esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 VSC System Modeling and Quantitative Evaluation Method of System Stability
### VSC Impedance Modeling
\(G_{de}\)(s) = \((1-0.75T_{s}\)s)/(1 + 0.75\(T_{s}\)s) is the control delay link [20]. \(T_{s}\) denotes the sampling period of the system. \[\mathbf{G}_{im}(s)= \begin{bmatrix}G_{i}(s)&0\\ 0&G_{i}(s)\end{bmatrix}  \tag{10}\]  
\[\mathbf{G}_{dem}(s)= \begin{bmatrix}G_{de}(s)&0\\ 0&G_{de}(s)\end{bmatrix}  \tag{11}\]  
\[\mathbf{Y}_{Lm}=\frac{1}{L_{f}(s^{2}+\omega_{1}^{2})} \begin{bmatrix}s&\omega_{1} \\ -\omega_{1}&s\end{bmatrix}  \tag{12}\]  
According to Figure 3 and Equations (10)-(12), the output impedance of the VSC can be obtained as follows,  
\[\mathbf{Z}_{VSC}=\frac{\Delta n_{dq}^{s}}{\Delta i_{odq}}=\left[\mathbf{Y}_{Lm}(s)(\bm {E}-K_{PWM}\mathbf{G}_{dem}(s)(\mathbf{G}_{y2m}(s)+\mathbf{G}_{im}(s)\mathbf{G}_{y1m}(s))) \right]^{-1}(\mathbf{E}+\mathbf{Y}_{Lm}(s)K_{PWM}\mathbf{G}_{dem}(s)\mathbf{G}_{im}(s)) \tag{13}\]  
where \(\mathbf{E}\) represents the unit matrix. The frequency sweeping test is utilized to obtain the simulation results as shown in Figure 4. It can be seen that the analytical model matches the simulation results well which validates the accuracy of the VSC impedance model. Figure 3: Mathematical model of VSC control system in \(dq\) coordinates. In the same way, the matrix model of the grid impedance can be obtained as,  
\[\mathbf{Z}_{g}= \begin{bmatrix}sL_{g}&-\omega_{1}L_{g}\\ \omega_{1}L_{g}&sL_{g}\end{bmatrix}  \tag{14}\]
## 3 Measurement Results
The input voltage signal is given, with a range of 1.315 V to 1.985 V and a frequency of 100 kHz. The measured rising and falling slew rates are each 0.609 V/\(\upmu\)s.  
The transient measurement result of the proposed chopper operational amplifier with a buffer configuration is shown in Figure 5.3 The buffer-transient measurement proceeded with a buffer configuration, with a loading output load of 10 k\(\Omega\) and 120 pF. The input sinusoidal signal was given, with an amplitude of 250 mV and a frequency of 100 kHz. The measurement result shows that the chopper operational amplifier with a buffer configuration operates in a normal operational condition. The transient measurement result of the proposed chopper operational amplifier with an inverting amplifier configuration is shown in Figure 5.4 Resistors of 5 and 10 k\(\Omega\) were used for the inverting amplifier configuration, amplifying a gain of 2. The measurement result shows that the chopper operational amplifier with an inverting amplifier configuration operates in a normal operational condition. Figure 5.5 (**a**) Measured input base current without base current cancellation; (**b**) measured input base current with base current cancellation. Figure 5.6 Power supply rejection ratio (PSRR) measurement result of the proposed chopper operational amplifier. Figure 5.7 Common mode rejection ratio (CMRR) measurement result of the proposed chopper operational amplifier.
# Lower-Latency Screen Updates over QUIC with Forward Error Correction
## 6 Empirical Results on Emulab
### Streaming Updates
Although a high redundancy rate such as 1.1 (i.e., black) yielded close to a 0 message-blocking time for all of the experiments, the redundancy rate of 1.05 (i.e., red) gave the same improvement for the message inter-delivery time as the 1.1 rate and also gave a lower completion time because of lower overhead. Figure 17 shows the FEC overhead with the n/k = 1.05 and n/k = 1.1 rates in terms of the message-blocking and message inter-delivery times when we set the packet-loss rate to 0%. The measured overhead for the message inter-delivery time is less than 10 ms, as seen in Figure 17b. Figure 17: **The overhead of partial+FEC was less than 10 ms in terms of message inter-delivery time. Streaming updates, 0% packet-loss (100 ms RTT, 100 Mbps, BBR): (a) CDF of message-blocking time. (b) Message inter-delivery time. Blue completed at 15.47 s, gold at 16.38 s, red at 15.94 s, and black at 17.1 s.**  
As a summary of the results for the streaming workload and partial order+FEC could help the reduce message blocking and inter-delivery time for a wide range of networks parameters compared to total ordering. For the baseline setting, we could reduce the 99-percentile of the message-blocking time by 230 ms compared to total delivery, as seen in Figure 9 (0.4 ms v.s. 230 ms). These improvements grew as we increased the packet-loss rate and RTT, and we also we obtained better improvement for BBR v.s. CUBIC CCA.
# Lightweight Transformer with Adaptive Rotational Convolutions for Aerial Object Detection
## 3 Proposed Methodology
### Pseudo-Label Guided Learning
#### 3.3.3 Global Consistency Loss  
In addition to the RAW loss, we introduce the GC loss, which enforces consistency between the global layout of pseudo-labels and the predictions made by the student model. This loss is inspired by optimal transport theory and is designed to ensure that the spatial relationships between detected objects are preserved. P\({}_{T}\) and P\({}_{S}\) are the sets of positions of the bounding boxes predicted by the teacher and student models, respectively. The GC Loss aims to minimize the optimal transport cost between these two distributions:  
\[\mathcal{L}_{GC}=\underset{\text{P}}{\text{min}}\sum\
olimits_{i,j}\text{C} \big{(}\text{P}_{Ti},\text{P}_{Sj}\big{)}P_{i,j}, \tag{20}\]  
where C(P\({}_{Ti},\text{P}_{Sj}\)) is the cost of transporting mass from position P\({}_{Ti}\) in the teacher's prediction to position P\({}_{Sj}\) in the student's prediction, and \(P\) is the transport plan that defines how mass is moved between these positions. The transport cost is typically defined as a combination of the spatial distance and the difference in confidence scores:  
\[\text{C}(\text{P}_{Ti},\text{P}_{Sj})=\lambda_{d}\cdot||\text{P}_{T_{i}}- \text{P}_{Sj}||_{2}^{2}+\lambda_{s}\cdot|\hat{s}_{T_{i}}-\hat{s}_{Sj}|, \tag{21}\]  
where \(\lambda_{d}\) and \(\lambda_{s}\) are weighting factors for the spatial and confidence score differences, respectively. The GC Loss is computed using the optimal transport theory, ensuring that the student model learns to replicate the global spatial relationships present in the teacher's predictions, even when pseudo-labels are noisy.  
The overall unsupervised loss \(\mathcal{L}_{u}\) in the PGL framework is the sum of the RAW loss and the GC loss:  
\[\mathcal{L}_{u}=\mathcal{L}_{RAW}+\mathcal{L}_{GC}, \tag{22}\]  
By combining these losses, the PGL framework ensures that the student model is trained not only to mimic the teacher's predictions but also to respect the global structure and orientation accuracy of the objects in the scene. The training process in the PGL framework involves iterating between updating the student model using the combined loss function and updating the teacher model using the EMA of the student model's weights. This iterative process continues until the model converges, resulting in a robust oriented object detection model that leverages both labeled and unlabeled data effectively.
## 1 Introduction
Their observations have shown changes in plantar pressure distribution effectively, as well as greater support of MLA [4] and reduction of excessive foot pronation [16]. [PERSON] et al. [13] observed a reduced level of activation of tibialis posterior, tibialis anterior, and peroneus longus after LDT intervention. In addition, [PERSON] et al. [6] and [PERSON] et al. [20] measured pain sensations after application. The main findings from these studies have confirmed that LDT increased PPT [6] and reduced pain sensations [20] in lower extremity muscles. Moreover, studies have used a short-term application period (from 10 to 20 min) [4; 13; 16; 21; 23]. The current study design is based on an effective method of taping (LDT) and a long-term period of application (72 h) [6] because the clinical approach is very important in treatment strategies (e.g., time and frequency of application). A greater level of understanding of LDT techniques may be very useful in injury prevention of the lower extremity, especially for the foot and ankle. Thus, the purpose of the present study was to investigate the changes in plantar foot force distribution (i.e., the percentage of force and force distribution under the rearfoot and forefoot) and plantar pressure pain sensitivity in professional futsal players after long-term LDT. We hypothesized that a reduction in FFD and an increase in PPT over the feet would occur in response to arch support provided by LDT.
## 4 Experimental Results and Discussion  
According to the experiment results, the average time for the word-by-word sound-generating system to spell a word was as follows: (1) eight directions = \(11.17\,\pm\,0.85\) s; (2) four directions = \(16.27\,\pm\,0.74\) s. The average time for the frequently used sentence system to finish a sentence was as follows: eight directions = \(6.00\,\pm\,0.63\) s; (3) four directions = \(8.86\,\pm\,0.58\) s.  
With the users' experience and their suggestions, this study improved the head-controlled system. Aside from testing the system, the patients also participated in the experiment of coding time count. With head-turn images filmed by the CCD and image scanning, this study obtained the parameters of a head turn and facilitated position detection. The obtained parameters were used as coding elements in a phonetic system. As head feature colors could be captured, it was possible to locate the head without the parameters set in the procedure. This not only simplified parameter adjustment, but also enabled the camera to locate the head at any time.  
In this study, different test environments and scenes were taken, and test results were used as adjustment coefficients in image processing. After the tests, the image processing and environment control methods most suitable for the system were found. The methods could lead to the correct  
Figure 10: The test results of the frequently used Chinese numeric sentences table with head-control interface (**a**) user #1 (**b**) user #2 (**c**) user #3 (**d**) user #4 (**e**) user #5 (**f**) user #6.  
judgment of head-turn directions. The system was used for repeated experiments, and the misjudgment rate was less than 3%, which meant that the system was highly stable.  
For the disabled who cannot accurately operate the bi-system coding of four or eight directions, there was a probability of misjudgment, caused by their inability to control their bodies. In the future, it is suggested that image judgment should be upgraded to make the program more complete. Additionally, the existing four-direction coding is too difficult for Chinese grammatical coding. If it could be extended to the eight-direction judgment for coding, then the combination of the recognized data and the phonetic system would allow users to operate the system more conveniently.  
The detection and coding of the system could also be applied to a rehabilitation system for the disabled. Unlike the traditional method, where the architecture is specifically designed for different movement control training, the combination of computer vision technology and movement control training enables users to train themselves with head turn games, makes rehabilitation training more attractive, and equips rehabilitation with both training and entertainment.
## 3 Proposed Methods  
### Data Pre-Processing  
The data used in these experiments were single 940 \(\times\) 940 pixel images. Therefore, data pre-processing converted EBUS video data into frame-by-frame EBUS image data for training. EBUS data pre-processing comprises six steps, organized as a flowchart in Figure 2. First, the EBUS video in MOV format was frame-by-frame converted into 1920 \(\times\) 1080 pixel EBUS images (Figure 3). The image includes some text and white lines that cannot be used to train the model directly; thus, central cropping is required to remove this information. Peripheral pulmonary lesion information is primarily concentrated around the probe in these EBUS images, so the final cropped image is 940 \(\times\) 940 pixels with the probe as the center. Next, the images were screened to remove images that could potentially impact the results. If there was no PPL within the image or another factor was captured, the EBUS image was excluded. Completely black images and those containing additional information, such as progress bars, were also excluded. Figure 4 shows examples of excluded EBUS images, and the data enhancement process is detailed in Section 3.2. Lastly, the enhanced data were divided into training, validation, and test sets.  
Figure 1: Examples of the benign and malignant peripheral pulmonary lesions in EBUS images. (**a**,**b**) are the benign lesions (**c**,**d**) are the malignant lesions. The benign and malignant lesions are confirmed by the physicians using a needle biopsy.
## 4 Numerical Examples

Table 1: Dominant eigenvalues (sorted by the real part) of the closed-loop time delayed system (\(\tau_{1}=1.0,\,\tau_{2}=0.5\)). Figure 2: Dominant eigenvalues of the closed-loop system without/with time-delays (“+”/“o”). Now consider the numbers of the eigenvalues within two contours using the contour integration (29) with \(\mathbf{J}_{\mathrm{m}}(\lambda)\). For two given contours with radii \(R=2\) and \(R=5\), respectively, the number \(N_{p}\) of all poles of \(f_{\mathrm{m}}(z)=\det(\mathbf{J}_{\mathrm{m}}(z))\) within each contour is 4 and 10, respectively, and each one is equal to that of all poles of the open-loop receptances \(\mathbf{H}_{\mathrm{0m}}(\lambda)\) in (12) within the corresponding contour, as shown in Figure 4a,b with marks \"+\".The following contour integration  
\[(2\pi\mathrm{i})^{-1}\oint_{\partial C}\mathrm{trace}\big{(}\mathbf{J}_{ \mathrm{m}}{}^{-1}(z)\mathbf{J}_{\mathrm{m}}{}^{\prime}(z)\big{)}\mathrm{d}z\]  
gives \(-2.3634\times 10^{-16}\approx 0\) and 3, respectively, which means that the number \(N_{z}\) of eigenvalues of \(\mathbf{J}_{\mathrm{m}}(\lambda)\) within each contour is equal to 4 and 13, respectively, from (29). It is the reason why the parameter \(q\) of the contour integration is chosen to be \(q=14\) (\(\geq 13\)) in the computation of dominant eigenvalues of the closed-loop time delayed system above. Figure 4: _Cont._  
Figure 3: Dominant eigenvalues of the closed-loop time delayed system, obtained by a spectral method [8] (“\(\ast\)”) and by the contour integral method (“o”) with radius \(R=5\).
## 3 Results and Discussion
### Ft\(-\)Ir Spectroscopy  
The FT-IR spectra of powder samples of undoped LaMnO\({}_{3}\) and samples doped with Ga\({}^{3+}\) or Y\({}^{3+}\) are presented in Figure 3 over the 400-4000 cm\({}^{-1}\) wavenumber range. The FT-IR spectra for the LaMnO\({}_{3}\) samples doped with Ga\({}^{3+}\) or Y\({}^{3+}\) ions have the same profile as the pristine sample, and it was found that there are no new bonds for the two dopants' transition elements. Thus, the most significant absorption bands are 600, 850, 980, 1368, 1480 and 3400 cm\({}^{-1}\). The absorption bands recorded around 600 cm\({}^{-1}\) for all samples are due to vibrational modes for the Mn-O-Mn bending and Mn-O stretching bonds of the octahedral MeO6 group, associated with the change in length of the metal-oxygen bonds typical for perovskite compounds [19].  
The two bands situated at 1368 and 1480 cm\({}^{-1}\), respectively, are due to the presence of the CO\({}_{2}\) molecule absorbed on the material's surface. The broad low-intensity peak with a maximum at approximately 3400 cm\({}^{-1}\) may be attributed to two kinds of OH- groups, such as O-H bonds assigned to the stretching modes of surface-adsorbed water and an OH-group corresponding to the lattice connections [20].  
Figure 2: TG/DTG curves for undoped and Ga- or Y-doped LaMnO\({}_{3}\) materials.
## 3 Experimental Set-Up and Procedure
### Digital Static Cone Penetrometer  
A digital static cone penetrometer (SCP) test is a low-cost method for measuring the penetration resistance of fine granular materials during site investigation and laboratory research. The cone tip was hydraulically forced into the soil during the SCP test at a consistent penetration rate. The cone tip resistance and sleeve friction resistance were measured. In this study, a digital SCP was used, which consists of a measuring body with two push handles, a drive rod with a diameter of 16 mm and a length of 498 mm, a 60\({}^{\circ}\) cone (area of cone base Ac = 9.97 cm\({}^{2}\)) assembled at the bottom of the drive rod, a linear variable displacement transducer (LVDT) with a range of 0-200 mm, a data acquisition system with a USB output slot, and a load cell with a capacity of 300 kg, as shown in Figure 4. It also depicts the layout of the test sections in the soil tank.  
The digital SCP is handheld and capable of penetrating sub-base and subgrade soils. The push handles are provided at the head assembly of the digital SCP. It penetrates the cone attached to the driving rod into the soil at a constant force given with the help of body weight. During the testing, both the load cell and the LVDT were active. The load cell assists in measuring the applied load, while the LVDT measures the digital SCP penetration. Both provide the electrical signal to the data acquisition system, transforming it from analogue to digital. The digital SCP was positioned vertically on the surface of the granular soil. Jerks were avoided while pushing the handles of the digital SCP since they affect the output data. The data from the load cell and LVDT were automatically saved in a tabular format with two columns, load and displacement, in the USB output device. When the applied force causes a negligible change in the magnitude of penetration, the device records refusal. It may produce inconsistent readings and damage the cone, driving rod, and load cell.
## 4 Simulation Results
However, with a 3/7 pilot overhead taken into account [1], our proposed approach outperforms the full CSI approach in terms of the net sum rate. For the larger pilot overhead cases such as the high mobility, high frequency cases, our proposed approach will exhibit larger performance gains. Figure 5: Achievable ergodic sum multicast rate performance comparison between the proposed coordinated approach and the conventional uncoordinated approach for Cases A and B. Both the DE and Monte-Carlo results are depicted. Figure 6: Achievable ergodic sum multicast rate performance of the proposed coordinated approach with different numbers of BS antennas. Both the DE and Monte-Carlo results are depicted.
## 3 Experiment Results
### The Performance Study of the Number of Recurrent Layers (\(L\)) and Timing Cost  
Figure 5 depicts the BER and calculation time for different numbers of recurrent layers (\(L\)) and units (\(U\)). \(U\) was fixed at 10 for the layer's curves, while the unit's curves were plotted at \(L=2\). To clearly display DeepESN performance, the 100 Mbps and \(-20.05\) dBm signal case was selected for this part. The calculation time represents the time consumed for DeepESN training and equalization based on the Python compiler installed on a computer with an \"Intell CITM) i5-1235U\" processor and 16.0 gigabytes of random-access memory (RAM).  
Figure 4: (**a**) The DeepESN equalization performance under different receiving powers and sizes of reservoir with different data rates. (**b**) The BER curves of DeepESN (DESN) and ESN for different data-rate signals at the receiving power of \(-20.05\) dBm.  
As the value of \(L\) increases from 1 to 9, the BER experiences a slight decrease from 1.03 \(\times\) 10\({}^{-3}\) to 4.89 \(\times\) 10\({}^{-4}\). However, the calculation time exhibits linear growth with a coefficient of approximately 4 s per layer. The calculation time increases rapidly when \(U\) rises to 3, but the increment afterward is relatively slow. It is worth noting that DeepESN degrades to ESN when \(L=1\). Thus, the improvement in DeepESN's equalization performance can be observed in this plot. However, deeper network configurations demand a larger amount of computational resources.
# Kalman-Filter-Based Tension Control Design for Industrial Roll-to-Roll System
###### Abstract  
This paper presents a robust and precise tension control method for a roll-to-roll (R2R) system. In R2R processing, robust and precise tension control is very important because improper web tension control leads to deterioration in the quality of web material. However, tension control is not easy because the R2R system has a model variation in which the inertia of the web in roll form is changed and external disturbances caused by web slip and crumpled web. Therefore, a disturbance observer (DOB) was proposed to achieve robustness against model variations and external disturbances. DOB is a robust control method widely used in various fields because of its simple structure and excellent performance. Moreover, the web passes through various process steps to achieve the finished product in the R2R process. Particularly, it is important to track the tension when magnitude of the tension varies during process. Feedforward (FF) controller was applied to minimize the tracking error in the transient section where tension changes. Moreover, the signal processing of a sensor using the Kalman filter (KF) in the R2R system greatly improved control performance. Finally, the effectiveness of the proposed control scheme is discussed using experimental results.  
Keywords:disturbance observer; Kalman filter; feedforward control; tension controller; roll-to-roll system +
Footnote †: journal:  
_articles 2019_, _12_, 86; doi:10.3390/a12040086
## 7 Numerical Results  
The numerical experiments in this section compare the efficiency and accuracy of the standard least-squares approach using the standard SVD with the accelerated least-squares approach using rSVD with DEIM. These results are obtained from MATLAB 2021b running on a computer with the following specifications Intel Core i5-1035G4, CPU 1.10, 1.50 GHz, and 8 GB RAM.  
We consider three numerical experiments. The first two numerical experiments use data from images with missing pixels. The last one uses data from the solution of 2D nonlinear flow in porous media with some missing components at certain time instances. This dataset can be considered as a sequence of images in the form of movie data. The accuracy of the reconstruction is measured by the average relative error of the reconstructed missing data. The CPU times used in the reconstruction process will be scaled with the ones used by the standard LS approach to see the speedup of the simulation time.