esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### ITC Analysis  
ITC analysis was performed to determine the binding parameters of MAO-A with propenylbenzene derivatives. Studies have shown that for MAO-A, only compounds **1b**, **2b**, **2c**, **3b**, **3c**, **4b**, and **5b** bind to the enzyme in the active site, blocking the deamination of 5-HT. Table 1 shows the results of the thermodynamic parameters obtained from the analysis.  
Figure 2: Example ITC raw data graph. The curve in the bottom figure shows the fitting of data to a one-set model, the nonlinear Levenberg–Marquardt fitting algorithm. The curve in the top figure is the baseline.  
The binding constant (K\({}_{A}\)) of propenylbenzene derivatives ranged from 1.02 to \(53.60\times 10^{3}\) L/mol for **2c** and **3b**, respectively. Compounds **2b**, **3c**, and **4b** showed a similar K\({}_{A}\) of approximately 4 \(\times\) 10\({}^{3}\) L/mol. However, compounds **1b** and **5b** showed a three times higher KA value than **2b**, **3c**, and **4b**. All tested compounds showed a negative enthalpy reaction (\(\Delta\)H), indicating the exothermic effect of the reaction. The \(\Delta\)H range was from \(-40.53\) to \(-2.00\) kJ/mol for **5b** and **2b**, respectively. Compounds **1b** and **3b** showed a slightly higher \(\Delta\)H, approximately \(-38\) kJ/mol, followed by **2c** (\(-27.21\) kJ/mol). The highest negative free energy of \(-34.12\) kJ/mol (\(\Delta\)G) and \(-34.00\) kJ/mol, interpreted as the substances affinity for binding, was recorded for **3b** and **1b**, while the lowest for **2c** was \(-17.88\) kJ/mol. However, **2b**, **3c**, and **4b** showed a similar \(\Delta\)G of approximately \(-21\) kJ/mol. Negative reaction entropy (\(\Delta\)S) was observed for compounds **1a**, **2b**, **2c**, **3b**, and **5b**, and positive for **3c** and **4b**.  
The inhibition constant K\({}_{i}\) of propenylbenzene derivatives indicated that the compounds bound to MAO-A as competitive inhibitors. Compounds **3b** and **5b** had aligned and low K\({}_{i}\), \(<\)0.001 mol/L. The rest of the interactions showed values in the range of 0.65-13.00 mol/L, respectively, for **4b** and **2b** (Table 1). The study showed that **5b** showed high inhibiting activity with IC\({}_{50}\) amounting to 2.38 mol/\(\upmu\)mol of the enzyme. Compounds **1b**, **3b**, and **3c** showed slightly higher IC\({}_{50}\) value, amounting to about 3 mol/\(\upmu\)mol of the enzyme. The study by [PERSON] et al. showed that eugenol is a substance that protects against the cytotoxic effects of amyloid \(\beta\) peptides and has an antidepressant effect. It has been demonstrated that eugenol competitively inhibits MAO-A at K\({}_{i}\) = 26 mol/L; however, when testing the available structures related to eugenol, not every compound showed MAO-A inhibitory effects [31]. Our research reached a similar conclusion about isoeugenol (**5a**) and eugenol itself did not show any inhibitory effect; however, the already synthesized diol **5b** was the best MAO-A inhibitor, also of competitive type (K\({}_{i}\) \(<\) 0.001).
## 2 Complete Mathematical Model of the FLHBR System  
Based on the FLHBR system considered in this study, the FLHBR kinematic model is completely derived via the Lagrange equation that mainly investigates the energy analysis. The schematic diagram of the FLHBR is shown in Figure 1 and the Lagrange equation is written by  
\[L_{a}=E_{kinetic}-E_{potential} \tag{1}\]  
and  
\[\tau_{i}=\frac{d}{dt}\left(\frac{\partial L_{a}}{\partial\dot{\theta}_{i}} \right)-\left(\frac{\partial L_{a}}{\partial\theta_{i}}\right),i=1,2,3,4,5 \tag{2}\]  
where \(E_{kinetic}\) denotes the kinetic energy of the FLHBR system; \(E_{potential}\) is the potential energy of the FLHBR system; \(L_{a}\) denotes the Lagrange function of the FLHBR system; \(\tau_{i},i=1,2,3,4,5\) is the \(i\)-joint torque; \(\dot{\theta}_{i},i=1,2,3,4,5\) denotes the angle of link1-link5; \(\dot{\theta}_{i},i=1,2,3,4,5\) is the velocity of link1-link5, \(M_{1}=M_{5}=4.55\) kg, \(M_{2}=M_{4}=7.63\) kg, \(M_{3}=49.00\) kg are the masses of link1-link5, \(M_{2}\), \(M_{4}\) denote the masses of exoskeleton thighs, \(M_{1}\), \(M_{5}\) denote the masses of legs, \(M_{3}\) denotes the mass of torso, \(L_{1}=L_{5}=0.502\) m, \(L_{2}=L_{4}=0.431\) m are the lengths of link1, 2, 4, 5, \(D_{1}=D_{5}=0.247\) m, \(D_{2}=D_{4}=0.247\) m, \(D_{3}=0.280\) m are the distances between the mass centers of link1, 2, 3, 4, 5 and those lower joint, \(I_{1}=I_{5}=0.105\) kg\(\cdot\)m\({}^{2}\), \(I_{2}=I_{4}=0.089\) kg\(\cdot\)m\({}^{2}\), \(I_{3}=2.350\) kg\(\cdot\)m\({}^{2}\) are the moments of rotational inertias for link1, 2, 3, 4, 5 and \(G=9.8\) m/s\({}^{2}\) is the acceleration of gravity.
## 1 Introduction
[16] successively proposed the processing strategy of two-way spiral milling. In the process of reverse cutting, the material itself was used as support to enhance the constraint rigidity, and a two-way milling cutter was designed to enrich the experimental study of the reverse spiral hole and to verify the feasibility of the cutting process of a bidirectional progressive spiral milling hole. At the same time, based on the basic principle of spiral hole milling, some researchers introduced an ultrasonic generator to output ultrasonic oscillation, and found that the effective cutting time of the tool in vibration cutting is shorter than that of ordinary cutting time [15], and the periodic separation of the tool and the workpiece leads to the periodic change of cutting speed and cutting depth [17]. Regarding changing the cutting mechanism in the cutting process, ultrasonic machining compared with the traditional machining method has a huge advantage. [PERSON] [18] and [PERSON] [19] successively conducted ultrasound-assisted carbon fiber drilling, and they found that the cutting force was significantly reduced and the quality of the parts was greatly improved. [PERSON] [20], [PERSON] [21], and [PERSON] et al. [22] established the theoretical model of cutting force of ultrasonic vibration cutting CFRP and compared it with the experimental results, and they found that the theoretical prediction and the measured results are in good agreement. Under the same cutting parameters, the average axial force can be reduced by 20-30% [20], and the cutting stress is more concentrated, which is beneficial to the shear fracture of the fiber [21] and improves the machining efficiency [22]. [PERSON] [23] further proposed a new process method of ultrasonic vibration sheath-reaming-shaving, and researched and realized high-quality, efficient, and low-cost processing to meet the requirements of precision assembly manufacturing. From what has been discussed above, spiral milling technology still cannot solve the machining defects of carbon fiber holes such as burr and delamination. There are still technical problems to be solved in the process of carbon fiber holes, such as a high-quality removal mechanism and a high-performance cutting tool design. Therefore, this paper putsforward four stages of a bidirectional progressive spiral milling material removal method to further study the mechanism of ultrasonic milling and the self-designed bidirectional progressive milling cutter, which were used to verify the mechanism of material removal and state of axial force. This study can effectively fill the gap of high-quality processing technology of carbon fiber composites, can solve the technical problems caused by excessive axial force in the process of aerospace carbon fiber processing, and can enrich the technical field of carbon fiber composite processing tool design.
# Compressive Sensing-Based Coding Iterative Channel Estimation Method for TDS-OFDM System
## 3 Cyclic Compressive Sensing Channel Estimation Algorithm Based on Channel Coding
### Separation and Reconstruction of Valid Data from the Aliased Data  
The conventional TDS-OFDM system can only conduct channel estimation in the IBI-free region of the PN sequence, which leads to inaccurate and incomplete channel estimation results and thus greatly affects the symbol detection performance. To overcome this problem, we separate and reconstruct the aliased data, and we then employ the compressive sensing algorithm to improve the accuracy of channel estimation.  
The symbol vector \(\mathbf{z}_{R}^{i}\) of the \(i^{th}\) frame at the receiver is expressed as  
\[\mathbf{z}_{R}^{i}=\left[\begin{array}{c}\psi(i)_{0}+y(i-1)_{N}\\ \psi(i)_{1}+y(i-1)_{N+1}\\ \vdots\\ \psi(i)_{L-1}+y(i-1)_{N+L-1}\\ \psi(i)_{L}\\ \vdots\\ \psi(i)_{M-1}\\ \psi(i)_{M}+y(i)_{0}\\ \vdots\\ \psi(i)_{M+L-1}+y(i)_{L-1}\\ y(i)_{L}\\ \vdots\\ y(i)_{N-1}\end{array}\right] \tag{5}\]  
Since the TDS-OFDM system uses the PN sequence as the guard interval, the cycle performance of data is disrupted. Under the effect of the multipath channel, the L sampling points of valid data at the tail of the received \((i-1)^{th}\) frame will alias with the first L sampling points of the PN sequence in the \(i^{th}\) frame, which causes IBI. Therefore, the TDS-OFDM system cannot apply the frequency domain equalization method commonly used in conventional OFDM systems, and it must splice the tail and head data of the received data to reconstruct the received data. The algorithm process is given below (Algorithm 1):  
1. Extract the IBI-free regions of the PN sequence from the received data \(\mathbf{z}_{R}^{i}\), and conduct channel estimation by LS to obtain the channel estimation value \(\tilde{h}_{0}\);
2. Use \(\tilde{h}_{0}\) obtained in Step (1) to calculate the PN sequence \(\psi(i)\) affected by the multipath channel, where \(\psi(i)\) is the matrix of \((\mathrm{M}+2\mathrm{L})\times 1\);
3. Delete the first M lines of data from the received data \(\mathbf{z}_{R}^{i}\) of the \(i^{th}\) frame, and mark the remaining data as \(z_{i}{}^{\prime}\), where \(z_{i}{}^{\prime}\) is the matrix of \(\mathrm{N}\times 1\);
4. Remove the first M lines of the PN sequence data (i.e.,: the \((\mathrm{M}+1)\) to \((\mathrm{M}+\mathrm{L})\) lines of the matrix \(\psi(i)\)) in \(z_{i}{}^{\prime}\) to obtain the valid data \(y_{f}(i)\), where \(y_{f}(i)\) is the matrix of \(\mathrm{N}\times 1\);
5. Repeat Steps (1) and (2) to obtain the updated channel estimation value and the PN sequence \(\psi(i+1)\) affected by the multipath channel;
6. Delete the L lines of data from the received data \(\mathbf{z}_{R}^{i+1}\) of the \(i+1\) frame, and mark the remaining data as \(z_{i+1}{}^{\prime}\), where \(z_{i+1}{}^{\prime}\) is the matrix of \(\mathrm{L}\times 1\);
7. Remove the PN sequence \(\psi(i+1)\) in \(z_{i+1}{}^{\prime}\) to obtain \(y_{e}(i)\), where \(y_{e}(i)\) is the matrix of \(\mathrm{L}\times 1\);  
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# Delegation-Based Personal Data Processing Request
## 3 Delegation-Based Personal Data Processing Request Notarization Framework
### Notarization Framework  
The proposed notarization framework aims to provide an objective view of credibility assurance for evidence data of processing requests and responses for GDPR audit on the processing of personal data. Figure 6 shows the conceptual network configuration of the proposed framework, consisting of DSs (service users), DCs (SPs), SAs (auditors), and notarization systems.  
Figure 5: Notarization framework design goals.  
All data derived from the process of requesting and responding to the processing of personal data between the DS and DC are stored in the BC ledger through a notarization system. SAs can conduct transparent and reliable GDPR compliance audits based on the notarized ledger through the notarization system.  
Moreover, for the reliability of notarization, only authoritative nodes should be allowed to participate in notarization. Therefore, the proposed notarization framework is based on a private BC framework wherein only authorized participants can participate in the BC network. The roles of the network participants are as follows:  
* DS (Service user): To register request information for viewing, correction, deletion, suspension of processing, and transmission of personal information stored by SPs in the system. To query the request and the response records of the DC in the system. To complain to the SA if the DC fails to satisfactorily process the personal data of the DS against the interests and rights of the DS. To grant SAs the authority to query the records of requests for the processing of personal data through the system.
* DC (SP): To respond to requests by the DS for the processing of personal data and register responses in the system. To grant SAs the authority to query response records to requests for the processing of personal data through the system.
* Auditor (SA): With the authority authorized by the DS, to query the system regarding the records of the requests and response for the processing of personal data.  
Figure 6: Notarization framework overview based on BC.  
To conduct a GDPR compliance audit of the DC to determine compliance. To ensure that the DC adheres to the GDPR regulations and is authorized by the DC to inquire the data of the DC when performing an audit. To check the records of the response of the DC to the requests made by the DS stored in the system to determine the regulations are being adhered to.
* Notarization system: To provide services to the web through smart contracts, manage the BC ledger, and manage the authority of network participants. To store the data of the request in the BC when the DS registers a request for the processing of personal data in the system.
## 4 Experiments Results and Discussions
### Experiments and Discussions of Baseline Network Models  
In this section, Alexnet [37], VGG-16, VGG-19, Resnet-50 [38] and Resnet-101 network are used as benchmark feature extraction networks. These networks are popular transfer learning models. Alexnet has the least network layers and Resnet-101 has the most layers. Faster R-CNN is used as target detection model for the experiment.  
According to Table 2 (Numbers in bold format are the best results, the same as below), under the test of the same dataset and target detection model, VGG-19 network can achieve 0.814 positioning accuracy and 0.733 detection accuracy, both of which are the highest values among the baseline networks. The accuracy of Alexnet is the lowest. This may be because the number of layers of Alexnet network is shallow, and the number of convolution cores is relatively small, which limits its deep feature expression ability. For the task of internal crack detection required by the expression ability of highly abstract features, Alexnet has difficult in achieving satisfactory results. It can also be seen that the detection accuracy of VGG-16 is slightly lower than that of VGG-19, because VGG-19 has more parameters, and the same trend can also be obtained in Resnet-50 and Resnet-101. Integrating the positioning accuracy and detection accuracy, VGG-19 is selected as the benchmark model.
## 3 Experimental Section  
### Physical Rehabilitation Scenario  
Prognosis of patient's functioning status, based on evidence or previous cases, help health professionals to analyze patients' case and improve the efficacy of their recommendations. There have already been some initiatives for extracting knowledge patterns about the evolution over time of patients who suffer from _spinal cord injury_ (SCI) [20]. In the following physical rehabilitation scenario, we describe a real life setting where the CDSS is used for research purposes.  
Chronic patients' data in the extra-hospital phase used in the scenario are taken from a bio-psycho-social study which analyzed the evolution of patients with neurological disability [21]. This study contains data of 661 patients who suffer from SCI over a span of 4 years. In total there are 5995 series-based cases and 2943 point-based cases obtained from PIE measurements in the period 2007-2010.  
As an example, [PERSON] is the (anonymized) name of a 51-year-old man from Barcelona who suffers from spastic paraplegia since he had a traumatic injury in 1990. In his last PIE in 2007 (2008 data are not available), his doctor recommended that he seek an employment. After completing his 2009 PIE, his doctor analyzed his health status and recommended for him a change in his activities or environmental factors. Figure 3 shows [PERSON]'s summary and prognosis during his PIE in 2009. The added value of using a CDSS in this scenario, apart from predicting a patient future status, is that the prognosis motivates [PERSON] to find an employment, as he can see the impact of this factor on his functioning. This prediction helps doctors to analyze his case and prescribe recommendations for changes in activities, participation and environmental factors.  
The proposed CDSS predicts [PERSON]'s functioning status based on previous cases. His summary is composed of the cause of functioning limitation, his recommendations, and the four chapters of ICF: body functions, activities and participation, environmental factors and body structures. In Figure 3,indicators of the ICF spinal cord injury (SCI) _core set_ for which data is available appear in ICF sections. Risk attributes which have a severe or grave level, or decrease by 2 levels or more, are outlined.
# Quantum Circuit Synthesis Using Fuzzy-Logic-Assisted Genetic Algorithms
## 2 Background
### Fuzzy Logic  
Fuzzy logic is a mathematical framework that extends classical binary logic to handle degrees of truth [16]. It assigns fractional values between 0 and 1 to variables to indicate its _degree of membership_ to a given state/category. For example, a _fuzzy variable_, with states \"HIGH\", \"MEDIUM\" and \"LOW\", could be 80% \"HIGH\", 15% \"MEDIUM\" and 0.5% \"LOW\", simultaneously. The degree of membership in one category can be independent of the degree of membership in another. This approach allows us to model uncertainty and overlap in real-world phenomena, similar to human linguistic reasoning [30]. Fuzzy variables can be combined using the fuzzy equivalents of Boolean operators such as AND and OR (refer to Table 1), allowing intuitive, linguistically defined, input-output relationships. Numerical input values (_crisp values_), are processed into fuzzy variables via _membership functions_, which map values to states characterized by degrees of membership to different categories. While these functions, often designed based on expert knowledge, can be arbitrary, they are often triangular, sinusoidal, Gaussian, or trapezoidal, and allow variables to belong to multiple categories simultaneously. The outputs can be _defuzzified_ using methods such as the centroid method [31].
# Behavior of Fiber-Reinforced and Lime-Stabilized Clayey Soil in Triaxial Tests
## 1 Introduction  
Civil infrastructure such as buildings, roadways, bridges, embankments, and dams is frequently built upon the usage of soil or rock materials. This kind of material has to be good enough in strength and deformation characteristics to ensure the safety and sustainability of civil infrastructure [1; 2; 3; 4; 5; 6; 7]. To capture and improve the behavior of geological materials, many studies have been carried out [8; 9; 10; 11; 12; 13; 14; 15; 16; 17]. In many engineering applications, however, soils with unsatisfactory engineering properties are encountered [18; 19], triggering the rapid development and wide application of soil improvementtechniques. These techniques fall into three main categories: physical techniques (e.g., vibration [20; 21]), mechanical techniques [22], and chemical techniques (e.g., admixture stabilization [23]).  
Reinforcing soil using randomly distributed fibers dates from more than 5000 years ago when plant roots and stems were used to reinforce building blocks. This technique still remains a hot research issue nowadays and its application fields are widening [24; 25; 26; 27]. The advantages of mixing fibers into soil include increases in shear, compressive, and tensile strength [28; 29; 30; 31]; a reduction in the swelling tendency of expansive soil [32; 33; 34]; reduced brittleness [35; 36; 37; 38]; and so forth. Meanwhile, disadvantages like the slight reduction in initial stiffness with increasing fiber concentration observed by [PERSON] and [PERSON] (2003) [38] are involved.  
Soil stabilization with lime is a chemical technique that results in marked improvements in strength, compaction, and compressibility characteristics [39; 40; 41; 42; 43]; a reduction in expansive soil's swell percentage and swelling pressure [44; 45]; and a decrease in plasticity [46; 47; 48], which can cause unfavorable brittle failure. To remedy this reduced plasticity, both synthetic and natural fibers have been added to lime-stabilized soil [49; 50]. Although the beneficial role of a combination of lime stabilization and fiber reinforcement is an established concept [51; 52; 53; 54; 55], the coupling effect of them with regard to magnitude and microstructural mechanisms is not well understood, especially for natural fibers with special microstructures such as coir and wheat straw.
## 1 Introduction
To prepare an SPME fiber, a thin layer of PPy can be coated on the surface of a conductive material such as a stainless steel narrow wire. In this case, the structured PPy film has a good compatibility with miniaturized analytical systems. PPy can be easily prepared from aqueous or organic solution containing a proper counter ion such as oxalate, dodecyl sulfate or perchlorate [20]. Nicotine is a toxic compound and the most important compound of tobacco, which easily absorbs into the bloodstream and is rapidly delivered to the brain. Therefore, it can cause various human diseases such as peripheral vascular disease, stroke, reproductive disorder, peptic ulcer, esophageal reflux, renal disease, enhancement of malignant hypertension, lung diseases and cancer [21]. Different analytical separation procedures such as solid-phase extraction (SPE) [22], liquid-phase microextraction (LPME) [23] and SPME [24] have been reported for sampling of nicotine in biological fluids. Nicotine is a semi-volatile polar compound, so it needs to be extracted using suitable polar fiber coatings through SPME. Our previous experiences showed a high extraction efficiency, long durability and high chemical/mechanical resistance for the platinized stainless steel fibers coated with nanostructured PPy, for headspace sampling of nicotine from aqueous media [19]. The mentioned research was based on conventional HS-SPME. However, HS mode is generally not efficient for polar compounds. The present study is based on the DI-SPME sampling strategy, which is proper for the extraction and preconcentration of polar species such as nicotine. On the other hand, exposing the fiber to a DC voltage (in DI mode) will improve the selectivity and sensitivity of the analysis. In this research, a PPy coating of the fiber was carried out for DI-SPME and EE-DI-SPME sampling of nicotine in biological fluids, followed by gas chromatography-flame ionization detector (GC-FID) measurement. For this propose, a nanostructured PPy thin film was synthesized and simultaneously coated on the porous and cohesive surface of a platinized stainless steel wire using an amended EPD method. To the best of our knowledge, DI-SPME and EE-DI-SPME have never been reported for the sampling of nicotine.