 1-3 were isolated, and then intensity values corresponding to a given reflection angle were averaged azimuthally in reference to the neutral position (degree 0,0). Results are given in Section 3.2.
# WATS-SMS: A T5-Based French Wikipedia Abstractive Text Summarizer for SMS
## 1 Introduction
These works are mostly based on extractive techniques that amount to ranking sentences and selecting the best ones. Usually, they produce summaries that are between one quarter to half the length of the original page. Although some works such as [PERSON] et al. [24] deals with word limitation when generating the summary, it remains very difficult to consider character limitation as imposed by SMS. Indeed, the summary should fit in a maximum of three SMS. Depending on the mobile carrier, the number of characters should not exceed 455, otherwise the message will be transformed into MMS, requiring adapted devices and additional cost compared to SMS. Moreover, due to the unpredictable relationship between the number of words or sentences and the number of characters, summarizing a webpage into SMS is a real challenge. The first attempt to consider text summarization with character limitation is provided in [25]. The authors used an extractive approach based on the combination of LSA and TextRank algorithms. Although their approach generates summaries with a limitation on the number of characters, the major constraint is the redundancy while producing the summary and the lack of context understanding. In fact, the generated summary is based on the computation of the weight of the words in the sentences, with a complete unawareness of the context. Although some other works try to understand the context during the summarization [26; 27; 28; 29; 30; 31], they do not take into consideration the limitation in terms of the number of characters. This paper introduces WATS-SMS, a French Wikipedia Abstractive Text Summarizer that aims to summarize French Wikipedia pages into SMS and to provide summaries directly on the user's device. It is built by applying a transfer learning technique to fine tune a pre-trained model on French Wikipedia pages. The length of the summary is checked before producing the final summary. The fine-tuning process quickly becomes the fast approach used to build a custom model due to the huge requirements needed to train a model from scratch and the good performance shown by fine-tuned models [32]. The rest of the paper is organized as follows: Section 2 briefly presents the related works on text summarization. Section 3 explains the general architecture of WATS-SMS, as well as details of its main components. Section 4 presents and discusses the quality of summaries based on comparison with existing approaches. Section 5 presents a demonstration of the solution on a basic phone. This paper ends with conclusions and directions for future works, as discussed in Section 6.
## 4 Results
### Risk Assessment-Related Standards
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Name** & **Description** & **Cat.** \\ \hline
[102] & ISO 21434: Automotive Cyber Security & Standard focusing on cybersecurity for road vehicles. & Std. \\ \hline
[116] & Risk Management Architecture: INL Framework & Aligning with the NIST framework for managing cybersecurity risks. & Fwk. \\ \hline
[116] & IEEE 1547.3 & Guide for cybersecurity of distributed energy resources interconnected with electric power systems. & Std. \\ \hline
[107,120,122,133] & ISO/IEC 27001 & Information security management standard. & Std. \\ \hline
[107,109,113,120,122,124,127,133,134] & NIST Cybersecurity Framework (CSF) & Guidelines for improving CI. & Gdl. \\
[9] & Cyber Resilience Framework (CRF) & Structured approach for ensuring resilience in information systems. & Fwk. \\ \hline
[9] & CERT Resilience Management Model (CERTRMM) & Framework for managing operational resilience. & Fwk. \\ \hline
[9,112] & COBIT & Framework for enterprise IT governance and management. & Fwk. \\ \hline
[9,128] & OWASP & Open Web Application Security Project framework for software security. & Fwk. \\ \hline
[121] & Sendai Framework for Disaster Risk Reduction 2015–2030 & Global strategy for reducing disaster risk and building resilience. & Fwk. \\ \hline
[121] & UN Guidelines on Man-Made and Technological Hazards & Guidelines provided by the UN Office for Disaster Risk Reduction (DRR). & Gdl. \\ \hline
[130] & ISO 27001:2013 & Updated version of ISO 27001 focusing on information security management. & Std. \\ \hline
[109,120,122,130,133] & ISO 27005 & Risk management standard for information security. & Std. \\ \hline
[22] & ISO 31010 & Standard for risk assessment techniques. & Std. \\ \hline
[117,132] & ISO 31000 & Guidelines for risk management. & Std. \\ \hline
[22,68,108,118,123,144] & NIST SP 800-30 & Guide for conducting risk assessments. & Gdl. \\ \hline
[22,129,144] & MITRE & Framework for threat analysis and mitigation. & Fwk. \\ \hline
[22,133] & ENISA & European Union Agency for cybersecurity recommendations and guidelines. & Gdl. \\ \hline
[112] & COBIT 2019 & Updated COBIT framework for IT governance and management. & Fwk. \\ \hline
[112] & ISO 42001:2023 & Standard for governance, risk, and compliance (GRC). & Std. \\ \hline
[115] & NISTIR: Guidelines for Smart Grid Cybersecurity & Guidelines for cybersecurity of smart grids. & Gdl. \\ \hline
[111] & NIST AI RMF & NIST AI risk management framework for managing AI risks. & Fwk. \\ \hline
[22] & DO326A/ ED-202A & Guidelines for cybersecurity in aviation. & Gdl. \\ \hline
[129] & NIST SP 800-160, Volume 2 & Systems security engineering guidelines. & Gdl. \\ \hline
[134] & IEC 61850 & International standard for communication networks and systems in substations. & Std. \\ \hline
[39] & ISO/SAE 21434:2021 & Standard for cybersecurity engineering in road vehicles. & Std. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Risk assessment-related standards (Stds. ), guidelines (Gdls. ), and frameworks (Fwks. ).
## 3 GLCM-Based Image Texture Features
### Statistical Features of GLCM  
For the texture analysis of an image, the GLCM is often not directly used as the texture features of the image. In fact, the statistical features extracted from the GLCM are used as the texture features.  
Before extracting the statistical features from the GLCM, the frequency \(P(i,j,d,\theta)\) should be normalized to co-occurrence probability \(g(i,j)\) as follows:  
\[g(i,j)=P(i,j)/\sum_{i=0}^{N-1}\sum_{j=0}^{N-1}P(i,j). \tag{19}\]  
[PERSON] et al. [47] suggested many statistical features, such as maximum probability, entropy, contrast, correlation, energy, and inverse differential moment. Three of them were calculated here: entropy, contrast, and energy. This is because the results of an experiment conducted by [PERSON] and [PERSON] [50] showed that these statistical features are suitable for effective classification and image recognition.  
Entropy is used to characterize the information capacity of an image and to characterize the complexity or non-uniformity of image texture. The entropy is highest when the image is full of texture and lowest when there is no texture:  
\[\text{Entropy}=-\sum_{i=0}^{N-1}\sum_{j=0}^{N-1}g(i,j)\log(g(i,j)). \tag{20}\]  
Contrast is a measure of the degree of texture clarity, and it is expressed as in Equation (20). The deeper the texture groove, the greater the contrast in the image, and vice versa. For example, the contrast of a constant image is zero, as there is only a single non-zero element in the GLCM.  
\[\text{Contrast}=\sum_{i=0}^{N-1}\sum_{j=0}^{N-1}(i-j)^{2}g(i,j). \tag{21}\]  
Energy, also known as the angular second moment (ASM), can be defined as follows:  
\[\text{Energy}\ =\ \sum_{i=0}^{N-1}\sum_{j=0}^{N-1}g^{2}(i,j). \tag{22}\]  
It characterizes the uniformity of the gray distribution of the image texture, and can reflect its roughness. When image texture is coarser, i.e., when the frequency distribution in a TFI is more concentrated, the energy is greater and vice versa.  
In this study, four GLCMs with \(\theta=\{0^{\circ},45^{\circ},90^{\circ},135^{\circ}\}\) were employed. We extracted three types of statistical features from the four GLCMs as texture features for characterizing the TFIs of the vibration signals. The 12 features were expressed as T1, T2, \(\cdots\), T12, where T1, T2, T3, and T4 represent  
Figure 5: Calculation schematic of gray level co-occurrence matrix (GLCM).  
entropy features; T5, T6, T7, and T8 represent contrast features; and T9, T10, T11, and T12 represent energy features of the four GLCMs.
## 3 Results and Discussion
### Experiments in Flight Conditions
#### 3.2.4 Flights and Auto-Tuning in UAV Mass Change Conditions
Auto-tuning in UAV mass change conditions will be the subject of a separate article, while it is worth stressing that the second problem encountered--mentioned at the beginning of the article--i.e., lack of stability criterion based on which it would be possible to estimate safe gains ranges of \(k_{P}\) and \(k_{D}\) for their exploration in the GLD method. Despite its high efficiency and safe operation in tuning of controllers of UAVs with nominal mass or with low extra mass, in case of large payloads (see Figure 22 for the case of 282 g) one can find examples of unstable flights. Then it is strongly recommended to use preliminary simulation tests based on the model. The introduction of the stability criterion into the proposed GLD method is in the area of further research interest of the author [50]. Figure 17: Snapshots from one of the initial research experiments with the auto-tuning of the altitude controller during the flight in the presence of wind gusts and with the mass attached to the UAV on a flexible joint. Figure 19: Step responses for the Bebop 2 UAV tuned with GLD method—variants: with and without addition mass (225 g tool accessories). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Exp.1** & **Exp.2** & **Exp.3** \\ \hline \(\alpha\) & 1.0 & 0.9 & 0.8 \\ \(\beta\) & 0.0 & 0.1 & 0.2 \\ \(k_{Dinit}\) & 10.0 & 10.0 & 10.0 \\ \(k_{p}\) range & [0.5,5.0] & [0.5,5.0] & [0.5,5.0] \\ \(k_{D}\) range & [1.0,20.0] & [1.0,20.0] & [1.0,20.0] \\ Best \(k_{p}\) and \(k_{D}\) values & 3.92/7.85 & 4.02/9.57 & 3.20/11.68 \\ \(J_{1}\) (after the 1 st bootstrap) & 2.5070 & 2.4992 & 2.6096 \\ \(J_{end}\) (after the tuning proc.) & 1.7583 & 2.5779 & 2.5255 \\ \(J_{avg}\) (average for tuning proc.) & 2.8091 & 2.4566 & 2.6553 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of real-world experiments—flying with the payload (tool accessories); for 2 bootstrap cycles, 56 iterations of the GLD method with low-pass filtration and \(\epsilon\) = 0.05. Figure 20: Time course from the tuning experiment via GLD method—variant: tuning of the altitude controller during the UAV flight with an additional (heavy) mass (225 g); the experiment interrupted due to the loss of stability. Figure 18: The Bebop 2 with additional payload used for in-flight auto-tuning experiments.
## 3 Results
### Prediction of Peak Amplitudes of the Shock Wave  
Predicted peak amplitudes of the shock wave were formulated. In order to predict the peak amplitudes of the shock wave for composite transducers other than those with 2 cm, 5 cm and 8 cm diameters at higher laser energy values over 500 mJ/pulse, a predictive sound pressure equation was calculated. The predicted peak positive and negative amplitudes for composite transducers between 2 cm and 8 cm were calculated by linear fitting the measured values of laser energies at 600 mJ/pulse, 700 mJ/pulse, and 800 mJ/pulse, as shown in Equations (1) and (2). The calculated root mean square (RMS) errors of Equations (1) and (2) are 1.7 MPa and 1.2 MPa, respectively:  
\[\text{Predict P}_{+}[\text{MPa}]=(0.0077\text{5E}-3.13)\text{D}+28 \tag{1}\]  
Figure 4: (**a**) Experimental setup for measuring the focal point of the shock wave. The focal point was at 0 mm. (**b**) The peak positive and negative pressures were observed at 0 mm with reference to the focal point along the axial direction. (**c**) The peak pressures were observed at 0 mm with reference to the focal point along the y-direction. (**d**) The peak pressures along the z- direction were also observed at 0 mm with reference to the focal point.  
\[\text{Predict}\;\text{P}_{-}[\text{MPa}]=(-0.00425\text{E}-0.468)\text{D}-5.35 \tag{2}\]  
E = laser energy in mJ/pulse, D = diameter of transducer in cm.  
The predicted and measured peak amplitudes of the shock waves are shown in Figure 5. The broken lines and the marks correspond to the predicted and measured positive and negative pressures of the shock waves, respectively. They show an agreement between the predicted and experimental values. For the 5 cm and 8 cm diameters, the measured values show a slight difference from the predicted values. For the 5 cm diameter, the measured positive pressures are a little lower than the predicted values, while the measured negative pressures are higher than the predicted values.
_Applied_  
_sciences_  
Experimental Study on Aerodynamic Characteristics of a Gurney Flap on a Wind Turbine Airfoil under High Turbulent Flow Condition  
**[PERSON] \({}^{1,2,3}\)\({}^{\text{(\ref{J1})}}\), [PERSON] \({}^{1,2,\bullet}\), [PERSON] \({}^{1,2,\text{(\ref{J2})}}\), [PERSON] \({}^{1,2}\) and [PERSON] \({}^{4}\)**  
1 College of Electrical, Energy and Power Engineering, Yangzhou University, Yangzhou 225127, China; [EMAIL_ADDRESS] (J.Y.); [EMAIL_ADDRESS] (W.Z.); [EMAIL_ADDRESS] (N.L.)
2 New Energy Research Center, Yangzhou University, Yangzhou 225009, China
3 College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225009, China
4 Jiangsu Key Laboratory of Hi-Tech Research for Wind Turbine Design, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; [EMAIL_ADDRESS]
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-138-1583-8009  
Received: 20 September 2020; Accepted: 14 October 2020; Published: 16 October 2020  
**Abstract:** The objective of the current work is to experimentally investigate the effect of turbulent flow on an airfoil with a Gurney flap. The wind tunnel experiments were performed for the DTU-LN221 airfoil under different turbulence level (T.I. of 0.2%, 10.5% and 19.0%) and various flap configurations. The height of the Gurney flaps varies from 1% to 2% of the chord length; the thickness of the Gurney flaps varies from 0.25% to 0.75% of the chord length. The Gurney flap was vertical fixed on the pressure side of the airfoil at nearly 100% measured from the leading edge. By replacing the turbulence grille in the wind tunnel, measured data indicated a stall delay phenomenon while increasing the inflow turbulence level. By further changing the height and the thickness of the Gurney flap, it was found that the height of the Gurney flap is a very important parameter whereas the thickness parameter has little influence. Besides, velocity in the near wake zone was measured by hot-wire anemometry, showing the mechanisms of lift enhancement. The results demonstrate that under low turbulent inflow condition, the maximum lift coefficient of the airfoil with flaps increased by 8.47% to 13.50% (i.e., thickness of 0.75%), and the Gurney flap became less effective after stall angle. The Gurney flap with different heights increased the lift-to-drag ratio from 2.74% to 14.35% under 10.5% of turbulence intensity (i.e., thickness of 0.75%). However, under much a larger turbulence environment (19.0%), the benefit to the aerodynamic performance was negligible.  
**Keywords:** wind tunnel experiment; wind turbine airfoil; turbulence; Gurney flap; aerodynamic characteristics
## 4 Experiments and Results
### Effects of Different MTL Strategies for Combination of LID and ASR  
We conducted experiments using a different choice of MTL weight \(\beta\), and we chose the best weight \(\beta=0.1\) in the following experiments. As shown in Table 3, LLS and ALS-share achieve an equivalent effect, both better than the CTC-Transformer baseline, consistent with our expectations. However, the effect of ALS-indep is worse, therefore we did not use ALS-indep for subsequent experiments.  
To add joint CTC training for LID, the LID CTC weight is set to 0.3, the same as the ASR CTC weight (\(\alpha=0.3\)). As shown in Table 3, the LID auxiliary task with joint CTC training can better assist the ASR main task, because CTC can learn to align the speech feature and the LID label sequence explicitly.