esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficiency Estimation of Roller Chain Power Transmission System
## 2 Chain Drive Efficiency
### Efficiency of Chain Drive System  
The magnitude of the efficiency of the chain drive system is given by the output energy divided by the overall energy, including output energy and transmission loss energy. The total transmission energy losses in the chain drive can be calculated by summing the sliding frictional loss and damping force in the tight span. The sliding friction force includes the coplanar drive transmission, which is calculated as the surface contact pressure and the lateral offset drive, which is the two-points contact.  
Using the power of the driven sprocket, the transmission efficiency \(\eta\) of the chain in the coplanar drive can be given by Equation (12):  
\[\eta=\frac{T_{o}\cdot w_{s,2}}{T_{o}\cdot w_{s,2}+N_{s,1}\cdot w_{s,1}\cdot(W_ {pin}+W_{\text{{bush}}})+F_{d}\cdot w_{s,1}\cdot R_{s,1}} \tag{12}\]  
Figure 5: Schematic of damping force between two sprockets.  
Transmission efficiency \(\eta^{\prime}\), considering lateral offset, can be written as Equation (13):  
\[\eta^{\prime}=\frac{T_{o}\cdot w_{s,2}}{T_{o}\cdot w_{s,2}+N_{s,1}\cdot w_{s,1} \cdot(W^{\prime}_{pin}+W_{busth})+F_{d}\cdot w_{s,1}\cdot R_{s,1}} \tag{13}\]  
where \(\eta\) is the chain drive efficiency with the coplanar drive; \(T_{o}\) is the torque of the driven sprocket; \(w_{s,1}\) and \(w_{s,2}\) are the rotational speed of the driving and the driven sprocket, respectively; and \(N_{s,1}\) is the number of teeth on the driving sprocket.
## 2 Materials and Methods
### HS-SPME-GC-MS Analysis  
The volatile compounds of burger samples were isolated by means of headspace solid-phase microextraction (HS-SPME) using a Gerstel MPS2 autosampler fitted with a 50/30 \(\upmu\)m Divinylbenzene/Carboxen/Polydimethylsiloxane (DVB/Carboxen/PDMS) SPME fiber (Supelco, Bellefonte, PA, USA). Prior to analysis, the SPME fiber was conditioned for 30 \(\,\) min at 270 \({}^{\circ}\)C, according to the manufacturer's instructions.  
Three grams (\(\pm\) 0.1%) of sample material was transferred into 20 mL glass headspace vials, sealed with aluminum crimp caps lined with PTFE/silicone septa and stored in a cooled tray at 4 \({}^{\circ}\)C until analysis. Samples were equilibrated at 45 \({}^{\circ}\)C for 20 min with intermittent agitation at 250 rpm (5 s on/2 s off). During the last five minutes of incubation,the SPME fiber was conditioned at 270 \({}^{\circ}\)C and then immediately exposed to the vial headspace for 40 min at 45 \({}^{\circ}\)C to extract the headspace volatiles.  
Extracted compounds were subsequently separated and analyzed using an Agilent 7890A/5975C GC-MS system equipped with an Agilent HP-1 ms capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.25 um). The SPME fiber was desorbed for 5 min in the GC inlet at 250 \({}^{\circ}\)C using a 0.75 mm ID HS-SPME liner (Supelco, Bellefonte, PA, USA), in splitless mode. An initial oven temperature of 35 \({}^{\circ}\)C was held for 5 min and increased at 4 \({}^{\circ}\)C/min to 215 \({}^{\circ}\)C, followed by a second ramp of 7 \({}^{\circ}\)C/min to a final oven temperature of 250 \({}^{\circ}\)C, which was held for 5 min. Helium (99.9999%) was used as a carrier gas at a constant flow rate of 1.2 mL/min.  
The MS detector was operated in Electron impact ionization (EI) mode with an ionization energy of 70 eV. The source and quadrupole temperatures were set to 230 and 150 \({}^{\circ}\)C, respectively. Mass ranges were scanned between 40 and 250 m/z.
## 6 Performance Evaluation
### Simulation Results
#### 6.2.5 Packet Delivery Ratio (PDR)  
The higher percentage of successfully received packets at the BS reflects the higher network reliability. In the case of HFECS, multiple data packets received by every CH node are aggregated into a single packet of specified length and then sent to the BS, so we have to incorporate aggregated data packets in PDR calculation instead of normal data packets. The percentage of successfully received aggregated data packets at the BS corresponding to total number of packets generated by all sensor nodes will be the PDR, in the case of HFECS, i.e.,  
\[PDR\ (Aggregated)=\left(\frac{\sum_{i=1}^{n}ADP_{i}}{Total\ Data\ packets\ generated}\right)\star 100 \tag{15}\]  
where \(n\) is the total number of aggregated data packets received at the BS and \(ADP_{i}\) denotes the aggregated data packet received by BS at the \(i^{th}\) instance in Equation (15). Figure 9 shows the PDR for SEP-E, DEEC-E, EBCS, TEEN and HFECS corresponding to different rounds of communication with our network size as \(300\times 300\) m\({}^{2}\). As in the SEP-E protocol, the optimum number of CHs is not guaranteed due to its poor stability and every CH in SEP-E depends on the probability of each type of sensor node without considering the residual energy of sensor nodes, so it results in the reduction in average residual energy and network lifetime, thus leading to degraded PDR in comparison to HFECS, DEEC-E, and EBCS. The TEEN protocol performs worst against SEP-E, DEEC-E, EBCS and HFECS due to the low average residual energy and lower network lifetime with increasing communication rounds. For lesser communication rounds, HFECS shows degraded performance in comparison to EBCS due to the extra energy spent on computation purposes which result in status transitions of a few sensor nodes, thus reducing the PDR. However, for higher communication rounds, our proposed protocol, HFECS, outperforms all other clustering schemes due to its higher network lifetime, higher average residual energy for larger network sizes, data aggregation performed at the CH, and collision avoidance achieved through the assignment of TDMA slots to every CM, thus resulting in the increase in success rate of aggregated data packets at BS. The percentage improvement of HFECS in terms of PDR can be seen in Table 14.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Schemes**} & \multicolumn{5}{c}{**Network Size**} \\ \cline{2-7}  & \(\mathbf{100\times 100}\) & \(\mathbf{150\times 150}\) & \(\mathbf{200\times 200}\) & \(\mathbf{250\times 250}\) & \(\mathbf{300\times 300}\) & \(\mathbf{350\times 350}\) \\ \hline SEP-E & \(11.39\) & \(8.64\) & \(15.38\) & \(34.19\) & \(44.8\) & \(58.95\) \\ DEEC-E & \(-2.94\) & \(8.64\) & \(34.73\) & \(57.
# Optical Pupil Shift Correction Method for Large Ground-Based Optical Telescopes
## 1 Introduction
Thus, the changes in the system aberration and optical axis shift were compensated in real time, and the correction effect of the adaptive optical system was improved. The overall optical model of the telescope was used to calculate the degrees of bending and sinking variation at different elevation angles and calibrate the optical axis, which helped eliminate the effect of optical pupil shift on the adaptive optical imaging system. This method not only improves the calibration accuracy but also reduces the number of calibration iterations needed to improve the system efficiency [19; 20]. A ground-based optical telescope with an aperture of up to 4 m was used to study the Cassegrain main system-based optical axis calibration method and optical pupil shift compensation technology. First, the components of the telescope were described in detail, and a simplified overall optical model of the telescope is constructed based on ray tracing. Then, the causes of the system pupil shift, the pupil calibration method, and the compensation process were introduced, based on the card-type master-system coma-free theory, a Hartmann-and-calibration-camera dual detector pupil-calibration compensation method. Finally, the number of effective sub-aperture points of the Hartmann detector in the calibration process of the adaptive optical imaging system was used to verify the effectiveness of the model and the compensation method.
## 4 Results  
### Systematization and Formal Description of the Forming Process  
As an information search is goal-directed (see Section 2.1.2), it is necessary to structure the forming process into sub-goals or sub-tasks; otherwise, it is not possible to determine the causes of operators' information search behavior. Video and interview data revealed seven different sub-tasks with different sub-goals (see also bottom of Figure 4):  
1. Definition of the starting point (sub-goal: determining an optimal starting point of the robot, right in front of the first touching point between the set of rolls and--later--the sheet metal);
2. Kinematic configuration (sub-goal: controlling the fit between the implemented curve progression of the machine tool guided by the robot and the work piece without work piece contact);
3. Setting the starting point/teach-in (sub-goal: transferring the specific starting point coordinates to the robot);
4. Empty run(s) (sub-goal: excluding collisions of the robot with the work table and clamping device during the forming process without a clamped work piece);
5. Resetting the clamping device (sub-goal: optimizing the clamping device after checking the actual curve progression of the machine tool guided by the robot);
6. Fixating the work piece (sub-goal: fixating the work piece correctly into the clamping device);
7. Forming the work piece (sub-goal: forming the work piece gradually by the robot according to the predefined target geometry).  
These sub-tasks were mentioned by all operators during the interviews consistently and were characterized by clear breaks/pauses between the sub-tasks. The sub-tasks lasted approximately between 2.50 and 13.45 min--specifically: 1. defining the starting point: 2.50 min; 2. kinematic configuration: 4.00 min; 3. setting the starting point: 6.45 min; 4. empty run 1: 4.45 min, empty run 2: 5.00 min; 5. resetting the clamping device: 3.50 min; 6. fixating the work piece: 6.00 min; 7. forming the work piece: 13.45 min (note that eye tracking data recording stopped after 7.00 min for operator 2). The the sub-task 4, \"empty-run\", was conducted twice as, halfway through the sub-task, operators detected a high collision potential with the clamping device. Therefore, the clamping device was reset before the empty run was executed a second time to test the adjusted set-up.  
In the following, the roles and responsibilities of the operators during the whole forming process are reported. Then, the monitoring process is described according to the above-mentioned sub-tasks, as monitoring is goal-driven and defined by measurable sub-goals. The sub-tasks 2 \"kinematic configuration\", 4 \"empty run(s)\" and 7 \"forming the work piece\" were chosen exemplary to validate the cognition-based framework, as, within these sub-tasks, anomalies were detected by the operators, which indicates that they are particularly error-prone and, therefore, of special interest.
## 3 Results
### Poultry Litter and Its Biochar on Soil Moisture Content, Bulk Density, and Water Holding Capacity  
Figure 2a shows the results for soil moisture content in treated soils. The incorporation of poultry litter and its biochar improved the moisture content of sandy-loam soils significantly. However, except at an application rate of 120 g/kg, where PLBC350 had a moisture content of 7.58%, BC600-treated soils has statistically higher moisture content than other treatments (Figure 2a). Water-holding capacities (WHC) of the treated soils followed the same pattern as moisture content, with the increased application rates significantly improving WHC compared to the control soil (Figure 2b). Even though there was a slight difference in organic amendment application rates, PLBC600 had a higher WHC of 19.09%. Figure 2c also shows that there is no significant difference in bulk density between treatments (\(p>0.05\)). However, at application rates of 15, 30, 60, and 120 g/kg, BC350- and PLBC600-treated soils have shown a slight or no increase in bulk (Figure 2c).  
Figure 1: Effects of poultry litter and its biochar on soil pH (**a**), Cation exchange capacity (**b**), and Organic matter content (**c**) at different application rates of 0, 15, 30, 60, and 120 g/kg. Bars represent means and standard errors, \(n=3\).
## 4 Results and Discussion
### Dataset Description  
Owing to the lack of publicly available textual datasets on poverty governance, the authors' team created the dataset used in the experiments. Its source is the webpage text searched and extracted by the authors using a web crawler program on the Internet with poverty governance-related keywords in the Chinese style, such as \"targeted poverty alleviation\", \"poverty alleviation\", \"returning to poverty\", etc. Then, we cleaned the webpage text data, filtered the HTML tags and characters unrelated to the main text, and unified the encoding of all texts. To eliminate the influence of geographical names, time, and people's names on the classification results of nouns, we replaced these words in the original texts with unified category markers. Next, based on the standardized format of the texts, industry experts on our team were organized to annotate the text using HLT-PGT. To eliminate as many non-objective factors as possible from the labeling, these experts were divided into three groups, two of which conducted their work back to back. Finally, texts with inconsistent markings from these two groups of experts were taken out and handed over to the third group for independent evaluation. We obtained a labeled dataset with this organizational structure and process (Table 1).
## 3 CPN Models
### Translator  
In order to automatically generate the function and application models along with their power consumption evaluation, the _nesc2 cpn_ translator was implemented. The translator _nesc2 cpn_ generates the power consumption models from nesC programs and then interacts with the CPN Tools that simulates and yields power consumption metrics of these models.  
According to Figure 12, the _nesc2 cpn_ receives a nesC program as input (1a) and some configurations parameters such as hardware platform, level of confidence and batch length (1b) (see Section 4.2.1). Next, it accesses the repository of CPN basic models (2-3) and generates the respective application model. The generated application model is then passed to the Evaluation Service (4) that simulates it in the Internet and yields the power consumption results (mean, variance and standard deviation) (5-6).  
The _Evaluation Service_ is a Web service (implemented in Java, available at [[http://ec2-23-22-219-74.compute-1.amazonaws.com:8080/EvaluationService/ws?wsdl](http://ec2-23-22-219-74.compute-1.amazonaws.com:8080/EvaluationService/ws?wsdl)]([http://ec2-23-22-219-74.compute-1.amazonaws.com:8080/EvaluationService/ws?wsdl](http://ec2-23-22-219-74.compute-1.amazonaws.com:8080/EvaluationService/ws?wsdl))) that has two functions, named evaluateApplication and getResult. Function evaluateApplication is used to evaluate a model asynchronously as the evaluation can be take hours or days. When this function is invoked, it saves the model (to be evaluated latter) and returns a _request id_. This _request id_ is used to get the results of the evaluation by invoking the function getResult.  
The _nesc2 cpn_ translator is an API (implemented in Java, available at [[https://github.com/sensor2](https://github.com/sensor2)]([https://github.com/sensor2](https://github.com/sensor2)) model-group/nesc2 cpn), which provides three methods: evaluateSync, EvaluateAsync, getResult. The first two methods are used to generate and evaluate the CPN models in a synchronous or asynchronous way; method getResult is used to retrieve the results when the model in evaluates in a asynchronous way. Finally, the _Repository Manager_ enable us to include and update the basic models in the repository, e.g., to add a basic model of a new hardware platform.
## 4 Experiment of Optimal Sound Absorption for a Four-Parameter SL
### The Experiment of Parameter Determination
#### 4.1.2 Determination of Electrical Parameters  
The coil resistance \(R_{c}\) can be measured directly using a Digital Multi-Meter. In contrast, the coil inductance \(L_{c}\) has a frequency-dependent nonlinearity, so its value as determined by multi-meter measurement would be inaccurate. \(L_{c}\) can be fitted under short-circuit states, and when the \(R_{c},L_{c},Bl\) are introduced, the system acoustic impedance can be expressed as:  
\[Z_{short}=j\omega[M_{m}-\frac{(Bl)^{2}L_{c}}{{R_{c}}^{2}+\omega^{2}L_{c}^{2}}] +[\delta_{m}+\frac{(Bl)^{2}R_{c}}{{R_{c}}^{2}+\omega^{2}{L_{c}}^{2}}]+\frac{ \Delta K_{m}}{j\omega} \tag{11}\]  
First, according to the frequency at the resonance peak and the absorption coefficient, the \(Bl\) and the coil \(L_{c}\) can be counted out at the resonance frequency \(f_{0}\), as shown in Equations (12) and (13).  
\[f_{0}=\sqrt{\frac{K_{\text{sys}}}{M_{\text{sys}}}}/2\pi=\sqrt{\frac{\Delta K _{m}}{M_{m}-\frac{(Bl)^{2}L_{c}}{{R_{c}}^{2}+\omega^{2}{L_{c}}^{2}}}}/2\pi \tag{12}\]  
\[\alpha_{0}=1-|\frac{Z_{\text{sys}}-Z_{0}}{Z_{\text{sys}}+Z_{0}}|^{2}=1-|\frac {j\omega_{0}(M_{m}-\frac{(Bl)^{2}L_{c}}{{R_{c}}^{2}+\omega_{0}^{2}L_{c}^{2}} )+(\delta_{m}+\frac{(Bl)^{2}R_{c}}{{R_{c}}^{2}+\omega_{0}^{2}{L_{c}}^{2}})+ \frac{\Delta K_{m}}{j\omega_{0}}-\rho_{0}c_{0}S}{j\omega_{0}(M_{m}-\frac{(Bl) ^{2}L_{c}}{{R_{c}}^{2}+\omega_{0}^{2}{L_{c}}^{2}})+(\delta_{m}+\frac{(Bl)^{2} R_{c}}{{R_{c}}^{2}+\omega_{0}^{2}{L_{c}}^{2}})+\frac{\Delta K_{m}}{j\omega_{0}}+ \rho_{0}c_{0}S}|^{2} \tag{13}\]  
Figure 6: Experimental impedance of the open circuit: (**a**) the specific acoustic resistance; (**b**) the specific acoustic reactance.  
The resistance measured from the three sets of experiments for the short circuit and the open circuit are shown in Figure 7. According to Equations (10b) and (11), the resistance difference between the short circuit and the open circuit is expressed as \({(Bl)^{2}R_{e}}/{\left({{{R_{e}}}^{2}+{\omega^{2}{{L_{e}}}^{2}}}\right)}\). For a small signal input, \(Bl\) can be regarded as a constant value. By using least squares method at each frequency, the theoretical value of \(L_{c}\) can be calculated. The calculated \(Bl\) was 4 Tm, and \(L_{c}\) was 0.68 \(mH\). The actual parameters of the loudspeaker were all obtained. In Figure 8, the theoretical calculation results and the actual sound absorption results are shown to match better in the short-circuit state, which verifies the parameters obtained.  
Figure 8: Short-circuit absorption coefficient comparison.  
Figure 7: Short-circuit and open-circuit resistance comparison.
## 3 Proposed Multiple Stationary Human Targets Detection Method
### Step A: Pre-Processing  
#### 3.1.1 Adaptive Background Subtraction  
The strong time-invariant background usually covers the respiration signal of interest, so the background needs to be removed from the received data. The simplest method is to calculate an average impulse response from all measured impulse responses. However, this method can only be processed after the measurement is completed and cannot be operated in real time.  
Based on the above analysis, we choose the adaptive background subtraction (ABS) method based on the exponential averaging, which replaces a scalar weighting factor \(\alpha\) with a vector of weighting coefficient \(\lambda\)[46]:  
\[p_{n}[m]=\lambda_{n}[m]\times p_{n-1}[m]+(1-\lambda_{n}[m])\times q_{n}[m], \tag{6}\]  
where \(p_{n}[m]\) and \(q_{n}[m]\) are one-dimensional vectors with the size [M \(\times\) 1] and contain'sampled background estimate' and'measured impulse response', respectively. Thus, the new background estimate takes a fraction of the previous estimate and a fraction from the measured impulse response. The weighting coefficient has the size [M \(\times\) 1] and is determined by two thresholds related to motions, and motions are automatically divided into micro- and macro-motions.
# High-Frequency and Spectrum-Clean Shear-Horizontal Acoustic Wave Resonators with AlN Overlay
## 4 Propagation Characteristics of SH Wave in AlN/LiNbO\({}_{3}\)/Si
### Rayleigh Spurious  
With the ability of high electromechanical coupling, the Rayleigh mode performs as the major spurious mode for most SH main-mode devices. In addition to the phase velocities of the Rayleigh mode always being very close to the SH mode, the Rayleigh spurious mode could generate prominent passband notches and near-band spikes for the SH wave filters, and pose severe risks for the application of the nonleaky SH waves. Therefore, the suppression of the Rayleigh spurious mode is highly desirable.  
Figure 9 depicts the simulated \(k_{\rm eff}\)\({}^{2}\) of the Rayleigh spurious mode versus AlN thicknesses for the AlN/LiNbO\({}_{3}\)/Si with different rotation angles and \(h_{\rm LiNbO3}\)/\(\lambda\) = 0.25. While the 30\({}^{\circ}\) and 35\({}^{\circ}\) rotation angles enable near-zero \(k_{\rm eff}\)\({}^{2}\) of the Rayleigh mode, when AlN overlay becomes thicker, the preferred rotation angle is smaller for the low \(k_{\rm eff}\)\({}^{2}\) of the Rayleigh mode. Or, in other words, for different rotation angles of the LiNbO\({}_{3}\) layer, the optimized AlN thicknesses for zero-coupling Rayleigh mode are varied: for relatively lower rotation angle, the optimized AlN thickness would be large to diminish the Rayleigh mode.  
As concluded in the previous section, 0.2\(\lambda\)-0.4\(\lambda\) thick AlN overlay is preferred for enabling the large velocity and \(k_{\rm eff}\)\({}^{2}\) level at the same time. The optimized cut angle for the near-zero coupling of the Rayleigh spurious mode would be between 10\({}^{\circ}\) and 15\({}^{\circ}\)\(Y\)X-LiNbO\({}_{3}\), as shown in the yellow and green curves as examples inside the design range marked in Figure 9. Again, from the green and blue curves in Figure 6a, it can be found that 15\({}^{\circ}\)\(Y\)X-LiNbO\({}_{3}\) with 0.2\(\lambda\) AlN enables larger \(k_{\rm eff}\)\({}^{2}\) of the main mode than 10\({}^{\circ}\)\(Y\)X-LiNbO\({}_{3}\) with 0.3\(\lambda\) AlN. As a result, 15\({}^{\circ}\)\(Y\)X-LiNbO\({}_{3}\) with 0.2\(\lambda\) AlN can be chosen for a high suppression of the Rayleigh spurious mode, as well as enabling large \(v_{\rm p}\) and high \(k_{\rm eff}\)\({}^{2}\) for the main SH main mode simultaneously.  
Figure 9: Simulated \(k_{\rm eff}\)\({}^{2}\) of the Rayleigh spurious mode versus AlN thicknesses for the AlN/LiNbO\({}_{3}\)/Si with different rotation angles and \(h_{\rm LiNbO3}\)/\(\lambda\) = 0.25. The cross labels mark the AlN thicknesses that enables full suppression of the Rayleigh mode.
###### Abstract
SQUID is used to measure magnetic properties of nanoparticles such as magnetization saturation, remanence and blocking temperature while VSM is used as a function of magnetic field, temperature and time [8]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Technique** \\ \hline Size & X-ray Diffraction (XRD), Transmission electron microscopy (TEM), Dynamic light scattering (DLS), UV-Vis Spectroscopy (UV-Vis) \\ \hline Shape & TEM, High-Resolution Transmission Electron Microscopy (HRTEM), Atomic force microscopy (AFM) \\ \hline Surface charge & Electrophoretic mobility (EPM), Zeta potential \\ \hline Size distribution & Dynamic light scattering (DLS), Atomic force microscopy (AFM), Differential centrifugal sedimentation (DCS) \\ \hline Optical properties & UV-Vis, Photoluminescence (PL) \\ \hline Magnetic properties & Vibrating sample magnetometry (VSM), Superconducting quantum interference device magnetometry (SQUID) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Nanoparticle characterization techniques.