esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuzzy Echo State Network-Based Fault Diagnosis of Remote-Controlled Robotic Arms
###### Abstract  
This paper presents a novel fault diagnosis technique for remote-controlled robotic arm systems, utilizing deep fuzzy echo state networks (DFESNs) and applies the covariance matrix adaptation evolution strategy (CMA-ES) to optimize the hyperparameters of the DFESN model. The developed DFESN model, optimized via CMA-ES, efficiently performs online fault classification through small datasets and training. The method is evaluated through experiments on a leader-follower robotic arm system, demonstrating high accuracy and efficiency. The faults under consideration include leader sensor fault, communication fault, actuator fault, and follower sensor fault. Only follower sensor data are utilized for fault diagnosis. The DFESN model achieves a mean accuracy of 99.5% with the shortest training and online diagnosis times compared to other methods, making it suitable for real-time fault diagnosis applications.  
Keywords:fault diagnosis; deep fuzzy echo state networks; remote-controlled robotic arms; CMA-ES +
Footnote †: journal: Applied sciences
## 2 Establishment of Measurement Control Network Model  
### Description of Measurement Control Network Grading  
Considering the vast distribution and numerous target points along long-distance tracks, using a single measurement device is insufficient to measure all target points and ensure the accuracy requirements. The total station offers a large measurement range, but it tends to have relatively large measurement errors at shorter distances, whereas the laser tracker provides high precision but has a limited maximum measuring range. Segmental measurement approaches using the laser tracker could lead to significant cumulative errors, failing to meet the full-range measurement requirements. Therefore, laser tracker and total station are used to establish a measurement control network, adopting a multi-level measurement and step-by-step control strategy to achieve high-precision coordinate measurement of target points on long-distance tracks.  
As shown in Figure 1, the measurement control network is divided into several levels: Level I corresponds to the distance between each of the two adjacent target points; Level II and higher levels are defined based on the characteristics of the measuring device. This multi-level measurement is defined as a network grading model. For example, with a total distance of 500 m, a laser tracker could be chosen as the measuring instrument within Level II and the total station as the measuring instrument within Level III. The key to ensuring measurement precision lies in how the data from Level II and Level III are integrated and transformed into the global coordinate system.
## 5 Conclusions  
This paper presents an improved modulation strategy for a compact single-phase six-switch AC-AC converter which effectively reduces the number of switches. The proposed modulation strategy allows the six-switch converter to achieve 'fast charging and discharging' in a complete switching cycle. The energy on the DC-link capacitor obtained through the rectifier can be quickly output to the load through the inverter to achieve minimum energy storage. Therefore, under the same DC-link voltage ripple constraint, the capacitance value can be reduced and the system power density can be improved. Based on the time average modeling analysis, the effectiveness of the proposed strategy is fully proved in theory. Finally, the experimental results show that ripple factor drops to 2.20% from 3.49% while THD reduces from 1.38% to 0.92% under improved modulation strategy. The improved converter requires only one-third of the capacitance required by the traditional modulation strategy when the ripple factor is limited to 2%.  
Under RL load, there may be different factors for inversion and rectification, the input power and output power will become synchronous and bring about twice frequency power in the DC-link capacitor. Next work will be focused on.  
Conceptualization, L.H.; Data curation, Q.T.; Formal analysis, Q.T.; Funding acquisition, L.H.; Investigation, Q.T.; Project administration, L.H.; Resources, L.H.; Software, Q.T.;  
Figure 16: The capacitance voltage waveforms when the voltage ripples are about 2%: (**a**) traditional modulation strategy; (**b**) improved modulation strategy.  
Figure 15: Capacitor voltage waveforms in line frequency cycle when \(\varphi=0^{\circ}\): (**a**) traditional modulation strategy; (**b**) improved modulation strategy.  
Validation, L.H.; Visualization, L.H.; Writing--original draft, Q.T.; Writing--review & editing, L.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Guangdong Province Natural Science Foundation under Grant 2021A1515011710, in part by the Natural Science Foundation of China under Grant 62071406 and in part by the Xiamen University Nanqiang Young Top Talents Program.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 5 Engineering Application
### Determination of Material Parameters  
On the basis of the relevant literature [5; 44], the elastic modulus \(E\) (MPa) and elastic resistance coefficient \(K\) (MPa/m) are calculated according to Formula (6). The elastic modulus E is obtained from the test:  
\[K=\frac{E}{(1+\
u)r}. \tag{6}\]  
where '\(\
u\)' is the Poisson's ratio of material, taking value as 0.2; '\(r\)' is the radius of the tunnel in meters.  
From Formula (6), the elastic resistance coefficient is related to the elastic modulus, Poisson's ratio, and the tunnel radius. The tunnel radius remains the same in this paper. Thus, the comparison of the material parameters is shown in Table 4.  
The elastic modulus of ordinary concrete and rubber concrete with 0.85 mm particle size obtained in the test was used to carry out finite element modeling, respectively, and the calculated vault settlement, arch bottom settlement, lining maximum principal stress, and maximum horizontal displacement on the left and right sides of the tunnel were analyzed. The numerical simulation results take the lining vault settlement as an example (Figure 12), and the calculation results are shown in Table 5. It can be seen that, compared with plain concrete, RC-1 has a greater elastic modulus and elastic resistance coefficient after cyclic loading, and its resistance to deformation is stronger.  
The simulation results in Table 5 show that, when OC backfill is used, the crown settlement is 29.1 mm, the bottom settlement is \(-\)26.4 mm, the maximum horizontal displacement on the left and right sides is 8.58 mm and \(-\)8.39 mm, and the maximum principal stress is 0.96 MPa. When RC\(-\)1 backfill is used, the deformation values are 28.7 mm, \(-\)27.0 mm, 7.61 mm, \(-\)7.40 mm, and 1.07 MPa, respectively. Then, the simulation results are analyzed: with RC-1 backfill, the vault settlement decreases by 0.4 mm, and the arch bottom uplift raises by 0.6 mm. The left and right arch waists are symmetrical, the maximum horizontal displacement of the arch waist of RC-1 is lower than that of OC, and the maximum principal stress increases by 11.5%. This proves that the elastic modulus of the backfill concrete has a significant effect on the deformation and forces in the tunnel envelope. The application of rubber concrete can significantly reduce the displacement and stress generated by the backfill of the tunnel structure, which brings a more stable and secure structure.
## 2 Coal Samples and Experiment Methods
### X-ray Diffraction Analysis  
This test was performed using a D8 ADVANCE X-ray diffractometer from Bruker, Mannheim, Germany, to scan the samples [37]. Coal samples with particle sizes of up to 0.074 mm were used for this test. The experimental conditions were an X-ray tube with a Cu target and K\(\alpha\) radiation (U = 40 kV, I = 30 mA, \(\lambda\) = 0.15405 nm). After the test, the diffraction results obtained from the experiment were processed using Origin 2022b software, and the parameters of the \(\gamma\) and 002 peaks were obtained by splitting the peak fit at 20\({}^{\circ}\) and 26\({}^{\circ}\), respectively [38]. Based on this, the aromaticity (\(f_{a}\)) of the coal sample can be calculated [39].  
\[f_{a}=C_{ar}/\left(C_{ar}+C_{al}\right)=A_{002}/\left(A_{002}+A_{\gamma}\right) \tag{3}\]  
\(A_{002}\) represents the area of the 002 peak and \(A_{\gamma}\) represents the area of the \(\gamma\) peak.  
In addition, interlayer spacing (\(d_{002}\)), the stacking layer number (\(N_{ave}\)), and the stacking heights (\(L_{c}\)) can be calculated using Equations (4)-(6) [18; 40].  
\[d_{002}=\lambda/\left(2\sin\theta_{002}\right) \tag{4}\]  
\[N_{ave}=L_{c}/d_{002} \tag{5}\]  
\[L_{c}=0.89\lambda/\left(\beta_{002}\cos\theta_{002}\right) \tag{6}\]  
In these equations, \(\lambda\) is the wavelength of the X-ray (\(\gamma\) = 0.15405 nm), \(\theta_{002}\) is the diffraction angle of the 002 peak, and \(\beta_{002}\) is the full width at half maximum (FWHM) values of the 002 peaks.
# Wireless Positioning in IoT: A Look at Current and Future Trends
## 3 Designing an IoT Positioning System
### IoT System Parameters
In addition, spread spectrum (SS) or Orthogonal Frequency Division Multiplexing (OFDM) modulations are also widely encountered. The modulation type plays a big role in the achievable positioning accuracy when TOA, TDOA, or AOA methods are used, but it has little or no impact when RSS methods are used. Certain modulation-based characteristics can be exploited for positioning purposes. For example, this is the case of SS signals (e.g., LoRa, ZigBee, etc. ), where the spreading pseudo-random sequence can be used to infer the signal's travel time in a similar fashion to GNSS;  
**Positioning signaling or data exchange** is the ability to use either pilot signals or sequences of data packets to provide the location of nearby devices. However, few of the existing IoT technologies support positioning-related signaling, except for most of the cellular IoT technologies (e.g., NB-IoT), which rely on the observed time difference of arrival (OTDOA), introduced in the LTE radio. Apart from the cellular IoT technologies, the future WiFi 802.11 az standards also showcase a dedicated data exchange regarding the time-of-flight information to determine the location of its devices;  
**Roaming** is the ability to provide continuity of service across multiple networks, owned or not by a single entity. As mobility is a keystone of most positioning applications, it is important to take note of this when looking at IoT systems. In this aspect, protocols such as Sigfox or Ingenu are at an advantage, as they operate similarly to cellular systems and they offer service across multi continents. Despite that, even proprietary solutions start to provide open application interface specifications and open guest periods in the radio access, which facilitate the exchange of data across multiple vendors and technologies;  
**Network ownership** raises security and privacy concerns. Security is becoming a strong requirement in IoT systems, especially as the data access, transport and storage become more and more regulated by international and European bodies [13]. Technologies such as Ingenu and Sigfox own the entirety of the network, meaning that the transportation of data is under their full responsibility. Thus, positioning solutions enabled by such systems will be protected by the system provider, as the infrastructure device's location will not be known to the user;  
**Power consumption** is a main topic for all IoT technologies. For positioning applications, low-power consumption is crucial for the viability of several systems, especially when the goal is to continuously track and monitor inexpensive items. For example, low-power consumption is mandatory in several use cases from the logistics and construction sectors.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 2 Image Encryption Algorithms
### Chaotic Systems
#### 2.2.2 Baker Map  
The Baker map is a chaotic transformation that maps a unit square into itself. It involves a sequence of stretching and folding operations that result in a new transformed unit square. The formula for the Baker map is as follows:  
\[B(x_{n+1},y_{n+1})=\begin{cases}(2x_{n},y_{n}/2)&0\leq x<\frac{1}{2},\\ (2-2x_{n},1-y_{n}/2)&\frac{1}{2}\leq x<1\end{cases}, \tag{2}\]  
where \((x_{n},y_{n})\) and \((x_{n+1},y_{n+1})\) are the coordinates of a point in the unit square before and after the transformation, respectively.  
[PERSON] et al. [11] proposed a design of a 2D chaotic Baker map that operates on three different modes: cipher block chaining (CBC) mode, cipher feedback (CFB) mode, and  
Figure 2: Logistic map bifurcation diagram.  
output feedback (OFB) mode. In the proposed algorithm, the Baker map performs the permutation procedure on the plaintext image by moving the positions of the pixels. The encryption key uses a random IV, which, depending on the operation mode, is XORed with the plaintext data blocks, where the IV has the same size as the plaintext block. The proposed implementation combines the speed of chaotic maps with the randomness of the mode of operation, resulting in a fast and secure cryptosystem.  
In [12], [PERSON] et al. presented an image encryption algorithm that uses a 2D Baker map, where the plaintext image is initially permuted using a pseudorandom sequence generated by the Baker map, followed by a diffusion process based on an XOR operation between the scrambled plaintext image and the generated sequence. The proposed encryption algorithm employs dual secret keys generated by the Baker map; one is used for permutation and the other for diffusion.  
The Baker map is known to be vulnerable to chosen-plaintext attacks, where an attacker with a pair of plaintexts and corresponding ciphertexts can reverse the permutation and diffusion processes to deduce information about the secret key. This vulnerability could be mitigated by combining other layers of security, such as using an IV or multiple chaotic maps.
## 4 Conclusions  
The main finding of this work was confirmation that it is possible to obtain 3D parametric designs of a handle for laparoscopic graspers that are scalable to fit each surgeon's hand size. Furthermore, it was found that a single anthropometric factor--the \"Palm Length Measured\"--can be used to relate any surgeon's hand size to the optimal diameter of the handle. Functional tests carried out in the CCMIJU confirmed that the parametric design developed for the handles adapts ergonomically to different surgeon's hand sizes.  
Thus, the specific contributions of this work include the following:  
* A 3D parametric CAD design has been obtained for the handle of laparoscopic surgery graspers that is scalable for different hand sizes. All of the resulting models personally customized for each surgeon complied with the ergonomic criteria for their use.  
Figure 6: Prototype custom handle. (**a**) Details of the pieces; (**b**) assembled prototype.  
Figure 7: Prototype custom handle used in a simulation test.  
* The results show that additive manufacturing and the application of ergonomics criterion provide an efficient method for the customized design and manufacture of this type of specialized tools, being possible to be applicable to other sectors.
* The relationship between the anthropometry of the surgeon's hand and the most suitable size of the handle was determined, with the most suitable anthropometric parameter to measure being the \"Palm Length Measured\" (PLM).  
Finally, the authors consider that the customization of the geometry of a surgical tool can serve as a reference for future research that addresses the optimal material and tools for their subsequent production through additive manufacturing techniques.  
Conceptualization, A.G.G. and D.R.S.; Formal analysis, D.R.S. and A.S.R.; Investigation, A.G.G., D.R.S. and L.G.M.; Methodology, A.G.G.; Software, L.G.M.; Validation, A.G.G., D.R.S., L.G.M. and A.S.R.; Writing-review & editing, A.G.G. and D.R.S.  
This research received no external funding.  
The authors wish to acknowledge the support of this research work to the VI Regional Research Plan and the Regional Government of Extremadura and to the Fondo Social Europeo (FEDER) linked to the financial of the research groups Manufacturing Engineering (GR-18029) and INNOVA RNM020--Design, Sustainability and Added Value (GR-18015). The authors are grateful for the outstanding willingness to cooperate of the 135 surgeons who participated in the study, and for their interest in following through with the appropriate trials so as to obtain a satisfactory outcome. In particular, they all considered it of great importance to adapt the sizes of the handles of the tools they use to the size of their hands.  
The authors declare no conflicts of interest.
## 1 Introduction
From this perspective, we think that erosion hazard assessment may help both riparian countries, since there is a high likelihood that their citizens and leaders will come to a consensus on the need for collaboration, e.g., [28]. Initiatives and sustainable development programs must be carried out on a realistic geographical scale that enables the full expression of hydrologic interrelations between all the components of a landscape continuum. In this case, the appropriate scale is the full Shatt Al-Arab river basin. Soil erosion hazard assessment has been used by scientists since the 1930s [29; 30] to forecast and detect control erosion techniques. Various methodologies have been used to estimate soil loss by water at various levels including global, regional, and local [28; 29; 30; 31; 32; 33; 34]. Several models have been created to assess soil loss rates [12; 35]. These models are divided into three types: physical-process-based models, empirical models, and semi-empirical models [36]. Field-based models for quantifying soil loss and its spatial variety are expensive and time-consuming, and insufficient sample plots might impair the consistency of the actual spatial degree of the areas experiencing erosion. In the outdated field-based methods, carefully mapping and monitoring the spatial distribution of soil loss in extremely wide zones is an arduous, expensive, and time-consuming task [37]. On the other hand, each regional scale erosion model such as USLE/RUSLE, SEMMED, WEPP, ANSWERS, EUROSEM, LISEM, SWAT, AGNPS, and SWRRB has its unique features and range of application [38; 39; 40; 41; 42; 43; 44; 45]. The Universal Soil Loss Equation (USLE) was established by the Department of Agriculture in the United States in 1978 to forecast the amount of eroded soil over time [46]. It is an empirical technique that evaluates the long-term means of fill and sheet erosion based on plot data collected in the eastern United States [47]. The authors of [48] provided a newer version of USLE called the Revised Universal Soil Loss Equation (RUSLE). Despite being an empirical method, the combination of RS, GIS, and RUSLE produces soil erosion estimates and its geographic distribution at acceptable costs and with greater precision across larger regions [49; 50; 51; 52; 53; 54]. The authors of [40; 53; 55] observed that the empirical equation of RUSLE remains the most suitable method for assessing soil erosion risk owing to its low data requirements and easy model structure. This is particularly true for most developing nations, where the application of more complicated models may be constrained by a lack of acceptable input data [36]. Therefore, this study aimed to assess soil loss in the Shatt Al-Arab basin using RS, GIS, and the RUSLE model. This may be used by decision-makers for optimal soil restoration planning and sustainable land management practices.
## 5 Data Analysis and Result
### Data Analysis  
To measure the stability of the measurements and model fit, we assessed the fit of multiple linear regression using AMOSv26.0. Table 4 summarizes the empirical results obtained in the analysis and shows the stability of the performed assessment. The goodness-of-fit values indicate an adequate fit (CMIN/DF = 1.536, IFI = 0.950, TLI = 0.944, CFI = 0.950, RMSEA = 0.046). Thus, the combined results of the analyses indicate that the model is a good fit.
# Spectrum Demand Forecasting for IoT Services
## 4 Spectrum Forecasting Model
### Definition of Scenarios  
The total spectrum calculation will be the sum of independent and parallel calculations for each _scenario_. A scenario \(\mathcal{X}_{i}\) is defined as a group of services and technologies, that use the same band in the same location (coverage area), and therefore contend for or share the spectrum, either in a licensed or an unlicensed case.
# The Antimicrobial Effectiveness and Cytotoxicity of the Antibiotic-Loaded Chitosan: ECM Scaffolds
## 1 Introduction
While both chitosan and ECM scaffolds possess antimicrobial activity, the exact mechanisms of the antimicrobial actions are still uncertain. Therefore, to maximize the antimicrobial effects combination of these scaffold materials with an antibiotic would be desirable. For example, it has been proposed that interaction between acidic, or positively charged chitosan molecules and negatively charged microbial cell membranes leads to the disruption of microbial membrane, and subsequently the leakage of the intracellular components [25; 26; 27]. The overall goal of this project was to develop a hybrid wound dressing composed of chitosan and ECM proteins along with an antibiotic that would be an effective hemostatic and antimicrobial dressing. For the present study the objectives were to perform in vitro testing of (1) the effectiveness and duration of antimicrobial activity; (2) the direct and indirect cytotoxicity of the antibiotic containing scaffolds in order to identify formulations for future in vivo testing.