esa-sceva/satcom-chunk-collection huggingface dataset:
# Image-Guided Surgical and Pharmacotherapeutic Routines as Part of Diligent Medical Treatment
## 4 MRI-Controlled SI and RDD--Performance and Compliance
### EMC Conformity Control  
The EMC analysis aims to control the influence of hosting in the MRI atmosphere diverse stuffs employed in medical treatments. Intended for EMC analysis, we can reflect expressions (1)-(4) that give the EMF equations:  
\[\mathbf{\
abla}\times\mathbf{H}=\mathbf{J} \tag{1}\]  
\[\mathbf{J}=\sigma\;\mathbf{E}+\mathbf{j}\;\omega\;\mathbf{D}+\mathbf{J}\mathbf{e} \tag{2}\]  
\[\mathbf{E}=-\;\mathbf{\
abla}\;\mathbf{V}-\mathbf{j}\;\omega\;\mathbf{A} \tag{3}\]  
\[\mathbf{B}=\mathbf{\
abla}\times\mathbf{A} \tag{4}\]  
In the EMF Equations (1)-(4), \(\mathbf{H}\), \(\mathbf{E}\), \(\mathbf{B}\), \(\mathbf{D}\), and \(\mathbf{J}\) have been defined before, \(\mathbf{A}\) and \(\mathbf{V}\) are the magnetic vector and electric scalar potentials. \(\mathbf{J_{e}}\) the source current density. The parameters \(\sigma\), \(\omega\), \(\mu\), and \(\varepsilon\) have been defined before. The symbol \(\mathbf{\
abla}\) is a vector of partial derivative operators, and its three possible implications are gradient (product with a scalar field), divergence and curl (dot and cross products, respectively, with a vector field). The Input source term in EMF Equations (1)-(4) is \(\mathbf{J_{e}}\) or its equivalent electric field \(\sigma\)\(\mathbf{E_{e}}\).  
The ruling Equations (1)-(4) can be solved locally in the birdcage RF coil-antenna for reference conditions (without inserted matter) compared to situations involving the diverse controlled matters. This can be accomplished by means of numerical discretized methods [125; 126; 127; 128; 129; 130; 131] or other techniques allowing local computations.  
The magnetic compatibility of a material is characterized by its permeability \(\mu\) (=\(\mu_{0}\). \(\mu_{\pi}\)) or the susceptibility \(\chi\) (=\(\mu_{\pi}-1\)). For a high magnetic material, \(\mu_{\pi}>>1\) and \(\mu_{\pi}\approx\chi\). For non-magnetic material, \(\mu_{\pi}=1\) and \(\chi=0\). Thus, a magnetic material that is MRI-compatible has \(\mu_{\pi}\approx 1\), or \(\chi\approx 0\). In addition, the conductivity \(\sigma\) characterizes the electric compatibility of a material, so an electric conductor that MRI-compatible has \(\sigma\approx 0\). Therefore, a completely MRI-compatible material has zero values for both \(\chi\) and \(\sigma\). In practice, a magnetic field as B\({}_{1}\) could be perturbed by the introduction of a magnetic material with non-zero value of \(\chi\) and indirectly due to eddy currents induced in a material introduced in the imaging scaffold having a non-zero value of \(\sigma\).  
Note that these perturbations depend on the size, the shape, and the orientation of the introduced matter as well as the frequency of the field (as will be demonstrated in the next example).
# Parameter Tuning of PI Control for Speed Regulation of a PMSM Using Bio-Inspired Algorithms
## 6 Results and Discussion
### Case 2 PMSM Response to a Trapezoidal Speed Reference with Variable Load Torque  
The speed references given by a series of trapezoidal shapes, where the change of reference values depends on the slope of the waveform. Then, the reference trajectory is as follows  
\[\omega^{*}=\begin{cases}\left(\frac{\omega_{2}-\omega_{1}}{T_{2}-T_{1}}\times t \right)+\omega_{1}&for\ T_{1}\leq t\leq T_{2}\\ \omega_{2}&for\ T_{2}\leq t\leq T_{3}\\ \left(\frac{\omega_{4}-\omega_{3}}{T_{4}-T_{3}}\times t\right)-\omega_{3}&for \ T_{3}\leq t\leq T_{4}\\ \omega_{4}&for\ T_{4}\leq t\leq T_{5}\\ \left(\frac{\omega_{6}-\omega_{5}}{T_{6}-T_{5}}\times t\right)+\left(\omega_{6 }+\omega_{5}\right)&for\ T_{5}\leq t\leq T_{6}\\ \omega_{6}&for\ t>6\end{cases} \tag{39}\]  
where \(\omega_{1}=0\) rad/s, \(\omega_{2}=500\) rad/s, \(\omega_{2}=\omega_{3}\), \(\omega_{4}=1000\) rad/s, \(\omega_{4}=\omega_{5}\), \(\omega_{6}=800\) rad/s, \(T_{1}=0\) s, \(T_{2}=1.5\) s, \(T_{3}=3\) s, \(T_{4}=4.5\) s, \(T_{5}=6\) s and \(T_{6}=7.5\) s.  
Figure 10 shows the speed tracking of all algorithms for case 2, the speed behavior is shown when the path changes from a slope-dependent value to a constant value. The absolute error is shown in Figure 11, where unlike case 1, the initial error is greater because this path is not as smooth. As in case 1  
Figure 8: Simulated waveforms of the rotor speed responses, Case 1.  
Figure 9: Waveforms of the rotor speed error performance, Case 1.  
the best result is obtained using the BBO and the algorithm with the largest error is CSA. Table 6 shows performance indicators ISE, IAE, ITSE and ITAE for case 2, sorting these algorithms in ascending order according to the smallest error we first have the BBO, followed by FPA, BA, SCA and lastly CSA.  
The results shown prove the usefulness and good performance of nature inspired algorithms. The tests carried out with the gains of the PI controllers calculated for the speed control show that the algorithms have a similar behavior since everyone complies with the tracking of the reference speed trajectory, only highlighting the BBO algorithm as the one that has a minor error.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **ISE** & **IAE** & **ITSE** & **ITAE** \\ \hline FPA & 0.0803 & 0.4513 & 0.1133 & 1.6335 \\ BBO & 0.0784 & 0.4462 & 0.11036 & 1.6120 \\ BA & 0.0804 & 0.4515 & 0.1134 & 1.6338 \\ CSA & 0.0928 & 0.4938 & 0.1349 & 1.7937 \\ SCA & 0.0915 & 0.4896 & 0.1328 & 1.7781 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance indices of PI controllers tuned using BA, BBO, CSA, FPA and SCA in case 2.  
Figure 11: Waveform of the rotor speed error performance, Case 2.  
Figure 10: Simulated waveforms of the rotor speed responses, Case 2.
# A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching
## 2 Overview of Feature Detectors and Descriptors
### [PERSON] et al. [52] proposed a local image descriptor named DAISY. DAISY gives very good results in wide-baseline situations in comparison to pixel- and correlation-based approaches. It is faster than other descriptors such as SIFT and the Gradient Location and Orientation Histogram (GLOH). SURF is also efficient, but it introduces artifacts that degrade the matching performance. DAISY computes descriptors at every pixel without inserting any artifacts, which improves its matching performance when used densely. Gaussian convolutions are used for descriptor computation. This method computes descriptors in real time. This speed increase is because DAISY uses sums of convolutions instead of weighted sums used by other descriptors. It uses a circularly symmetrical weighting kernel.
## 3 Samples and Methods
### Analytical Methods
#### 3.2.2 Soil Major Elements  
According to the standard method, \"Determination of Twenty-two Elements in Geological Samples by Inductively Coupled Plasma Optical Emission Spectrometry\" (WSBB/004-2019) [43], an inductively coupled plasma optical emission spectrometer was used to quantify Al, Fe, Mg, Na, and S in the samples. A sample mass of 0.2500 g was weighed into a PTFE crucible and moistened with a small amount of water, followed by the addition of 6 mL of nitric acid, 10 mL of hydrofluoric acid, and 2 mL of perchloric acid. The mixture was decomposed on a 200 \({}^{\circ}\)C hotplate and was then evaporated. In case of incomplete decomposition, nitric acid and hydrofluoric acid were replenished before evaporation. The slightly cooled mixture was treated with 8 mL of aqua regia, heated on the hotplate until the solution volume was reduced to 3-5 mL, and the crucible walls were rinsed with approximately 10 mL of deionized water. Mild heating (5-10 min) was applied until the solution was clarified. The cooled solution was then transferred to a polyethylene tube, diluted with water, brought to a final volume of 25 mL, and thoroughly mixed. After 24 h of settling, the elemental content in the samples was determined using an ICP 7400-type plasma emission spectrometer produced by Thermo Fisher Scientific, Waltham, MA, USA. The calibration solution series medium is a 10% hydrochloric acid solution. A multi-point calibration curve is constructed using multiple calibration solutions, and the calibration curve is automatically generated by computer software. To ensure the quality of sample data, measurements of standard substances GSR-1, GSS 40, GSS 41, and GSD17a were taken during the experimental process and compared to their standard values, with errors of less than 5%. All samples were measured twice for repeatability. The Limit of Detection (LOD)values were as follows: 0.05% for Al and Mg, 0.1% for Fe and Na, and 20 ug/g for S. It is worth noting that the standard substance/repeat sample qualification rate was 100%.  
According to the standard method, \"Exploration Geochemical Sample Analysis Method X-Ray Fluorescence Spectrometry for Determining Thirty-four Major and Trace Elements\" (WSBB/005-2004) [44], X-ray fluorescence spectrometry was employed to quantify Si, Ca, K, Mn, P, and Ti in the samples. A dried sample mass of 4.00 g was transferred to a sample container, evenly spread within it, and pressed using a pellet press to create a homogenous sample pellet. The prepared samples were directly analyzed for each element using a ZETUM X-ray fluorescence spectrometer produced by Malvern Panalytical, Malvern, WR14 1 XZ, UK. The matrix effects between elements were corrected using the empirical coefficient method and the internal standard method for scatter correction. The LOD were as follows: 0.1% for Si, 0.05% for Ca and K, and 10 ug/g for Mn, P, and Ti. The qualification rate for standard substances/repeat samples was 100%.
# Special Equipment Safety Supervision System Architecture Based on Blockchain Technology
## 2 Requirement Analysis
### Current Requirements
#### 2.2.1 Efficient Accident Responsibility Tracing  
The lack of the ability to trace accident responsibility in traditional systems is the main cause of many safety management problems and unsafe behaviors, which is also the biggest challenge in the current supervision work. The most direct consequence caused by this crucial flaw is that the primary supervisors often become scapegoats after the accident [42]. Based on this situation, primary supervisors have to increase the frequency of field checks of equipment, which leads to their fatigue and inefficiency. [PERSON] et al. believe that the trust crisis among various roles in the supervision system caused by unfair accident responsibility tracing is also worthy of attention [43]. In the last few years, the significance of responsibility consciousness of supervision participants for safety accident prevention has been constantly growing. Nevertheless, the reality is that the responsibility consciousness of the supervision participants is very poor due to the lack of efficient accident responsibility tracing function, resulting in many pervasive irregularities, as follows:  
1. The equipment users collude with the inspection agency to conceal the safety risks of the equipment.
2. In order to reduce maintenance costs, potential safety risks are covered by equipment users in safety management.
3. The inspection agencies are slack in inspection work.
4. Corruption between the primary supervisors and those they supervise.
## 4 Holistic Trash Collection System Architecture
### System Components
#### 4.1.4 Cloud-Based Analytics  
The optimization of garbage collection routes will be facilitated by the cloud-based analytics system, which utilizes data sourced from users through the mobile app and the sensors situated at the central garbage collection facility, as shown in Figure 5. Furthermore, leveraging the historical data on trash levels for each bin maintained by the cloud-based systems, the system will employ machine learning techniques to predict future waste levels in these bins and offer optimized collection route recommendations. Additionally, the system will factor in variables such as traffic conditions, road closures, and potential obstructions that could affect the collection path. It will also encompass attributes like scalability, dependability, and security. Ensuring data security, particularly safeguarding the physical integrity of IoT sensors and employing robust encryption methods, is crucial [42; 43]. The system will be hosted on a cloud platform, such as Microsoft Azure or Amazon Web Services, and will continually enhance the garbage collection and management process through the utilization of real-time data.  
Figure 6 illustrates the route optimization algorithm employed within our proposed system. When the waste bins reach their capacity threshold, users are required to scan the QR code, transmitting the fill level data to the cloud for subsequent bin emptying. In scenarios where multiple bins necessitate servicing concurrently, the cloud-based analytics system performs calculations to determine the most efficient route, considering factors such as minimizing distance and avoiding traffic congestion. Subsequently, this optimized route is relayed to the small garbage truck responsible for collecting waste from the bins and depositing it at the central waste disposal facility.  
Figure 5: Cloud-based data analytics using app data.
# Research on Rolling Bearing Fault Diagnosis Method Based on ECA-MRANet
## 3 Fault Diagnosis Method Based on ECA-MRANet
### ECA-MRANet Network Model  
Most deep-neural-network-based bearing fault diagnosis mine features directly from the original signal, which often exhibits problems such as insufficient information expression and weak generalization performance of fault diagnosis models. Therefore, in this  
Figure 8: Schematic of ECA.  
paper, a multi-channel residual attention network will be used for feature extraction from different perspectives, and data fusion will be performed by using ECA to differentiate the importance of the features learned by the network and to improve learning efficiency. The architecture of ECA-MRANet is shown in Figure 9.  
This model is constructed by stacking Residual CBAM blocks. It has the capability to simultaneously input the CWT time-frequency diagram, original data, and the optimal IMF decomposed by VMD for feature extraction. The attention to crucial feature information is enhanced using ECA, and ultimately, fault classification is achieved through the Softmax function.
# Structural Design and Kinematics Simulation of Hydraulic Biped Robot
## 3 Kinematics Analysis
### Inverse Kinematics
#### 3.2.1 Sagittal Inverse Kinematics  
In the sagittal plane, the knee joint angle of the right leg is \(\theta 4\), and the position coordinates of the hip and ankle joints are known, as shown in Figure 8.  
We get:  
\[cos(\theta_{4})=\frac{R^{2}-l_{4}^{2}-l_{5}^{2}}{2l_{4}l_{5}} \tag{19}\]  
From the inverse tangent function atan2, \(\theta_{4}\) can be obtained  
\[\theta_{4}=a\tan 2\left(\sqrt{1-\left(\frac{R^{2}-l_{4}^{2}-l_{5}^{2}}{2l_{4}l_{ 5}}\right)^{2}},\frac{R^{2}-l_{4}^{2}-l_{5}^{2}}{2l_{4}l_{5}}\right) \tag{20}\]  
where, \(R^{2}=\left(x_{3}-x_{5}\right)^{2}+\left(z_{3}-z_{5}\right)^{2}\).  
The ankle angle \(\theta_{5}\) is composed of \(\theta_{a}\) and \(\theta_{b}\), \(\theta_{a}=a\tan 2(x_{3}-x_{5},z_{3}-z_{5})\), and \(\theta_{b}=a\tan 2(l_{4}\sin\theta_{4},l_{5}+l_{4}\cos\theta_{4})\)  
\[\theta_{5}=\theta_{a}+\theta_{b} \tag{21}\]  
After calculating the knee joint angle \(\theta_{4}\) and the ankle joint angle \(\theta_{5}\), the next step is to calculate the hip joint angle \(\theta_{3}\). During the movement of the robot, the upper body needs to maintain a vertical posture all the time, so the sum of the ankle joint angle, knee joint angle, and hip joint angle is zero, and \(\theta_{3}\) can be calculated by  
\[\theta_{3}=-\theta_{4}-\theta_{5} \tag{22}\]  
Figure 8: Sagittal plane geometric relationship diagram.  
The inverse kinematics of the left leg is obtained in a similar way as for the right leg, and we get  
\[\theta_{I4}=a\tan 2\left(\sqrt{1-\left(\frac{R_{I}^{2}-l_{4}^{2}-l_{5}^{2}}{2l_{4 }l_{5}}\right)^{2}},\frac{R_{I}^{2}-l_{4}^{2}-l_{5}^{2}}{2l_{4}l_{5}}\right) \tag{23}\]  
\[\theta_{I5}=a\tan 2(x_{I3}-x_{I5},z_{I3}-z_{I5})+a\tan 2(l_{4}\sin\theta_{I4},l_{ 5}+l_{4}\cos\theta l_{4}) \tag{24}\]  
\[\theta_{I3}=-\theta_{I4}-\theta_{I5} \tag{25}\]  
where, \(\theta_{I4}\) is the knee joint angle of the left leg, \(\theta_{I5}\) is the ankle joint angle, and \(\theta_{I3}\) is the hip joint angle.
# Joint AP Association and Bandwidth Allocation Optimization Algorithm in High-Dense WLANs
## 3 Network Model and Design Requirements
### Network Model
#### 3.2.1 Demands of Service  
All clients are divided into four classes according to their application businesses: voice (class-1), video (class-2), best-effort (class-3) and background (class-4) stream. The priorities of business are from the high to the low respectively. Since different application businesses have different demands of bandwidth and delay, they can be used to determine the bandwidth allocation.
## 1 Introduction  
The use of mobile phones has grown extensively. However, the use of mobile phones raises a health concern as they emit electromagnetic fields (EMF) during the transmission of data that could potentially affect human health by causing DNA damage [1], human reproductive system damage [2], and inducing brain cancer [3]. Mobile phones communicate by emitting radiofrequency electromagnetic fields from the antennas inside the phone to the base stations nearby [4]. Many studies have figured out how the EMF emitted by phones affects human health, but few of them mention how to prevent it. Apart from changing the habit of using mobile phones [5], not many studies provide practical solutions or physical devices to prevent the EMF emitted by phones. There are some products in the market that claim to prevent the EMF emitted by phones; however, most of them use a shielding method, which does not reduce the harm to human health [6]. Although the ICNIRP international guideline for phone radiation safety has been established, it does not reflect the actual EMF amount that can safely be absorbed by the human body [5]. In addition, a study investigated the effects of the distances between the human head and a mobile phone's antenna on the specific absorption rate (SAR) and found that SAR is decreasedwhen the distance between the user's head and the antenna is increased, showing that increasing the distance between the phone and the human body is one of the most effective ways to prevent the EMF induced by mobile phones [7]. In order to prevent the potential impact of phones' magnetic fields on our health, this research aims to use TRIZ to design prototypes to protect our bodies, especially our brains and reproductive systems. Finally, three prototypes have been created, and the result shows that they effectively reduce the phone's radiation during phone conversations and the phone's standby mode.
## 4 Design of MIM-QSMS Decoder for RC-QC-LDPC Codes
### LUT Optimization
1: Compute \(P_{L|X}\) and \(\Gamma_{ch}\) as described in Section 4.1.1
2: Compute the weight coefficient \(w_{j}\) using (11) for \(j=1,2,\ldots,N_{b,\max}\)
3:\(P_{R|X}^{(0,j)}=P_{L|X},j=1,2,\ldots,N_{b,\max}\)
4:for\(t=1:T_{\max}\)do
5:for\(j=1:N_{b,\max}\)do
6: Compute \(\bar{P}_{R|X}^{(t,j)}\) and \(P_{S|X}^{(t,j)}\) based on (12) and (14), respectively
7: Construct \(\phi_{ch}^{(t,j)}\) and \(\phi_{v}^{(t,j)}\) based on (15)
8: Compute \(\mathcal{B}^{(t,j)}\) and \(P_{B|X}^{(t,j)}\) according to (6) and (18), respectively
9: Perform \([P_{R|X}^{(t,j)}\), \(\Gamma_{v}^{(t,j)}]=\mathbf{DP}(\mathcal{B}^{(t,j)}\), \(P_{B|X}^{(t,j)})\)
10:endfor
11: Compute \(\bar{P}_{S|X}^{(t)}\) by (20)
12: Construct \(\phi_{ch}^{(t)}\) and \(\phi_{v}^{(t)}\) with \(\bar{P}_{S|X}^{(t)}\) by (15)
13: Determine \(\Gamma_{v}^{(t)}\) with \(\bar{P}_{S|X}^{(t)}\) based on (19)
14:for\(j=1:N_{b,\max}\)do
15: Perform Steps 6-8 using \(\phi_{ch}^{(t)}\) and \(\phi_{v}^{(t)}\)
16: Update \(P_{R|X}^{(t,j)}\) using \(\Gamma_{v}^{(t)}\) based on \(\mathcal{B}^{(t,j)}\), \(P_{B|X}^{(t,j)}\)
17:endfor
18:endfor
```  
**Algorithm 1** The Design Flow of MIM-QSMS Decoder with LUT Optimization
## 3 Results  
All participants completed all experimental procedures and reported no adverse effects. Specific data for all participants are shown in Table S1. A paired \(t\)-test on bilateral ankle-dorsiflexion force control capabilities variables indicated a significant reduction of RMSE (\(t_{13}\) = \(-\)2.851 and \(p\) = 0.014; Figure 3b) and SD (\(t_{13}\) = \(-\)3.055 and \(p\) = 0.009; Figure 3d) in the active-tDCS condition compared to the sham-tDCS condition. However, there was no significant difference between two tDCS conditions: (a) mean force (\(t_{13}\) = 0.174 and \(p\) = 0.865; Figure 3a) and SampEn (\(t_{13}\) = 0.017 and \(p\) = 0.987; Figure 3d). These resultsindicate that the active-tDCS protocol transiently improved force accuracy and steadiness in the lower extremities.  
A two-way repeated measures ANOVA on the SD revealed a significant stimulation condition main effect (\(F_{1,\;13}\) = 10.912; \(p\) = 0.006; \(\mathrm{n^{2}}\) = 0.456; Figure 4b). The post hoc analysis indicated that the SD in the active-tDCS condition was significantly lower than in the sham-tDCS condition (\(p\) = 0.006). However, the two-way repeated measures ANOVA showed no significant effects on mean force: (a) stimulation condition \(\times\) limb side interaction (\(F_{1,\;13}\) = 0.001; \(p\) = 0.980; \(\mathrm{n^{2}}\) < 0.001; Figure 4a), (b) stimulation condition (\(F_{1,\;13}\) = 0.030; \(p\) = 0.865; \(\mathrm{n^{2}}\) = 0.002) and (c) limb side (\(F_{1,\;13}\) = 1.207; \(p\) = 0.292; \(\mathrm{n^{2}}\) = 0.085) and SampEn: (a) stimulation condition \(\times\) limb side interaction (\(F_{1,\;13}\) = 4.283; \(p\) = 0.059; \(\mathrm{n^{2}}\) = 0.248; Figure 4c), (b) stimulation condition (\(F_{1,\;13}\) = 1.012; \(p\) = 0.333; \(\mathrm{n^{2}}\) = 0.072), and (c) limb side (\(F_{1,\;13}\) = 0.307; \(p\) = 0.589; \(\mathrm{n^{2}}\) = 0.023). These results suggest that active-tDCS protocol did not induce different force control patterns between feet.  
Figure 3: Bilateral ankle-dorsiflexion force control capabilities. (**a**) Mean force; (**b**) RMSE; (**c**) SD; (**d**) SampEn. Box plots display: (**a**) individual data points as circles, (**b**) mean value as X sign inside box and (**c**) median value as black horizontal line inside box. The top and bottom of each box indicate the interquartile range (IQR = Q3–Q1), with whiskers extending to Q3 + 1.5 \(\times\) IQR (maximum value) and Q1 - 1.5 \(\times\) IQR (minimum value). Asterisk (*) denotes a significant difference between the active- and sham-tDCS conditions (\(p\) < 0.05). RMSE = Root mean square error; SD = Standard deviation; SampEn = Sample entropy.