esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Coriolis Force on Vibration of Annulus Pipe
## 3 Methodology
### FEM Approach
#### 3.2.2 Hinged Pipe  
Let us now consider a pipe hinged to its end nodes and defined with two finite elements. These two elements have the same length \(L\) and the total amount of degrees of freedom is 4, as shown in Figure 4.  
In this case, the elements of the Equation (6) are defined for four degrees of freedom derived from the imposed boundary condition and result in:  
\[K=\frac{EL}{L^{3}}\begin{pmatrix}4L^{2}&-6L&2L^{2}&0\\ -6L&12&-6L&0\\ 2L^{2}&-6L&4L^{2}&0\\ 0&0&0&0\end{pmatrix}+\frac{EL}{L^{3}}\begin{pmatrix}0&0&0&0\\ 0&12&6L&6L\\ 0&6L&4L^{2}&2L^{2}\\ 0&6L&2L^{2}&4L^{2}\end{pmatrix} \tag{14a}\] \[M=\frac{mL}{420}\begin{pmatrix}4L^{2}&13L&-3L^{2}&0\\ 13L&156&-22L&0\\ -3L^{2}&-22L&4L^{2}&0\\ 0&0&0&0\end{pmatrix}+\frac{mL}{420}\begin{pmatrix}0&0&0&0\\ 0&156&22L&-13L\\ 0&22L&4L^{2}&-3L^{2}\\ 0&-13L&-3L^{2}&4L^{2}\end{pmatrix}\] (14b) \[C=\frac{2\rho v}{60}\begin{pmatrix}0&-6L&L^{2}&0\\ 6L&30&-6L&0\\ L^{2}&6L&0&0\\ 0&0&0&0\end{pmatrix}+\frac{2\rho v}{60}\begin{pmatrix}0&0&0&0\\ 0&-30&-6L&6L\\ 0&6L&0&L^{2}\\ 0&-6L&-L^{2}&0\end{pmatrix}\] (14c) \[K_{nl}=\rho v^{2}\begin{pmatrix}\frac{2L}{15}&-\frac{1}{10}&-\frac{L}{30}\\ -\frac{1}{10}&\frac{6}{5L}&-\frac{1}{10}\\ -\frac{L}{30}&-\frac{1}{10}&\frac{2L}{15}\\ 0&0&0&0\end{pmatrix}+\rho v^{2}\begin{pmatrix}0&0&0&0\\ 0&\frac{6}{5L}&\frac{1}{10}&\frac{1}{10}\\ 0&\frac{1}{10}&\frac{2L}{15}&-\frac{L}{30}\\ 0&\frac{1}{10}&-\frac{1}{30}&\frac{2L}{15}\end{pmatrix} \tag{14d}\]  
Following the procedure of the previous case, the unknown quantities are expressed as follows:  
\[\begin{cases}w_{2}=We^{i\omega t}\\ \dot{w}_{2}=\partial_{t}w_{2}\\ \ddot{w}_{2}=\partial_{t,t}w_{2}\end{cases}\quad\begin{cases}\phi_{1}=\Phi e^ {i\omega t}\\ \dot{\phi}_{1}=\partial_{t}\phi_{1}\\ \ddot{\phi_{1}}=\partial_{t,t}\phi_{1}\end{cases}\quad\begin{cases}\phi_{2}= \Phi e^{i\omega t}\\ \dot{\phi}_{2}=\partial_{t}\phi_{2}\\ \ddot{\phi_{2}}=\partial_{t,t}\phi_{2}\end{cases}\quad\begin{cases}\phi_{3}= \Phi e^{i\omega t}\\ \dot{\phi_{3}}=\partial_{t}\phi_{3}\\ \ddot{\phi_{3}}=\partial_{t,t}\phi_{3}\end{cases} \tag{15}\]  
Substituting Equation (15), Equation (14) in Equation (6), dividing by \(e^{i\omega t}\) and collecting the coefficient of \(\omega\) and \(\phi\), it is possible to build the homogeneous system  
Figure 4: Element representation of the pipe hinged to its boundary nodes.  
\[\begin{pmatrix}A_{11}&A_{12}&A_{13}&A_{14}\\ A_{21}&A_{22}&A_{23}&A_{24}\\ A_{31}&A_{32}&A_{33}&A_{34}\\ A_{41}&A_{42}&A_{43}&A_{44}\end{pmatrix}\begin{pmatrix}w\\ \phi_{1}\\ \phi_{2}\\ \phi_{3}\end{pmatrix}=0 \tag{16}\]  
Again, excluding the trivial solution, the natural frequencies are found solving  
\[Det[\mathbf{A}^{hing}]=0 \tag{17}\]  
where  
\[\mathbf{A}^{hing}=\begin{pmatrix}A_{11}&A_{12}&A_{13}&A_{14}\\ A_{21}&A_{22}&A_{23}&A_{24}\\ A_{31}&A_{32}&A_{33}&A_{34}\\ A_{41}&A_{42}&A_{43}&A_{44}\end{pmatrix}\]
## 3 Experimental Results  
In this work, we systematically investigated the classification performance of the introduced LORSAL method compared with four classifiers, Naive Bayes (NB) [27], support vector machine (SVM) [25, 26], linear LR with L1-regularization (LR_L1), linear LR with L2-regularization (LR_L2) for the binary classification of the LV/HV and LA/HA emotional states. These features, including PSD, DE, DASM, RASM, and DCAU, were extracted from the EEG-signals and used directly as inputs for the classifier. The NB in MATLAB was employed as in [27]. The LIBLINEAR [63] software was adopted for the implementation of the LR_L1, and LR_L2 classifiers, respectively, with the default cost parameter. The LIBSVM [64] tool was utilized to implement the SVM classifier by using the linear kernel with default parameters. For simplicity, the parameters for Gaussian kernel and Laplacian prior in the LORSAL method were set as default in [46]. Such parameter settings may be not optimal for EEG-based emotion recognition, but present ideal classification performance in the experiments.
# Barrel Shifter Physical Unclonable Function Based Encryption
## 6 Post-Layout Simulation Results
### Commutativity  
Encryption and decryption rely on function composition. Decrypting a message encrypted by both oneself and another party is required. The other party may have changed the text bit (0 or 1). Thus, delay variation must be independent from the text input. The transmission delay of 1 and 0 should not have a significant difference.  
BS-PUF path delays depend only on the key input. Shift units are sized to achieve balanced pullup and pulldown resistance. Transmission gate NMOS sizing is \(W_{n}/L_{n}=4/3\) PMOS sizing is \(W_{p}/L_{p}=4/1\), where \(L_{n}=L_{p}\).  
Two tests are performed to verify physical commutativity of BS-PUF: (1) Testing rising/falling edge delay in four different (FF, FS, SF, SS) process corners. Transmission time difference for 0 and 1 must be smaller than the counter period (4 ns); (2) Performing Monte Carlo sampling of path delay for inputs 0 and 1. Delays are recorded for all paths without bit shifting. No bit flips should occur in the path delay.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline HD & \([90,100)\) & \([100,110)\) & \([110,120)\) & \([120,130)\) \\ \hline \% & 0.12\% & 2.57\% & 15.68\% & 37.12\% \\ \hline HD & \([130,140)\) & \([140,150)\) & \([150,160)\) \\ \hline \% & 37.29\% & 6.25\% & 0.97\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Inter-chip HD of BS-PUFs 2 nd LSB (HD: Hamming distance; %: percentage of bit-stream pairs with certain HD).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**c1** & **c2** & **c3** & **c4** & **c5** & **c6** & **c7** & **c8** & **c9** & **c10** & **\(p\)-Value** & **Proportion** & **Statistical Test** \\ \hline
15 & 24 & 22 & 19 & 15 & 17 & 10 & 21 & 20 & 37 & 0.005166 & 200/200 & Frequency \\
12 & 18 & 24 & 27 & 15 & 26 & 20 & 13 & 29 & 16 & 0.048716 & 200/200 & BlockFrequency \\
11 & 21 & 20 & 26 & 16 & 22 & 19 & 9 & 24 & 32 & 0.012650 & 200/200 & CumulativeSums \\
15 & 21 & 15 & 21 & 18 & 18 & 28 & 11 & 28 & 25 & 0.099513 & 200/200 & CumulativeSums \\
22 & 25 & 26 & 20 & 18 & 20 & 16 & 18 & 19 & 16 & 0.807412 & 199/200 & Runs \\
17 & 20 & 22 & 21 & 24 & 22 & 18 & 14 & 20 & 0.917870 & 197/200 & Serial \\
24 & 19 & 20 & 19 & 21 & 17 & 18 & 25 & 14 & 23 & 0.825505 & 197/200 & Serial \\ \hline \hline \end{tabular}
\end{table}
Table 2: NIST test results of the 2 nd LSB response.  
According to corner test results, maximum transmission time difference for 0 and 1 is 2.34 ns; this is much smaller than the 4 ns clock period. Consequently, there are no 2 nd LSB flips in Monte Carlo sampling.
# Novel Pointing and Stabilizing Manipulator for Optical Space Payloads
## 4 Simulation Validation
### Isolation Algorithm Verification
Furthermore, considering that the \(\mathbf{C}\prime(s)\), and \(\mathbf{K}_{we}(s)\) is inherently stable, i.e., the unstable pole number of the OLTFM is also zero, the IPFL is bound to be stable according to Nyquist stability criterion. Since the smaller support module mass and the AFC corresponding to LSM is above that of OSM and cross over the 0 dB line at the frequency of 5.16 Hz and 5.57 Hz. Then it is seen that the phase-frequency curve corresponding to LSM crosses over the \(-180^{\circ}\) line from above once at 5.42 Hz, and the phase margin is \(-26.2^{\circ}\). So, the encirclement number is no longer zero; thus, the IPFL corresponding to LSM is unstable. On this basis, the electromagnetic damping of each fine actuator is increased to 1160 N\(\cdot\)s/m from 23.2 N\(\cdot\)s/m to simulate the larger mode damping ratio case of OSM and LSM. For convenience, they are abbreviated as LDOSM and LDLSM, respectively, here. The passive transmissibility comparison is shown in Figure 11. It is seen that the case of LSM and OSM have similar passive transmissibility, and so do LDOSM and LDLSM, which is consistent with the theoretical result that the transmissibility frees from the support modules. As for stability, as shown in Figure 10, the AFC corresponding to LDLSM comes back below the 0 dB line due to the passive transmissibility attenuation at about 5 Hz, so the encirclement number is zero again, and thus, the IPFL is stable. Combining the stability conditions of OSM, LSM, and LDLSM, it could be accepted that the larger mode damping ratios of \(\mathbf{G}_{\text{P}}(s)\) or the smaller ratio of the payload mass to the support module mass, the better robust stability of the IPFL. Figure 9: LOS jitter comparison between the passive isolation and active feedforward isolation. (**a**). comparison in the orientation of (\(0^{\circ}\), \(0^{\circ}\), \(0^{\circ}\)), (**b**). in the orientation of (\(8.5^{\circ}\), \(0^{\circ}\), \(0^{\circ}\)), (**c**). in the orientation of (\(0^{\circ}\), \(-7^{\circ}\), \(0^{\circ}\)), (**d**). that in the orientation of (\(0^{\circ}\), \(0^{\circ}\), \(14^{\circ}\)). Figure 11: Passive transmissibility given in four cases. Figure 10: Open loop transfer functions of the given channel in four cases.
# Chemical Compositional Changes in Over-Oxidized Fish Oils
## Discussion
### Hoki Liver Oil Over-Oxidation
Carboxylic acids can catalyze the chain scission of fatty acid peroxides, thereby increasing the rate of secondary oxidation of an oil [67]. Although we did not measure the level of free fatty acids directly, it is possible that the more marked development of secondary oxidation in hoki liver oil under thermal oxidation Condition B, compared to the anchovy oil, is promoted by a higher level of free fatty acids. Another important aspect of reproducing the experimental conditions was that the hoki liver fish oil used in the study by [PERSON] et al. did not contain added antioxidants, whereas the large majority of fish oils, as ingredients and finished products, contain added antioxidants (typically \(\alpha\)-tocopherol or mixed tocopherols) to protect the oil from oxidation. This hoki liver oil was minimally refined and still contained some tocopherol (all \(\alpha\)-tocopherol, which originates from the liver tissue from which the oil was extracted) but did not contain added antioxidants [68]. The approximately 15-fold lower total tocopherol content than the standard refined and antioxidant-stabilized anchovy oil rendered it much more susceptible to oxidation. The progress of oxidation in hoki liver oil is well illustrated by the measured changes in induction time, which is a parameter that reflects the remaining resistance of an oil to oxidation. This resistance reflects the level of antioxidant that remains at any time point, which still serves to protect the oil from entering in an uncontrolled radical propagation phase that will consume all available peroxididizable polyunsaturated fat. Tocopherols are chain-breaking antioxidants that donate hydrogen to reduce fatty acid peroxyl radicals and alkoxyl radicals to the corresponding alcohols, lowering the possibility for radical reactions to progress [69; 70]. When the induction time had decreased approximately 6-fold, which occurred between day 5 and 15, both primary and secondary oxidation ramped up substantially. Marked increases in PV, p-AV, TOTOX, and oligomers occurred at this time. By day 15, the total tocopherol level was reduced by half and was thereafter completely consumed in the second half of the incubation period. This antioxidant activity was reflected in the near-perfect correlation between induction time and tocopherol content (r = 0.997). In contrast, the formation of total volatiles lagged in terms of the change in induction time compared to the other indicators of secondary oxidation, supporting the idea that a major portion of volatiles is continuously stripped out of the oil by the delivery of oxygen gas to the oil.
## 4 Determination of Indicator Weights Based on Combination Weighting Method
### Determination of the Combination Weight  
In the practical application of AHP, it is difficult to completely reflect the actual situation of the evaluation objects only based on subjective experience, which may lead to the final evaluation results deviating from reality and have certain limitations. In reference [26], the ISM-AHP combination weighting method is applicable to systems with many variables and unclear structures, but the structural relationship formed is too complicated. In the evaluation with a small number of variables, the difficulty is increased. In reference [36], multiplication is applied to connect the subjective and objective weights, which can improve the ability to reflect information of data to a certain extent. In reference [37], integration weights of indicators are defined according to AHP and entropy weight method, as displayed in Equation (11).  
\[w_{i}^{\prime}=aw_{i}^{1}+(1-a)w_{i}^{2} \tag{11}\]  
where \(w_{i}^{1}\) and \(w_{i}^{2}\) are the indicator weights that are based on AHP and the entropy weight method, respectively. The integration weights still have partial subjectivity through this method, because the assignment \(a\) is determined by expert scoring method. While in this paper, after determining the subjective weights by AHP, the information entropy \(H_{i}\) is introduced to eliminate the subjectivity in the evaluation process so that the information of original data can be fully utilized, and the reliability of evaluation results can be effectively improved. The combination weights of evaluation indicators are given, as shown in Equation (12).  
\[w_{i}^{\prime}=w_{i}^{1}H_{i}+w_{i}^{2}(1-H_{i}) \tag{12}\]  
where \(\sum\limits_{i=1}^{m}w_{i}^{\prime}=1\), \(w_{i}^{1}\), and \(w_{i}^{2}\) are the indicator weights that are calculated by AHP and the entropy weight method, respectively.
## 2 Optimization
### Adaptive Learning Rate Optimizers
#### 2.2.4 Adam  
The Adam (_Adaptive Moment Estimation_) optimizer introduced in [28] can be viewed as a \"tweaked\" RMSProp optimizer with added momentum. There are two main differences between RMSProp with momentum and Adam:  
* Estimates of the first moment and second raw moment, i.e., accumulation variables \(\mathbf{m}\) and \(\mathbf{v}\), respectively, used for parameter update in Adam are calculated using exponential moving average.
* Adam includes initialization bias-correction terms for the first and second moment estimates, which are due to their initialization to the vector of zeros in initial iterations biased towards 0.  
Adam update rule is given below.  
\[\mathbf{m}_{0} =\mathbf{0},\ \mathbf{v}_{0}=\mathbf{0}\quad\text{(initialize 1 st and 2 nd moment estimates)}\] \[\mathbf{m}_{t} =\beta_{1}\mathbf{m}_{t-1}+(1-\beta_{1})\
abla J(\mathbf{\theta}_{t-1}) \quad\text{(update biased 1 st moment estimate)} \tag{21}\] \[\mathbf{v}_{t} =\beta_{2}\mathbf{v}_{t-1}+(1-\beta_{2})\left(\
abla J(\mathbf{\theta}_{t -1})\right)^{2}\quad\text{(update biased 2 nd moment estimate)}\] (22) \[\hat{\mathbf{m}}_{t} =\frac{\mathbf{m}_{t}}{1-\beta_{1}^{t}},\ \ \hat{\mathbf{v}}_{t}=\frac{\mathbf{v}_{t}}{1-\beta_{2}^{t}}\quad\text{( compute bias corrected moment estimates)}\] (23) \[\theta_{t} =\mathbf{\theta}_{t-1}-\frac{\eta}{\sqrt{\hat{\mathbf{v}}_{t}+\epsilon}} \circ\hat{\mathbf{m}}_{t}\quad\text{(apply update)}. \tag{24}\]  
Adam and classical momentum are the two most used optimizers used in many papers that reported _state-of-the-art_ results in different fields.
# Using Machine Learning Models and Actual Transaction Data for Predicting Real Estate Prices
## 1 Introduction  
The real estate market is one of the most crucial components of any national economy. Hence, observations of the real estate market and accurate predictions of real estate prices are helpful for real estate buyers and sellers as well as economic specialists. However, real estate forecasting is a complicated and difficult task owing to many direct and indirect factors that inevitably influence the accuracy of predictions. In general, factors influencing real estate prices could be quantitative or qualitative [1]. The quantitative factors possibly include macroeconomic factors [2], business cycles [3], and real estate attributes [4]. The macroeconomic factors contain unemployment rates, share index, current account of a country, industrial production, and gross domestic product [2]. Attributes of real estate, for example, includes past sale prices, land area, years of constructions, floor space, surface area, number of floors and building conditions [1, 4]. The qualitative factors refer to subject preferences of decision makers, such as views [5], building styles, and living environment [1]. However, some difficulties arise in data collection for qualitative factors. For qualitative factors, sometimes these data are suffering from lack of measurements. Data of qualitative factors sometimes are suffering from lack of measurements. Thus, qualitative factors are hard to measure [1]. Therefore, this study did not take qualitative factors influencing real estate prices into considerations and used quantitative data gathered from actual transaction data recording details of real estate transaction data in Taiwan. Four machine learning models were used to forecast real estate prices accordingly. The rest of thisstudy is organized as follows. Section 2 presents literature review of real estate prediction. Section 3 illustrates methods used in this study. Section 4 introduces the proposed real estate appraising system. The numerical results are depicted in Section 5. Section 6 provides conclusions.
## 3 Findings
### Feedback on App Usability and Workflow Modifications
#### 3.3.4 Sending Data  
Sending data to the next management level was significantly easier with the ODK Scan process. In the Scan process, this simply involves getting to an area with internet connectivity and sending data to the server by syncing it. Conversely, in the routine data management process, it is very time and energy consuming: copies of the TB register must be sent to the office through a reliable courier service. One of the field workers explained this process: \"Using sync feature is easier because I do editing mostly at home then I reach an area of connectivity and sync forms. While, for sending paper forms via courier, first I have to carry big sized TB03 register to photocopier to make the copies of TB03, then I have to visit courier office to send the parcel. It will not reach the same day, at least it takes one day and then I have to call office to confirm if they have received the parcel/documents.\"  
Another field worker explained the process of data sharing that he adopted over the intervention period: \"Once editing is finalized, then we connect device to Internet--mostly to WiFi--and open Core Services app for syncing. First I verify my email address then syncing is confirmed. Mostly I used to sync the data from public partner's office, and if I am far away then I go to local Internet cafe to sync data. To ensure my connectivity with Internet I check and confirm WiFi icon on top of the screen.\"  
However, dependence on WiFi internet connection for syncing the data was challenging for two of the field workers, and one of them said, \"It would be very great and easy for us if ODK can sync forms via 3G Internet connectivity. As it is, I scan and edit the forms at home and for syncing forms I have to travel to place where I can have access to WiFi which takes too much time.\"
## 2 Materials And Methods
### Experimental Site  
Atmospheric data and surface fluxes were obtained from January 2014 to December 2018 by a flux tower installed in the experimental area located in a natural vegetation (native fields) characteristic of the Pampa biome, near the city of Santa Maria, Rio Grande do Sul state, Brazil (lat \(-\)29.72; Ion \(-\)53.77; alt 88 m elevation) (Figure 2). The experimental area is used for beef cattle, with the composition of the vegetation uniformly distributed in the area, predominant by _Andropogon lateralis_, _Axonopus affinis_, _Paspalum notatum_, and _Aristida laevis_[43; 44]. The soil type is Planassolo Haplico Eutrofico [45], characterized with high fertility and high water retention. The soil physical proprieties are described in [46].  
The climate of the region is subtropical humid, belongs to the Cfa group in Koppen classification [47], with precipitation evenly distributed throughout the year. The climatic annual  
Figure 2: (**a**) Location of the experimental site in South America and (**b**) in the state of Rio Grande do Sul (the color scale denotes the elevation of terrain at a resolution of 900 m) and (**c**) image of the micrometeorological tower.  
cumulative rainfall level (\(Prec_{clim}\)) is 1545 mm while the average monthly climatological temperature (\(Tair_{clim}\)) is approximately 20 \({}^{\circ}\)C. These climatic values were obtained from the collected data between 1961 and 2013 by the Conventional Meteorological Station (OMM: 83936) located approximately 3.8 km away from the experimental site.
# Depth-Optimized Quantum Circuit of Gauss-Jordan Elimination
## 2 Preliminaries
### Quantum Gates  
In quantum computing, quantum gates in Figure 11 are frequently employed to perform arithmetic operations in quantum circuits. These include the NOT (X), CNOT, Toffoli, and controlled-swap (CSWAP) gates. The X gate changes the state of a qubit, serving as a quantum equivalent of the NOT operation (i.e., \(X\left(a\right)=a\oplus 1\)). The CNOT gate operates on a pair of qubits, where the sate of target qubit is altered based on the state of control qubit. If the control qubit is set to 1, the state of target qubit is inverted; if the control qubit is 0, there are no changes in the target qubit (i.e., CNOT \(\left(a,b\right)=\left(a,a\oplus b\right)\)). This gate effectively performs an XOR operation between the control and target qubits. The Toffoli gate operates with three qubits: two serving as control qubits and one as the target qubit. The target qubit's state is flipped only if both control qubits are 1 (i.e., Toffoli \(\left(a,b,c\right)=\left(a,b,c\oplus a\cdot b\right)\)). This gate XOR is the result of the AND operation of the control qubit to the target qubit. Thus, Toffoli gates can be used for AND operations in quantum circuits. The controlled-swap (CSWAP) gate exchanges the states of two qubits depending on a control qubit, acting as the quantum equivalent of a conditional swap operation (i.e., CSWAP \(\left(a,b,c\right)=\left(a,b,c\right)\) if \(a=0\) and \(\left(a,c,b\right)\) if \(a=1\)).  
These quantum gates allow cryptographic algorithms to be implemented in quantum computing by replacing classical NOT, XOR, AND, and swap (including branch) operations. To optimize quantum circuits, minimizing the number of Toffoli gates is essential. The Toffoli gate has a high cost to implement because it requires a combination of a \(T\) gate (which affects the depth of \(T\)) and a Clifford gate. Several decomposition methods for the Toffoli gate are available, and the total depth represents the depth after the Toffoli gate is decomposed. In this work, following one of the methods presented in [19], we use a decomposition method involving seven \(T\) gates and eight Clifford gates with a \(T\) depth of four and a total depth of eight for a single Toffoli gate.