esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Summary of Results
At 200 V and a degreaser concentration of 55 g: L\({}^{-1}\) the measured thickness at 3 and 5 min was lower than predicted, with differences ranging from 3.052 \(\upmu\)m to 4.467 \(\upmu\)m. At 4 min, the measured value was higher than predicted (\(-\)2.899 \(\upmu\)m), while at 7 min, it was again lower (4.083 \(\upmu\)m). At 250 V, shorter times (3-5 min) showed that the measured thickness was always lower than predicted, with differences around 4 \(\upmu\)m. At 6 min, there was an increase in measured thickness above the predicted value (+5.130 \(\upmu\)m), but at 7 min, the difference was the largest (\(-\)4.402 \(\upmu\)m), indicating a possible inaccuracy of the prediction model at longer times. At 300 V, the measured thickness was always lower than predicted at 3-5 min, with differences ranging from 2.962 \(\upmu\)m to 3.699 \(\upmu\)m. At 6 min, there was a significant increase in thickness above the predicted value (4.599 \(\upmu\)m), but at 7 min, it was again lower than predicted (3.101 \(\upmu\)m). In summary, the experimental findings align closely with the theoretical predictions of the statistical model. The most influential factors in determining layer thickness are deposition time and applied voltage, with solution concentration and temperature playing supplementary roles. The consistency between experimental results and model predictions supports the model's applicability in optimizing cataphoretic painting processes under varying operational conditions.
# High-Fidelity Fin-Actuator System Modeling and Aeroelastic Analysis Considering Friction Effect
## 5 Aeroelastic Analysis Results and Discussion
### Aeroelastic Characteristics of the System with Freeplay and Friction
An optimal \(F_{\rm c}\) can be obtained by appropriately designing the lubrication, pressure, and the geometry of the contact surface between the friction sheet and fork, so that the system is stable and friction is reduced. In addition to \(\sigma_{1}\) and \(F_{\rm c}\), \(\sigma_{0}\) and \(\sigma_{2}\) also have a non-negligible effect on the aeroelastic characteristics. The smaller the \(\sigma_{0}\), the greater the flight speed at which the fin starts to oscillate, which is the desired effect. However, once the oscillation starts in this case, the amplitude will increase sharply. As mentioned earlier, \(\sigma_{2}\) is related to lubricants, so it is not the focus of this study. It can be seen that larger \(\sigma_{2}\) will slightly increase the speed at which the fin starts to oscillate, but it will also cause the amplitude of oscillation to increase at the same flight speed. As mentioned earlier, a large number of studies have used the Coulomb model. Some researchers have discovered the limitations of Coulomb friction. They have noticed that with the increase of friction, the stable region increased, but the stable boundary changed little [19]. [PERSON] et al. studied the effects of both freeplay and Coulomb friction last year, pointed out that although friction is dissipative, it is not enough to restrain LCO [21]. However, the analysis in this article is sufficient to show that a single factor may have limitations. The synergistic effect of multiple parameters can suppress the LCO in the low-speed range, and it is also effective in increasing the flutter velocity and reducing the oscillation amplitude.
# An Improved Artificial Ecosystem Algorithm for Economic Dispatch with Combined Heat and Power Units
## 4 Simulation Results  
The acquired findings for the ED incorporating CHPUs were contrasted with the basic AEA to illustrate the effectiveness of the proposed MAEA. Both techniques are tested on two standard test systems. The two selected tested networks have different configurations and scalability called 7- and 48-unit systems. The first test system consists of two CHPUs, four POUs, and one HOU. As mentioned in [37], system data is stated as loss coefficients, fuel prices, and CHPU restrictions. For this system, the loading level of power and heat are 600 MW and 150 MWth. The second test system comprises 48-unit systems as mentioned in [38] which illustrates that 4700 MW and 2500 MWth are the load demand and heat demand, respectively, and it has 10 HOUs, 26 POUs, and 12 CHPUs. For the basic AEA and the proposed MAEA, the number of individuals is taken as 100 and the numbers of iterations are 300 and 3000, respectively, for the first and second system. MATLAB 2017b is used to execute the simulated implementations.  
Based on the consideration of power losses, four cases are included in the study, which can be summarized as follows:  
Case 1: Minimization of the fuel costs without loss consideration for the 7-unit system.  
Case 2: Minimization of the fuel costs considering the power losses for the 7-unit system.  
Case 3: Minimization of the fuel costs without loss consideration for the 48-unit system.  
Case 4: Minimization of the fuel costs considering the power losses for the 48-unit system.
# Face De-Identification Using Convolutional Neural Network (CNN) Models for Visual-Copy Detection
## 2 Related Work
### Image-Generation Method  
Feature inversion is a method of reconstructing the original input data using features extracted from the intermediate layers of a trained CNN model. This is aimed at understanding the model's representation from various perspectives. Articles [6; 7] focused on discerning the visual information encoded in each layer of the CNN and identifying information through feature inversion. They demonstrated an optimization technique that reconstructs the image closest to the activation of the layers while preserving the features of the original image as much as possible. GAN (Generative Adversarial Network) [21] is an image-generation model using two networks, a generator and a discriminator. Due to the diversity in input images, common GANs can suffer from mode collapse, where images are transformed only into certain types. To address this, D2 GAN [8] was proposed. Among the two discriminators included in D2 GAN, one differentiates between real and fake images like a regular GAN, while the other assigns high values to generated images and low values to real images, thereby solving the mode-collapse issue. In [22], the authors introduced a de-identification method using D2 GAN for medical images. That paper presented a method to effectively fuse and emphasize critical information in medical images using ED-D2 GAN. In our study, we propose a de-identification method utilizingfeature inversion and D2 GAN. The method using feature inversion reconstructs the input facial image to produce an image that is visually unrecognizable, yet retains the necessary features for image- and video-copy-detection tasks. This is implemented by utilizing the intermediate-layer features of feature inversion. The method using D2 GAN is trained to blur the image while maintaining important features. This method was inspired by the medical-image-fusion method in [22]. An additional discriminator is introduced to the traditional generator and discriminator structure, distinguishing between the blurred and original images, and learning to generate de-identified blurred images while maintaining the crucial features.
## 4 Conclusions  
The winding rotor is a complex system with a coupling structure. Considering the time-varying rotational speed and frequency-dependent supporting parameters, the system dynamic method is adopted to reveal the dynamic characteristics of the winding rotor.  
The simulation results show that the flexible support with rubber O-rings can effectively reduce the unbalance response amplitude of the winding rotor, thereby ensuring that the rotor passes smoothly through the critical speed points, so that the winding rotor can rotate steadily in a wide working speed range.  
The nonlinearity of material constitutive parameters and structure of the rubber O-ring results in the frequency-dependent characteristics of flexible support parameters. Different flexible support stiffness and damping parameters can be obtained by changing the rubber hardness, structural parameters and number of rubber rings. Through the selection of the number and type of rubber rings in the flexible support, the degree of reduction of the system amplitude can be controlled to satisfy the design requirements. It is one of the convenient methods to improve the design only by selecting the rubber O-rings.  
In addition, properly shortening the speed-up time can make the rotor pass quickly through the critical speed points and effectively reduce the vibration amplitude, so as to provide the reference for reasonably determining the rotational speed range and designing working parameters. Of course, to shorten speed-up time means that a large capacity motor drive is required.  
A vibration measurement method of the winding rotor was proposed, and the non-contact measurement device was also developed by using the optical displacement sensor of Keyence. The measuring device is not limited to the material on the surface of the measured object and the distance between the sensor and the measured object.  
The simulation and measurement methods proposed in this paper, as well as the finding, can also be used for investigating and designing the other high-speed flexible support rotor.
# Comparison of Functional Connectivity Analysis Methods in Alzheimer's Disease
### Data Driven Methods  
To overcome the flaws of model/seed-based methods, certain other methods that are independent of prior knowledge or assumed models have been introduced [11]. Based on decomposition techniques, PCA and ICA are widely used in FC analysis. These types attempt to represent the original fMRI dataset as a base vector linear combination (in PCA) or statistically independent component (in ICA). Instead, the complete four-dimensional fMRI data may be divided into time courses and related spatial maps that describe the temporal and spatial features of the data's components [12]. Using ICA, a decrease in FC has been found in older subjects while examining the components in DMN (PCC and anterior cingulate cortex (ACC)) [13]. A significant difference in the connectivity of patterns between AD patients and mild cognitive impairment (MCI) subject have been found using ICA [14]. However, determining how many data components are decomposed into and how ICNs are differentiated from noise is equally challenging in ICA.  
Graph theory is a widely used method to assess the relationships in data [15]. When graph theory is used for FC assessment utilizing fMRI time series, the fMRI data become spatially parceled as per an anatomical map of the brain, and the correlations are calculated across all pairs of activated regions. If the active regions indicate correlations, they are linked together as a network. The group of these regions and their relationships may be represented as a graph with nodes and edges. Both model/seed-based techniques and ICA approaches attempt to collect temporally coherent networks. Graph theory-based methods, by contrast, are more commonly used to explain functional relationships across all the brain nodes (regions). A study focusing on the modularity of functionally active networks shows the topological correlation of regions using graphs and shows the aging effect on FC [16].  
The paper is divided into four parts: the first section starts with an introduction to fMRI, its analysis methods, and the role of fMRI in the diagnosis of AD. The second section explains AD pathophysiology and its effect on FC in the brain. The third section discusses the conceptual details of methods for FC assessment, such as their principles, as well as their challenges and limitations with experimental results. Finally, the fourth section presents the conclusion, which points out the significance of FC analysis in fMRI study.  
Figure 1: Functional connectivity analysis methods.
# 8-W 2-Stage GaN Doherty Power Amplifier Module on 7 \(\times\) 7 QFN for the 5G N78 Band
## 3 Implementation and Measurement  
Figure 7 depicts a photograph of the implemented 2-stage DPAM based on IPD on a \(7\times 7\) QFN. The total area of the DPAM is \(9.5\times 14.8\) mm\({}^{2}\), which includes the external load network. Wavice's 0.3 \(\upmu\) GaN-HEMT process was used to fabricate the transistor dies for both driver and main stages. The total gate widths of the carrier and peaking amplifiers are 3500 \(\upmu\)m and 7000 \(\upmu\)m, respectively. The gate width for the driver amplifier is 2100 \(\upmu\)m. The interstage and input matching networks were implemented using Wavice's GaN-on-SiC IPD process, and each IPD block occupies an area of 4700 \(\times\) 1075 \(\upmu\)m\({}^{2}\). The evaluation board featuring the load network was designed using Rogers' 20 mil RO435 OB, which exhibits a dielectric constant of 3.48. The load network includes high-Q components consisting of Murata's GQM series capacitors and LQW series inductors. The drain supply voltage for both the main and driver stages is set at 48 V. Quiescent currents of 30 mA and 50 mA were applied to the driver stage and the carrier amplifier of the main stage, respectively. Figure 5: Distribution of load impedance over frequency at the current source plane for low power and peak power levels. Figure 6: Insertion loss of load network over frequency. Figure 8 displays the measured output power, gain, and PAE:Figure 8a using a pulsed continuous wave (CW) signal with a duty cycle of 10% and a period of 5 ms and Figure 8b using a down-link 5G NR modulated signal with a signal bandwidth of 100 MHz and a PAPR of 9.3 dB. For the 3.4-3.8 GHz band, the DPAM demonstrated peak output power between 47.5 and 47.9 dBm along with efficient performance curves. Using the modulated signal, a power gain between 26.8 and 28.4 dB and a PAE between 37.8 and 41.9% at an average output power of 39 dBm were achieved. Figure 8: Measured output power, gain, and PAE: (**a**) pulsed CW, (**b**) 5G NR signal. Figure 7: Implemented 2-stage DPAM on a 7 \(\times\) 7 QFN. Figure 9a displays the measured ACLR across the carrier frequencies within the 3.4-3.8 GHz, ranging from \(-19.0\) to \(-28.0\) dBc at an average output power of 39.0 dBm. Figure 9b shows the power spectral density (PSD) at a carrier frequency of 3.55 GHz, measured before and after digital predistortion (DPD) was applied. As a result of DPD linearization, the ACLR improved dramatically from \(-25.21\) dBc to \(-43.01\) dBc. Table 1 presents a performance comparison to previously reported results, demonstrating notable efficiency across the frequency band, even with a higher output PBO of 8.5 dB than previous works. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref. **} & \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Process**} & \multirow{2}{*}{**Driver**} & \multirow{2}{*}{**Freq.
# FPGA Implementation of a Real-Time Edge Detection System Based on an Improved Canny Algorithm
## 2 Proposed Design Strategy
### Improved Canny Edge Detection Algorithm
#### 2.2.3 Non maximum Suppression  
Non maximum suppression (NMS) is an edge-thinning technique for preserving local maxima along the image gradient direction. Figure 9 shows the NMS architecture. The selector is used to find the pixels in the direction equivalent to the current pixel (center pixel of 3 \(\times\) 3 window) direction depicted as a and b in Figure 9. For directions 1 or 8, the values of a and b are g3 and g5; for directions 2 or 3, the values of a and b are g0 and g8; for directions 4 or 5, the values of a and b are g5, and g6; for directions 6 or 7, the values of a and b are g1 and g7; The comparator (CMP) is used to compare the current pixel (g4) with a and b to determine the presence of maxima. If the maxima are present in the current  
Figure 8: Architecture to find the direction.  
Figure 7: Architecture of the gradient magnitude calculation and quadrant_flag computation.  
pixel, then it is preserved. Otherwise, it is suppressed as 0. Considering that the maximum value of g4 requires 10 bits, to save hardware resources for later statistics, we limit the value range to 8 bits. The value generated after the NMS is sent to both the data cache module and the adaptive threshold computation module, as shown in Figure 2. Data can be cached inside the FPGA or outside the FPGA. Here, data are cached in external SDRAM.
# Motor Outcome Measures in Pediatric Patients with Congenital Muscular Dystrophies: A Scoping Review
###### Abstract  
Congenital muscular dystrophies (CMDs) are a heterogeneous group of genetic neuromuscular disorders. They usually occur at birth or in early childhood, with delayed acquisition of motor milestones, and diffuse muscle weakness. A dystrophic pattern is evident on the muscle biopsy. They are highly variable both in terms of severity and clinical evolution and in terms of pathogenetic biochemical mechanisms. The aim of this review is to collect and summarize the current knowledge of motor function in pediatric patients with congenital muscular dystrophies and the instruments used to assess it. This scoping review was conducted using the methodology of PRISMA (extension for Scoping Reviews, PRISMA-ScR). Two databases were queried from January 2002 to November 2022. Articles were identified based on title and abstract. Full-text papers published in peer-reviewed English-language journals were selected. It emerged that motor functional aspects are still underinvestigated in CMD patients, probably due to the rarity of these conditions and the phenotypic variability. The scales used to assess motor function are heterogeneous, as are the age groups considered. Finally, the predominant type of research design is cross-sectional; few studies analyze the progression of motor function over time. All these factors make it difficult to correlate the results of different publications and stress the need for more accurate and shared protocols for assessing motor function in these patients.  
congenital muscular dystrophies; motor outcome measures; motor function; natural history; motor assessment
# Validation of Regional-Scale Remote Sensing Products in China: From Site to Network
## 5 A National Validation Network and an Operational Validation System
### National Network for Validating RSP  
As stated earlier, multi-platform and multi-scale validation activities allow for systematically verifying and improving the proposed validation guidelines and technical specifications. In the meantime, uncertainties in various RSPs can be quantitatively evaluated using diverse validation metrics. Experience obtained from such validation exercises is helpful for establishing a Chinese national validation network. Such a network is being coordinated as a prototype by initially selecting typical stations/sites with sound observational foundations. Currently, 12 anchor stations are chosen over seven representative regions in China [169]. The distribution of these sites as well as each dominant landscape is illustrated in Figure 4. In addition, four of these stations--namely, the Huailai Station, the Hulunber Station, the Jingyuetan Station, and the Heihe Station--have been selected as core observation sites. Basic information about the former three stations is described in Table 2. For the most heavily instrumented Heihe Station, a very detailed description can be found in [169].  
Figure 4: Sketch map of anchor sites in the validation network [169].  
Over the past few years, various validation experiments, including dedicated ground-based remote sensing experiments and intensive field campaigns, were individually and/or jointly conducted. Datasets are collected at these four sites for investigating remote sensing and validation mechanisms and theories. Because each station features distinct local landscapes, specific validation target variables are therefore identified [169]. Validation activities over such diverse environments can still help further improve the validation guidelines and specifications developed in Component 1. This procedure can be progressively performed until it fulfils the validation requirement over various surface conditions and multiple scales. Meanwhile, principles on the establishment of observation fields, deployment of instrumentation, data processing procedures, data quality control, data sharing and product evaluation metrics, etc. can be standardised. Accordingly, more sites and stations can be united to form a recognisable nationwide remote sensing validation network.
## 3 Results
### Radiation-Shielding Performance  
The results for the radiation-shielding performance of one layer of each of the two fiber fabrics are shown in Table 4. In terms of the effective energy of radiation, the shielding rate was high in the low-energy region. However, in the monoenergy region, where the tube voltage was high, no observable difference was seen and the shielding rate was less than 10%. Therefore, it can be inferred that an effective response can be obtained in the low-dose scattered-ray shielding area. Overall, the shielding performance of the fabric containing bismuth oxide was superior to that of the fabric containing barium sulfate. From the measurements of the Al equivalent thickness ratio using the Al filter, fabrics containing barium sulfate provided shielding equivalent to an Al thickness of 0.18-1.48 mm, and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & **Yarn Containing BaSO4** & **Yarn Containing Bi2O3** \\ \hline Fineness (denier) & 75.1 & 76.2 \\ Tensile strength (g/d) & 3.14 & 4.11 \\ Elongation at break (\%) & 29.4 & 69.5 \\ Mineral content (wet\%) & 5 \\ Twists per meter (TPM) & 75 denier/36 filaments \\ \hline \hline \end{tabular}  
* Denier is the thickness unit of the fiber; it is 1 d is when a 9000 m thread weighs 1 g. Filament is the number of yarn in the minimum unit. TPM (twist-per-meter) is the number of twists given to a 1 m thread.  
\end{table}
Table 2: Physical properties of shielding fiber yarns.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Fabric** & \begin{tabular}{c} **Tensile** \\ **Strength (N)** \\ \end{tabular} & \begin{tabular}{c} **Elongation at** \\ **Break (\%)** \\ \end{tabular} & \begin{tabular}{c} **Tearing** \\ **Strength (N)** \\ \end{tabular} & \
\begin{tabular}{c} **Specific** \\ **Weight** \\ **(g/m 2)** \\ \end{tabular} &
\begin{tabular}{c} **Thickness** \\ **(mm)** \\ \end{tabular} \\ \hline Containing & Warp & 740 & Warp & 34 & Warp & 16 \\ BaSO4 & Weft & 290 & Weft & 20 & Weft & 9 \\ \hline Containing & Warp & 710 & Warp & 58 & Warp & 28 \\ Bi2O3 & Weft & 590 & Weft & 76 & Weft & 18 \\ \hline \hline \end{tabular}  
* Denier is the thickness unit of the fiber; it is 1 d is when a 9000 m thread weighs 1 g. Filament is the number of yarn in the minimum unit. TPM (twist-per-meter) is the number of twists given to a 1 m thread.  
\end{table}
Table 3: Characteristics of the shielding fiber fabrics.  
Figure 6: Conditions for weaving of the fabrics.  
fabrics containing bismuth oxide showed the equivalent of 1.52-3.24 mm. These results are consistent with the graph of radiation transmittance by Al thickness, shown in Figure 7. Thus, it was determined that the fabric containing the bismuth oxide provides higher shielding performance. The radiation-shielding rates of the two shielded fibers developed in this study represent an average of 9-13%, a range appropriate for application in shielding such as for low-dose radiation or scattered rays.