esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
### Simulation Tests Conducted
#### 4.3.2 Comparison with Terminal Sliding Mode Controller
The values of the force and torque decrease significantly after a short time (Figure 5c), but the torque suddenly increases and decreases after about 8 s, which is not good for the performance of the task. The velocity values are acceptable, as can be seen in Figure 5d. **Sine trajectory with friction** and using \(M_{diag}\). The same step time \(\Delta t=0.003\) s and ODE 3 integration method were applied. From Figure 6a,b, it can be observed that the sine trajectory tracking is not as accurate when frictional forces appear in the dynamics model. However, in this case, the force and torque have smaller values and the suddenly jumping value of the torque is clearly smaller, as shown in Figure 6c. The velocity values are now also slightly lower, as can be seen in Figure 6d. **Complex trajectory without friction** and using \(M_{diag}\). For this desired trajectory, the numerical problems were even greater than for the previous one. The ODE 3 method was adopted, but the time step was reduced 10 times, i.e., \(\Delta t=0.0003\) s. As can be noted in Figure 7a, and which is also confirmed by the results in Figure 7b, the algorithm works properly, ensuring trajectory tracking. The time to reach steady state is about 10 s, so the controller works quickly. Figure 7c shows that the force and moment values are acceptable. However, after about 15 s, there is a sharp change in the value of the moment due to a change in the shape of the desired trajectory. This also indicates the sensitivity of the algorithm when this situation occurs. **Complex trajectory without friction** and using \(M_{diag}\). The algorithm did not provide any acceptable results even when reducing the integration step. **Sine and complex trajectory without friction** and using \(M_{sym}\). The algorithm proved to be completely useless and the task of trajectory tracking remained unsolved. The performance parameters of the TSMC controller are shown in Table 3.  
Figure 5: Simulation results for TSMC and sine trajectory (without friction): (**a**) desired (\(p_{d}\)) and realized (\(p\)) trajectory; (**b**) position errors \(\Delta p_{x},\Delta p_{y}\) (in inertial frame); (**c**) input signals components (applied force and torque) \(\tau_{u},\tau_{j}\); (**d**) actual velocities \(u,v,r\). Figure 6: Simulation results for TSMC and sine trajectory (with friction): (**a**) desired (\(p_{d}\)) and realized (\(p\)) trajectory; (**b**) position errors \(\Delta p_{x},\Delta p_{y}\) (in inertial frame); (**c**) input signals components (applied force and torque) \(\tau_{u},\tau_{j}\); (**d**) actual velocities \(u,v,r\).
## 2 Materials and Methods
### RSVP Task
#### 2.3.2 RSVP Task: Calibration  
RSVP calibration tasks each consisted of 100 inquiry sequences, where a single inquiry sequence comprised a target letter prompt onscreen for 1 s, followed by a fixation cross for 500 ms, and then a series of ten letter stimuli presented one after another at a pace determined by the test condition (no jitter; \"small\" jitter; or \"large\" jitter). Participants were instructed to take note of the target prompt, watch for that same character in the subsequent letter stream, and mentally react to the target character when they saw it appear onscreen. The experimenter asked participants to do their best to refrain from physically reacting to the letter presentations and to wait until between sequences to blink, yawn, cough, etc.  
Letter stimuli included all 26 characters of the English alphabet, as well as the character \"_\" to indicate a space. All letter stimuli appeared at the center of the screen, while a counter at the upper-left corner of the screen kept track of the number of inquiries completed out of the total 100. In 90% of inquiry sequences, the ten flashing letters comprised one target to match the most recent prompt, as well as nine non-target stimuli. In the remaining 10% of inquiries, however, only non-target characters were presented. Target and non-target characters were determined and sequenced randomly for each inquiry. There was a 2 s blank delay period between the disappearance of the final stimulus of one inquiry sequence and the onset of the target prompt for the next inquiry.  
In the \"no-jitter\" condition (Figure 1B), the ten letter stimuli following the fixation cross were presented at a steady rate of 5 Hz (i.e., a fixed SOI of 200 ms), with no substantial blank interval between the offset and onset of adjacent characters. In the \"small\" jitter condition, these same letters were presented using a uniform random range of 150-250 ms (\(200\pm 50\) ms). For the \"large\" jitter condition, this uniform range was increased to 100-300 ms (200 \(\pm\) 100 ms) per character. Jitter ranges of 100 and 200 ms were selected because they, respectively, constituted 50% and 100% of the fixed SOI duration in the no jitter condition. Lastly, in the supplementary \"slow\" calibration at the end of the visit, letter stimuli were presented at a pace of 1 per second, with no jitter. Additionally, the number of inquiry sequences for the slow calibration was reduced from 100 down to 50, in order to help with participant fatigue.
# A Data-Driven Battery Degradation Estimation Method for Low-Earth-Orbit (LEO) Satellites
## 3 Proposed Method
### Battery Modeling
Furthermore, the terminal voltage can be expressed as shown in Equation (9), where it is derived by subtracting the voltage drops across \(R_{i}\) and \(V_{diff}\) from the OCV. To apply the nonlinear function to the Kalman filter algorithm, the state and measurement equations must be partially differentiated to obtain the Jacobian matrix, which is then incorporated into the algorithm [22]. In other words, the Jacobian matrix is constructed by differentiating the state and measurement equations with respect to the state variables. Thus, by linearizing Equation (9) through partial differentiation with respect to each state variable, it can be represented as Equation (10). \[V_{k}=\text{OCV}-V_{diff}-\text{I}_{k(\text{s})}\cdot R_{i} \tag{9}\]  
\[C_{k}=\left[\frac{df(SOC)}{dSOC}\quad-1\quad-i_{k}\right] \tag{10}\]  
To model battery degradation, changes in \(\text{R}_{i}\) were estimated using the UKF algorithm. Battery degradation was defined in terms of the rate of change in \(R_{i}\), as expressed in Equation (11) as follows:  
\[SOH=\frac{R_{i\_EOL}-R_{i\_current}}{R_{i\_EOL}-R_{i\_BOL}}\, \tag{11}\]  
where \(R_{i\_EOL}\) denotes the internal resistance at the end of life, \(R_{i\_BOL}\) denotes the internal resistance at the beginning of life, and \(R_{i\_current}\) denotes the current internal resistance. These parameters were incorporated into the state equations, which were applied using the UKF.
# Towards a Counting Point Detector for Nanosecond Coherent X-ray Science
## 3 Results: High-Speed Data Acquisitions  
Under the PLS-II special operation mode, 300 electron pulses are injected into the consecutive rf-buckets while the rest of the remaining 200 buckets are unfilled. Figure 2a shows an oscilloscope trace of the X-ray pulses that are collected by the APD within 10 revolution clock cycles. The signal from the X-ray pulses striking the detectors is manifested as positive voltage peaks where the average pulse height corresponding to the detection of an individual 10 keV X-ray photon is 8.2 mV. Here, the peaks with an amplitude greater than 2.5 mV are identified with red circles and are counted as the registered photon events in post-processing. Figure 2b shows a histogram of the time gap between two successive X-ray pulses. This result demonstrates that the detector and acquisition system has the ability to resolve the timing of a single X-ray pulse at the count rate of 34 MHz, which corresponds to approximately one detected X-ray photon for every ten pulses. As shown in Figure 3, re-binning the histogram into individual buckets approximates the arrival time of the next photon as a continuous variable. The exponential distribution result implies that the photons are uncorrelated. This is expected since there is no coherent scattering (speckle) in this measurement.  
Figure 1: Sketch of the data acquisition scheme. 10 keV X-ray radiation is delivered from the Silicon (111) monochromator and focused on the 0.5 mm APD sensor by using a set of compound refractive lenses (CRL). The X-ray flux for the measurement is controlled by using varying combinations of aluminum filters positioned before the CRL. The waveforms are acquired by feeding the APD signal to the oscilloscope.
## 3 Development of the EMS and Obtained Results for a Specific Case Study
### BESS Characteristics  
The capacity of the BESS is 2560 kWh. The values of the BESS open-circuit voltage and resistance are \(U_{BESSoc}=1500\) V and \(R_{BESS}=0.05\)\(\Omega\). As the system is an energy-oriented application, the output power that the battery can provide is capped to \(P_{BESSmax}=2560\) kW. The maximum power change in one sample step is \(P_{stepMAX}=100\) kW. With regard to the capacity limits, the minimum \(SOC\) is set to \(SOC_{BESSmin}=5\%\) and the maximum to \(SOC_{BESSmax}=95\%\). These limits are very usual for standard li-ion batteries.  
As mentioned, \(SOC\) and \(P_{BESS}\) minimum and maximum values can be modified in the frame of the optimization processes, while they respect the data sheet limits. The values given above are initial values.  
For the chosen size of the battery, the open-circuit voltage, \(U_{B_{enc,x}}\), is 1500 V, and the internal resistance, \(R_{B}\), is 0.05 \(\Omega\). For the degradation cost calculation of the BESS, an inversion of \(C_{cap}=600\) k\(\xi\) is supposed, as proposed in [31] for a BESS of this size.
## 2 Materials and Methods
### Total Phenolic Content and Antioxidant Activity  
For the determination of the total phenolic content (TPC) and antioxidant activity (AA) of wheat flour, PL, and bread, phosphate-buffered saline extracts (PBS) of pH 7.4 were prepared. Of each sample, 0.5 g was extracted for 1 h with 20 mL of PBS. The samples were shaken for 40 min and centrifuged according to the procedure described by [PERSON] et al. [28]. The collected supernatants were used for the determination of TPC and AA.  
The TPC was determined according to the Folin-Ciocalteu method with some modifications [29]. In brief, 2 mL of the Folin-Ciocalteu reagent (1:5 H\({}_{2}\)O), 0.5 mL of H\({}_{2}\)O, and 0.5 mL extract of the sample were mixed. After 3 min, 10 mL of 10% Na\({}_{2}\)CO\({}_{3}\) was added. The absorbance of the sample was read at 725 nm after 30 min using a UV-Vis spectrophotometer (UV-1900 UV-VIS, Shimadzu, Osaka, Japan). Then, the TPC was determined based on the standard curve for gallic acid and expressed in milligrams of gallic acid equivalent (GAE) per gram DM.  
The antioxidant activity of the samples was determined using the following parameters: the ability to neutralize ABTS radicals, chelating power (CHEL), and ferric reducing power (RED).  
The ABTS** radical cation was generated by the oxidation of 7 mmol/L stock solution of ABTS using 2.45 mmol/L potassium persulfate as the oxidizing agent [30]. The obtained solution was diluted with distilled H\({}_{2}\)O until an absorbance of 0.7 \(\pm\) 0.05 at 734 nm was achieved. Then, 40 \(\upmu\)L of the extract was added to 1.8 mL of ABTS solution, and the absorbance was measured at 5-min intervals. The ability of the extracts to neutralize the ABTS**+** radical (AN) was calculated as follows:  
\[\text{AN}=\frac{\text{A}_{\text{CO}}\ -\text{A}_{\text{SA}}}{\text{A}_{\text{CO}}}. 100\% \tag{2}\]  
where A\({}_{\text{CO}}\) represents the absorbance of the control, and A\({}_{\text{SA}}\) is the absorbance of the sample.  
CHEL was determined using a previously reported method as follows [31]:  
\[\text{CHEL}=\frac{\text{A}_{\text{SA}}}{\text{A}_{\text{CO}}}. 100\% \tag{3}\]  
where A\({}_{\text{SA}}\) and A\({}_{\text{CO}}\) are the absorbance of the sample and the control, respectively.  
RED was determined according to the method described in [32]. In brief, 500 uL of PBS extract, 500 uL of 200 mM sodium phosphate buffer (pH 6.9), and 1% solution of potassium ferricanide (500 uL) were mixed. The mixture was kept at 50 \({}^{\circ}\)C for 20 min. Then, 500 uL of 10% trichloroacetic acid was added, and the mixture was allowed to rest for 5 min. Then, 1 mL of the mixture was mixed with 1 mL of deionized water and 0.2 mL of 1% iron (II) chloride. Absorbance was measured at 700 nm and was considered RED.  
Antioxidant activities were determined as the extract concentration corresponding to 0.5 absorbance value (EC\({}_{50}\) in the case of RED assay) or 50% of activity (EC\({}_{50}\) in the case of ABTS and CHEL assays) based on a dose-dependent mode of action [33]. EC\({}_{50}\) indices were obtained by interpolation from linear regression analysis.
# Energy and Spectral Efficiency Balancing Algorithm for Energy Saving in LTE Downlinks
## 3 The Proposed SET Algorithm  
This section introduces a new energy-efficient algorithm that prolongs battery lifetime at eNodeB, which is able to adaptively adjust the network parameters.
## 2 Materials and Methods  
The metal oxide nanowires selected in this article are ZnO nanowires (50-120 nm in diameter, 2-20 \(\upmu\)m in length), and the solder are nanoclusters composed of multiple Ag nanoparticles (60-150 nm in diameter). They are all purchased from Jiangsu Xianfeng Nano Material Technology Co., Ltd, Nanjing, China. A solution of ZnO nanowires with a mass fraction of 0.065 g/L and a solution of Ag nanoparticles with a mass fraction of 0.125 g/L are mixed, configured into a suspended ethanol solution. The solution is spin-coated on a silicon oxide substrate with marks after being ultrasonically dispersed. The deposited silicon oxide substrate is placed on a nanomanipulation platform (MM3A-EM, Kleindiek, Reutlingen, Germany) built in a scanning electron microscope (SEM, Zeiss Sigma300). Nanoprobes (T-4-5B, GGB) are used to assemble nanowires-solder interconnection structures. The detailed assembly process of this part has been described in the reference [20]. The position of the ZnO nanowires joint before soldering is marked by SEM. The schematic diagram of the femtosecond laser (Pharos, Light Conversion, Vilnius, Lithuania) irradiation system is shown in Figure 1. The wavelength of the femtosecond laser is 1030 nm. The pulse duration of the femtosecond laser is 255 fs. The 1 MHz repetition rate of incident laser is focused by using the optical lenses with the focal length of 50 mm. The diameter of laser focus is about 10\(\upmu\)m. The adjustable output power of 10 mW-100 mW is used to irradiate solders. The electron beam lithography system (EBL, Nanometer Pattern Generation System, Bozeman, MT, USA) can selectively expose the sample coated with a photoresist to construct the electrode structure. After the development, the gold electrode is obtained by a thermal vacuum evaporation instrument (VZZ-300, Vnano Vacuum Technology Co., Ltd., Tianjin, China). The external Keithley 4200 A Semiconductor Characterization System (SCS, Beaverton, OR, USA) is used to characterize the electrical properties of the interconnection structure by the probe station. The photoelectric response based on the interconnection structure is tested under 350 nm UV light irradiation.
# The Development of the Pipe Jacking Guidance Technology
## 4 Prism Method
### Research Status
#### 4.2.1 Algorithmic Research  
By constructing an algorithmic prediction model, the collected data is passed through the prediction model to compensate for the positional error and improve the accuracy. [PERSON] [14] employed the Kalman filtering algorithm to measure and predict prism coordinates in real time. Experimental evidence has demonstrated that the utilization of the attitude solution algorithm in conjunction with the Kalman filter algorithm results in a reduction of error when compared to the conventional prism method of attitude measurement. However, this method exclusively considers smooth motion in establishing the equation of state and makes predictions exclusively for prism coordinate observations, without considering the effects caused by objective factors such as curved pipes. He [15] adopted a coordinate prediction and a coordinate fusion scheme. A Kalman filter prediction model was established for straight segments and curved segments. Through coordinate prediction and data fusion, the influence of prism measurement time asynchrony on measurement error was further improved.
# Smart Ocean: A New Fast Deconvolved Beamforming Algorithm for Multibeam Sonar
## 4 Summary and Discussion  
In this paper, we proposed a new fast deconvolved beamforming algorithm to reduce the computation complexity of the R-L algorithm. Compared to signal subspace beamforming algorithms, such as MUSIC, the proposed algorithm is based on CBF, which is theoretically robust. The robustness was further demonstrated by a simulation and experimental data processing. The principle of reduction is based on the convolution theorem, which utilizes the FFT technique to calculate the convolution and correlation indirectly. Combined with the original accelerated R-L algorithm, we obtained the most efficient R-L algorithm--a fast accelerated R-L algorithm. In the application of this paper, the algorithm could reduce the computing time up to 150 times. The greater the beam number was, the higher the efficiency was. Both theoretical analysis and simulation experiments evaluated the performance of the algorithm. The algorithm made it possible for the real-time high-resolution beamforming of multibeam sonar.  
At the same time, we applied the algorithm to a high-frequency horizontal array. In the application of multibeam sounding system with a frequency of 200 kHz, we obtained a 2D acoustic image of a flat seabed with a sharpening of the beam width, a suppression of the side lobe level, and a restraining the tunnel effect. In the application of Kongsberg M3 with a frequency of 500 kHz, firstly, we performed 2D acoustic imaging on two close spherical targets, and CBF could not distinguish the two targets while deconvolved beamforming could separate them. This proves that deconvolved beamforming has a higher bearing resolution than CBF. Secondly, we performed 2D acoustic imaging on the pool wall. Compared with the rough and fuzzy pool wall profile obtained by CBF, the pool wall profile obtained by deconvolved beamforming was sharper and clearer, and the 2D acoustic image of the pool wall obtained by deconvolved beamforming had no obvious bright side lobe, which proves that the deconvolved beamforming has a narrower main lobe and lower side lobe level.  
Overall, the efficiency of the new fast deconvolved beamforming algorithm was higher than that of the existing deconvolved beamforming algorithm. The advantages of the deconvolved beamforming algorithm were verified in the high-frequency linear array.  
Conceptualization, T.Z.; Formal analysis, J.H. and T.Z.; Funding acquisition, T.Z. and W.D.; Resources, J.S.; Validation, J.H. and W.Z.; Writing - original draft, J.H.; Writing - review & editing, W.D.  
This research was funded by the National Natural Science Foundation of China (grant numberU1709203, 41606115 and 41506115), the National Science and Technology Major Project of China (grant number 2016 ZX05057005), the National Key R&D Program of China (grant number 2016 YC1402303), the Fundamental Research Funds for the Central Universities (grant number HEUCFJ180510).  
The authors declare no conflict of interest.
## 3 Establishment of the Improved Electromagnetic-Thermal Coupling Model  
In the available finite element calculation of the armature velocity and inductance gradient, the conductivities of the armature and track are usually considered as constant. However, in the actual launching process, the armature and track will produce a high temperature rise gradient due to the high frequency current, which causes the change of conductivity and affects the current diffusion, the armature velocity and the inductance gradient. In summary, this paper establishes an improved model by using a parameter identification method. Firstly, multi-group calculations of the armature velocity and inductance gradient are carried out by changing the conductivity values in the finite element analysis software Ls-dyna. Then, the prediction functions for the armature velocity and inductance gradient are obtained according to variation rules of the armature velocity and inductance gradient with conductivity values. Finally, the improved model of electromagnetic-thermal coupling is obtained by parameter identification. A flow chart of the establishment of the improved model is shown in Figure 11.  
Figure 10: Temperature curves of the armature and track.
# Risk Factors of Avulsion Fracture after Iliac Crestal Flap for Jaw Reconstruction
## 2 Patients and Methods  
### Patients  
This study was conducted with 22 patients who underwent iliac crestal flap surgery for jaw reconstruction from February 2013 to July 2019. The patient's medical records and pre- and post-operative CT scan data of iliac bones were evaluated. Within one month after surgery, fracture of the iliac bone occurred in three patients, of which two patients required surgical reduction and one patient was treated with conservative treatment by maintaining bed rest for six weeks.  
Patient 1 was a 70-year-old male who underwent reconstruction surgery using the iliac crestal flap for mandibular reconstruction. The patient had absolute bed rest for 6 days, then conducted a wheelchair ambulation after the 7 th postoperative day (POD) and was discharged on the 18 th POD. About a month after the operation, he was complaining of pain in the donor site while walking downhill. An x-ray of the pelvis was taken and he was diagnosed with an anterior superior iliac spine (ASIS) avulsion fracture (Figure 1a). He underwent open reduction and internal fixation. There was no gait disturbance after surgery during 18 months follow-up.  
Patient 2 was a 78-year-old female who had a partial maxillectomy with verrucous carcinoma and reconstructive surgery with a DCIA flap. She complained of pain in the donor site on the 16 th POD when moving out of bed. There was a fracture of the ASIS on the donor side (Figure 1b). She needed ABR for about a month or more and was treated using symptomatic treatment and gait training with physical therapy in the department of rehabilitation medicine. At 32 days after the fracture, callus formation was observed on x-ray and CT images. Due to her old age and the decrease of muscle, she had 4 weeks of walking practice. She was on follow-up for more than 2 years and she did not have difficulties in walking at her final check-up.  
Patient 3 was a 53-year-old male with reconstruction surgery using an iliac crestal flap after segmental mandibullectomy. After the third POD, he was allowed to move using a wheelchair, and after the fifth POD, gait practice was performed with support from the nursing staff. He complained about the sound from the donor-side pelvis bone at the 24 th POD. An avulsion fracture of the ASIS was founded in pelvis x-ray and CT scan images (Figure 1c). A reductive operation was performed with a titanium mesh. He was discharged after hospitalization for about one month, and no gait disturbance was observed in follow-up checks for more than 3 years.
# Performance Analysis of Existing ITS Technologies: Evaluation and Coexistence
## 3 Performance Evaluation and Comparision
### Performance Evaluation and Comparision
#### 3.3.1 Performance of ITS-G5 Technology
The messages with a large size of 1200 bytes, which are suitable for carrying perception data, such as CPM messages, are shown in Figure 6. Again, the optimal configuration is provided almost by MCS 4 coding, ensuring the best range and reduced update delays for 95% of all received messages. The packet length can directly affect the performance of ITS-G5 technology, where the optimal configuration with MCS 4 coding providing the highest data rate may not always guarantee the most high performance. Thus, a configuration trade off between range and delay arises in some situations.