esa-sceva/satcom-chunk-collection huggingface dataset:
# eHealth Integrity Model Based on Permissioned Blockchain +
## 2 Blockchain Based eHealth Integrity Model
### Security Assumptions  
The BEIM was built using a blockchain as the basic component to ensure record integrity. A blockchain is a distributed ledger where multiple participants share the same copy of the ledger. The distributed ledger is protected against tampering because it is not required that all participants be trusted. Furthermore, a consensus protocol must be in place that allows maintaining ledger consistency. Recently, [PERSON] and [PERSON] [21] listed and described integrity and confidentiality requirements. The integrity requirements are (for full definitions, see Reference [21]):  
1. Agreement on Transaction Validity: only a valid transaction can be recorded in the ledger;
2. Tamper Evidence: the ledger cannot be tampered with and is consistent among participants;
3. Finality: no transaction is rejected after it is approved and recorded in the ledger.  
The blockchain used in the BEIM must meet the above three requirements. Additionally, it must be possible to implement the following privacy (confidentiality) requirements (for full definitions, see Reference [21]):  
1. Anonymity to Third Parties: no third party can see the true identity of the subjects, and
2. Confidentiality of Transaction Content: third-party subjects or nodes cannot view transaction content.  
However, in the BEIM, anonymity only concerns patients. The nodes or systems that create and add transactions to the blockchain do not have to be anonymous. Moreover, it is assumed that the used hash functions must be secure cryptographic hash functions, and a public key (asymmetric) encryption algorithm must be considered as secure.
## 5 Additively Manufactured Flexible Sensors (3D Printing)
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Printing Method** & **Type of Sensor** & \begin{tabular}{c} **Stability/Minimum** \\ **Bending Cycles** \\ \end{tabular} & \begin{tabular}{c} **Sensitivity (Smallest** \\ **Detectable Quantity)** \\ \end{tabular} \\ \hline Coaxial extrusion. [73] & Inductive sensor & \begin{tabular}{c} 500 bending/stretching \\ cycles \\ \end{tabular} & 0.001–0.25 \(\upmu\)H/mm \\ \hline FDM. [74] & \begin{tabular}{c} Inductor–capacitor-resonant tank \\ circuitry \\ for monitoring the quality of \\ liquid food. \\ \end{tabular} & n/a & 4.3\% resonance frequency shift \\ \hline FDM. [75] & Capacitive and piezoresistive & n/a & n/a \\ \hline FDM. [77] & Multiaxial force sensor & 1 000 bending cycles & \textasci{}2.11 N/mm \\ \hline FDM. [80] & Force sensor & 38 MPa Young’s modulus & n/a \\ \hline FDM. [82] & Environmental monitoring & 30 \({}^{\circ}\)C Tg & 76 mW/cm\({}^{2}\) \\ \hline FDM. [83] & \begin{tabular}{c} Wearable (programmable heater, \\ temperature sensor and circuitry) \\ \end{tabular} & 0–80 \({}^{\circ}\)C & n/a \\ \hline FDM. [85] & Tactile sensors & 5 Pa–100 Kpa & n/a \\ \hline FDM. [84] & Wearable (temperature sensor) & & \textasci{}0.225 k\(\Omega/^{\circ}\)C \\ \hline DIW. [78] & Strain sensors & 1–30\% stain & \\ \hline DIW. [79] & Strain sensor & Strain at break of 180\% & \\ \hline Inkjet printing. [81] & Supercapacistors & 3 000 bending cycles & \begin{tabular}{c} 300\(\Omega\)/sq sheet resistance, \\ power density 96 mW/cm\({}^{3}\) \\ \end{tabular} \\ \hline Inkjet printing. [41] & Temperature sensor & 20–60 \({}^{\circ}\)C & \begin{tabular}{c} 2.23 \(\times\) 10\(-\)3/\({}^{\circ}\)C \\ \end{tabular} \\ \hline Stereolithography. [59] & Temperature sensor & \textasci{}27\textasci{}\textasci{}39 \({}^{\circ}\)C & \
\begin{tabular}{c} \textasci{}>98\% strain fixity rate, \textasci{}>93\% strain recovery rate \\ \end{tabular} \\ \hline Photopolymerisation. [88] & Piezoresistive sensor &
\begin{tabular}{c} 5.5 MPa Young’s modulus, \\ elongation at break of 18.3\% \\ \end{tabular} & n/a \\ \hline SLM. [89] & pH sensing & n/a & n/a \\ \hline Directprint/cure (DPC) and & & & \\ projection-based & Piezoresistive tactile sensor & n/a & n/a \\ stereolithography. [87] & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Examples of additively manufactured (3D printed) sensors and some key characteristics studied in literature. \begin{table}
\begin{tabular}{c c c c} \hline \hline
\begin{tabular}{c} • Cheap materials \\ Wide ranges of printers from cheap to expensive \\ depending on needs \\ • Easy material customisation (e.g., adding \\ nanomaterial into polymer matrix) \\ • Print speed can be varied depending on required \\ • • Easy material customisation \\ •
# High Speed Decoding for High-Rate and Short-Length Reed-Muller Code Using Auto-Decoder
## 2 RM Decoder Based on AD
### Performance Evaluation  
To evaluate the performance of the proposed decoder using AD, we consider two cases of RM code, \(\mathfrak{R}(1,4)\) and \(\mathfrak{R}(2,4)\) whose code structure is simple, with a short message length. Figure 5 shows the BER of RM coding with the AD compared with FHT decoding. The BER at each SNR was calculated when the maximum number of error bits was 500, or the number of codewords generated reached \(10^{5}\). From the graph, we can see that the two methods show almost the same BER performance. Table 5 shows the decoding times for \(\mathfrak{R}(1,4)\) and \(\mathfrak{R}(2,4)\) using FHT decoding and the AD. A computer with a central processing unit (CPU) of Intel i9-7920, graphics processing unit (GPU) of Nvidia Titan XP, and 64 GB random access memory (RAM) was used for the evaluation. If a GPU is used for decoding, the proposed method can have an advantage over the method using FHT, and for a fair comparison, we measure the decoding time using a CPU without a GPU. The decoding time of the proposed method was 1.8 times faster than the decoding time of FHT decoding for \(\mathfrak{R}(1,4)\) and 125 times faster for \(\mathfrak{R}(2,4)\). This derives from the fact that (16,11) \(\mathfrak{R}(2,4)\) decoding using FHT needs \(2^{6}\) FHTs, considering six masking bits, whereas (16,5) \(\mathfrak{R}(1,4)\) decoding needs only one FHT operation [15]. The main difference between the RM decoding using the AD for \(\mathfrak{R}(1,4)\) and \(\mathfrak{R}(2,4)\) is the number of nodes in the FC layer, which increases the number of parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline Training SNR & \multirow{2}{*}{0} & \multirow{2}{*}{1} & \multirow{2}{*}{2} & \multirow{2}{*}{3} \\ (\(\rho_{t}\)) & & & & \\ \hline NVE & 0.972 & 0.945 & 0.982 & 1.138 \\ \hline Training SNR & \multirow{2}{*}{4} & \multirow{2}{*}{5} & \multirow{2}{*}{6} & \multirow{2}{*}{7} \\ (\(\rho_{t}\)) & & & & \\ \hline NVE & 0.981 & 1.320 & 1.529 & 2.489 \\ \hline \hline \end{tabular}
\end{table}
Table 4: NVE for different training SNRs.  
\begin{table}
\begin{tabular}{c c} \hline \hline loss function & cross-entropy \\ optimizer & Adam \\ training data set & \(2^{k}\times 10^{5}\) \\ epoch & \(10^{2}\) \\ batch size & \(10^{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameters for model training.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**RM Code** & **Method** & **Time (ms)** \\ \hline \(\mathfrak{R}(1,4)\) & FHT & 0.6012 \\  & AD & 0.3327 \\ \hline \(\mathfrak{R}(2,4)\) & FHT & 46.625 \\  & AD & 0.3704 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Decoding time of RM code using FHT and AD.
# A New Method for Rapid Optimization Design of a Subsonic Tandem Blade
## 5 Conclusions  
Based on the analysis of the aerodynamic interaction of tandem blades, a new optimization method for a subsonic tandem blade is put forward; the effectiveness and feasibility of the method are validated using a 2D tandem blade model and a double-stage compressor with tandem blades. The main conclusions are as follows:  
1. Under subsonic (shock-free) conditions, the traces of aerodynamic interaction between FB and AB can be reflected in blade surface isentropic Mach number distributions. Compared to the single-blade configuration, the FB trailing edge local aerodynamic load and the AB incidence increased under the FB and AB relative position investigated in this paper (AO \(\sim-0.1\), PP \(\sim 0.7\)). Furthermore, this phenomenon will lead to additional total pressure loss in the tandem configuration.
2. To fully utilize the aerodynamic interaction in tandem blades, the optimization principles and the \"camber line modification method\" are presented in this paper: in a tandem configuration, the FB and AB surface isentropic Mach number distributions (i.e., the local aerodynamic load) can be revised to rapidly and conveniently approach the CD airfoil by using this method.
3. To validate the effectiveness and feasibility of this method, two tandem blade optimization examples were performed and simulated: * Optimization for a 2D tandem blade model: with an inflow Mach number of 0.8, the minimum loss decreases by 6%, and the incidence range extends approximately 2\({}^{\circ}\) compared to the baseline; * Optimization for a double-stage transonic compressor with tandem blades: at 80% design speed, isentropic efficiency increases by 2 points, with an increase of mass flow by 1.6% at the choke point simultaneously. In addition, compressor characteristics at design speed remain almost unchanged.  
Conceptualization, B.L. and X.Y.; methodology, Y.T.; software, Y.T.; validation, Y.T.; formal analysis, Y.T.; investigation, Y.T.; data curation, Y.T.; writing--original draft preparation, Y.T.; writing--review and editing, Y.T.; project administration, Y.T.; funding acquisition, Y.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the \"Fundamental Research Funds for the Central Universities\", grant number 3122018C009.  
The authors would like to acknowledge [PERSON]'s help in improving the grammar of the manuscript.  
Figure 27: Mach contours near the blade surface at 100% design speed at the DE point. (**a**) Original; (b) modified.
## 3 Obstacle Avoidance Scenario
### Obstacle Avoidance Techniques
#### 3.4.4 Fuzzy Logic Algorithm  
Fuzzy logic was proposed by [PERSON] [304], which uses the fuzzy controller. To use fuzzy logic in any system, the operator needs to assign a set of data or knowledge to create fuzzy sets that will be used to avoid obstacles or navigate the mobile robot. This process of assigning fuzzy input sets is called fuzzification. The set value usually can be anywhere between two traditional logics, such as (0, 1) or (Low, High) or (Cold, Hot), etc. This is why, usually, those vehicles that use fuzzy logic for navigation and avoidance use one kind of multiple sensors or sensor fusion. [PERSON] [305] used the fuzzy controller to control an obstacle avoidance mobile robot. In Figure 12, the usage procedure is given.  
This classic method is used in many vehicle navigation systems [306]. [PERSON] [307] used fuzzy logic techniques to build a reactive navigation system and avoid obstacles. Several research works created a fuzzy logic controller using fuzzy sets to avoid obstacles in real time. [PERSON] et al. [308] used a fuzzy-based approach to track paths and avoid obstacles. [PERSON] [309] proposed a navigation algorithm using a fuzzy controller and sensor fusion (camera and sonar) with a mobile robot to avoid obstacles and generate trajectories. Using the fuzzy logic system and three-way ultrasonic sensor, [PERSON] and [PERSON] [310] proposed an avoidance algorithm for a mobile robot. [PERSON] et al. [311] designed a fuzzy logic controller to improve the vehicle's movement according to the obstacle's position.
## 3 Results and Discussion
### Metamodel and Multi-Objective Optimization
#### 3.2.4 Pareto Front and Optimal Solution  
Based on NSGA-II optimization, the Pareto front for each auxetic model is shown in Figure 15. Each orange circle in Figure 15 represents a nondominated solution. As expected, the Pareto front illustrates the conflict between the two objectives, positioned at the outer side of the prediction sample marked with light blue circles. As \(\delta_{p}\) increases, SEA also increases, and vice versa.  
Several design points from Figure 15, including the baseline, ideal, balanced, and optimized designs, are listed in Table 8. The table shows that the average prediction error from the ML model compared to the simulation is relatively small, with values less than 10%, except for the ideal design with minimum \(\delta_{p}\) and the optimized designs for the REH and CH models. However, when examining the absolute value difference between prediction and simulation, it remains below the MAX value from our trained model, as indicated in Table 7. Therefore, considering the small sample size used in this study, we can conclude that the model's error is within an acceptable range, indicating that the optimization results are reliable.  
Figure 14: Scatter plot showing the influence of input variables for (**a**) REH, (**b**) DAH, (**c**) SH, and (**d**) CH models.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Geometry**} & \multirow{2}{*}{**Type**} & \multirow{2}{*}{\(N_{y}\)} & \multirow{2}{*}{\(N_{z}\)} & \multirow{2}{*}{\(\theta\)/\(r\)} & \multirow{2}{*}{\(t\)} & \multirow{2}{*}{\(\rho_{r}\)} & \multicolumn{4
## 5 Application to Tensile Test Specim
### Discussion  
The diagrams provided in Figure 11 reveal that the numerical obtained stress-strain curves clearly showed the expected elasto-plastic behavior. Despite the fact that the material behavior was modeled based on the assumption of an elastic-ideal plastic material model, the resulting stress-strain curves showed an overall good agreement with the experimental data. This was related to the localized plastic deformation and the rearrangement of the applied loading. In addition, the diagrams disclose, which the numerical results showed, a better agreement with the experimental data for a decreasing correlation length. This is also indicated by the values provided in Table 7. For a decreasing correlation length, the mean stress state converged to the experimentally obtained values; for a correlation length of 5 mm, the results showed an overall good agreement. The deviation between the mean stress states at the different strain levels for the experimental and numerical results was covered by the standard deviation. However, despite the good overall conformity, the results indicated two shortcomings. First, the experimentally obtained stress range at strain levels up to 1 % was not covered by the numerical simulation. This might be due to the reduction of the parameters, which were represented by random fields. Second, even though the composite material did not show explicitly ideal plasticity, the plastic deformation within the numerical simulations started at higher strain levels, as indicated by the experimental results. This might be resolved by reducing the yield strength locally or taking into account hardening effects.  
The modeling approach was further endorsed by the results on the right-hand side by two important aspects. First, the modeling approach led to a plastic deformation covering the whole specimen and, hence, did not show localized plastic deformation. Second, when dividing the measurement length into small sections, each section showed a slightly different behavior. Both phenomena were confirmed by the experimental investigation.  
All in all, it can be stated that the presented modeling approach is a very appropriate method to incorporate the spatial distribution of the elastic and plastic material properties of SFRCs.
# Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring
## 9 Ultrasonic Method
### Introduction and Principle of the Application
#### 9.1.2 Timber Structures  
It is also possible to use UT methods to detect insect damage and delamination as well as cracks in timber structures. Moreover, fungal growth in timber has been assessed by transverse transmission methods of UT. In this method, waves propagating in the direction perpendicular to the grain were used to detect the decayed portions of the wood through wave attenuation, wave velocity as well as analysis of frequency spectrum. Two ultrasonic methods, namely, pulse-echo and through-transmission and thermal camera, were used as NDT methods to assess internal defects of pruned and unpruned eucalyptus wood samples at 120% moisture content. The experiment aimed to determine the influence of defects on the ultrasonic wave propagation and thermal camera. It was observed that, for standing stress, several types of defects in various parts of the tree might not affect the wave velocity but may influence other parameters such as wave amplitude [125].
## 2 Materials and Memristor Fabrication
### Fabrication of NbO\({}_{\text{v}}\)/NbSe\({}_{2}\) Heterostructured Memristor Device  
The as-grown NbSe\({}_{2}\) flake sample was transferred to a target substrate by a typical wet-transfer approach [26]. Firstly, the as-grown NbSe\({}_{2}\) sample was coated with a poly-methyl-methacrylate (PMMA) C4, which formed a support layer. Then, the substrate was etched with a diluted hydrogen fluoride (HF) acid solution to separate the grown sample/PMMA film from the growth substrate. With several rinsing with deionized (DI) water, it was transferred onto a target SiO\({}_{2}\)/Si substrate. Finally, after drying the transferred sample, the PMMA support layer was removed by acetone and rinsed again via isopropyl alcohol (IPA) solution. (Figure 1c)  
The native oxide layer (NbO\({}_{\text{x}}\)) was formed by exposing the grown NbSe\({}_{2}\) sample to the air at room temperature for several days (in our case, 21 days), resulting in NbO\({}_{\text{x}}\)/NbSe\({}_{2}\) heterostructure, where the NbO\({}_{\text{x}}\) layer can act as a memristive surface. To fabricate the memristor device, the metal electrodes (source and drain) for a probe contact were patterned on NbO\({}_{\text{x}}\)/NbSe\({}_{2}\) heterostructures using e-beam lithography followed by e-beam deposition of Cr/Au (5/50 nm). Electrical and memristive feature measurements for NbSe\({}_{2}\) and NbO\({}_{\text{x}}\)/NbSe\({}_{2}\) heterostructures were performed using a probe station and a semiconductor analyzer (Keysight B1500A) at room temperature.  
Figure 1: Synthesis settings and characteristics of chemical vapor deposition (CVD)-grown NbSe\({}_{2}\): (**a**,**b**) The schematic image and photo image of synthesis settings for NbSe\({}_{2}\) Growth. The ammonium niobium oxalate-C\({}_{4}\)H\({}_{4}\)NNbO\({}_{9}\)xH\({}_{2}\)O (ANO) source-coated substrate was loaded in the center of the CVD chamber and the solid Se was located at the upstream region where the distance is 17 cm from the center of the quartz; (**c**) optical microscope image of as-grown NbSe\({}_{2}\); (**d**) Raman mapping image according to A\({}_{\text{1g}}\) mode; (**e**) the out-of-plane A\({}_{\text{1g}}\) mode at 225 cm\({}^{-1}\) and the in-plane E\({}_{\text{2g}}\) mode at 250 cm\({}^{-1}\) were observed.
## 2 Problem Scenario
As a consequence, the experiments developed with the simulator will be reproducible under the same conditions. * In addition, each of the position distances \((d_{1},d_{2},d_{3},d_{4})_{k}^{NT}\) may be individually lost according to a pseudorandom uniform probability distribution with loss probability \(P\). When a position distance \(d_{i}(i=1,...,4)\) is lost, \(\gamma_{i}=0\); otherwise, \(\gamma_{i}=1\). * The system state estimate \((\tilde{V}_{x},\tilde{V}_{y},\tilde{W}_{y},\widehat{X},\widehat{Y},\widehat{ \psi})_{k}^{T}\) is computed via the NUDREKF. The prediction step is generated at period \(T\) from the odometry measurements \((\omega_{1},\omega_{2},\omega_{3},\omega_{4})_{k}^{T}\). The correction step is also obtained at period \(T\), but from data sensed at the two different periods--that is, \((\psi)_{k}^{T}\) and \((d_{1},d_{2},d_{3},d_{4})_{k}^{NT}\). More details about the proposed NUDREKF can be found in Section 3.2.
## 1 Introduction  
The thoracic wall is a musculoskeletal structure that protects vital organs, gives support to the forelimbs, and provides muscular support for ventilation [1]. In cats, it is innervated by eleven pairs of thoracic spinal nerves (T), from the second (T2) to the twelfth (T12) [2]. Once they emerge from the intervertebral space, these nerves promptly divide into the rami dorsales, rami ventrales, and rami communicantes [2]. The rami dorsales are then divided into the rami mediates and rami laterales (RDL), providing innervation to the epaxial region [3]. The rami ventrales continue ventrally as the intercostales nerves, which run between the intercostales interni muscles and the endothoracic fascia. As they run ventrally, they extend a branch to form the rami cutanei laterales (RCL), which provide sensitive innervation to the lateral thoracic wall [4]. Finally, at their ventral end, the intercostales nerves emerge as rami cutanei ventrales to innervate the ventral midline [5].  
The serratus plane block is an ultrasound-guided anaesthesia technique that aims to desensitie the RCL, providing analgesia to the cranio-lateral thoracic wall [6]. It was first described in human medicine by [PERSON] et al. (2013) [7] to provide analgesia for mastectomies. Since then, it has been used to relieve pain in several clinical situations [8], including rib fractures [9; 10], thoracoscopic surgeries [11], and thoracotomies [12]. In veterinary medicine, two approaches have been described in dogs: the superficial and the deep serratus plane (DSP) blocks. In the former, the local anaesthetic is administered between the latissimus dorsi (LD) and the serratus ventralis thoracics (SVT) muscles [13]. Meanwhile, in the DSP block [14; 15], the injection is performed in the interfacial plane between the SVT and the intercostales externi (IE) muscles/lateral aspect of the ribs. Several applications of this anaesthetic technique, including mastectomies [15], thoracoscopies [16], and persistent ductus arteriosus surgical correction [17; 18], have been suggested as part of a multimodal analgesic protocol in dogs. To the authors' knowledge, only a case report for cranial lobectomy and pericardiectomy using the serratus plane block as multimodal analgesia has been published in cats [19].  
This study aims to assess the distribution of a mixture of methylene blue and iopromide injected by the DSP block technique at the 5 th rib. We hypothesise that this technique would be feasible in cats and would stain several RCL cranial to the 8 th rib.