esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Fast Detection Technology of Dark Currents in a Ge-Si Detector Array
## 4 Conclusions  
By analyzing the dark current characteristics and detection methods of Ge-Si photodetector arrays, this paper proposed and developed a dark current detection system for the rapid scanning and detection of large-scale Ge-Si detector arrays. The high-isolation optical relay was used to quickly select the pixels of the array chip, and the electrometer amplifier was used to amplify the dark current to quickly detect the dark current of each pixel of the detector. We used this system to detect dark currents on array devices. The experimental results indicate that our designed system achieved rapid discrimination of the array performance and rapid localization of damaged pixels. The scanning test results show that the average dark current of the detector array chip was on the nanoampere level, and the proportion of bad points was \(<\)1%. The consistency of the array chip was high, which meets the requirements of light detection at the receiving end of LiDAR. This work laid the foundation for our subsequent development of a LiDAR prototype system.  
Methodology, Z.G.; software, Z.G.; validation, X.L.; formal analysis, Z.L.; investigation, Z.W.; resources, C.L.; data curation, J.S.; writing--original draft preparation, Z.G.; writing--review and editing, M.T.; supervision, J.S.; project administration, M.T.; funding acquisition, J.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers 62090053, 62090054, and 61934003; the Major Scientific and Technological Program of Jilin Province, grant number 20200501007 GX; the Project of Industrial Technology Research and Development of Jilin Provincial Development and Reform Commission, grant number 2020C019-2; and the Program for JLU Science and Technology Innovative Research Team, grant number 2021 TD-39.  
Not applicable.  
Figure 12: Responsiveness of devices A, B, C, and D under different bias voltages.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Bias Voltage** \\ **(V)** \\ \end{tabular} } & \multicolumn{4}{c}{**Responsiveness (A/W)**} \\ \cline{2-5}  & **A** & **B** & **C** & **D** \\ \hline \(-2.5\) & 0.417 & 0.418 & 0.325 & 0.213 \\ \hline \(-2.0\) & 0.435 & 0.421 & 0.335 & 0.226 \\ \hline \(-1.5\) & 0.448 & 0.443 & 0.350 & 0.256 \\ \hline \(-1.0\) & 0.456 & 0.452 & 0.376 & 0.263 \\ \hline \(-0.5\) & 0.478 & 0.455 & 0.383 & 0.286 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Responsiveness of devices A, B, C, and D under different bias voltages.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw/processed data required to reproduce these findings cannot be shared at this time, as they are part of an ongoing study.  
**Acknowledgments:** We are grateful to the anonymous reviewers for constructive comments that improved the quality of the work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction  
The air rudder can provide the control torque and lift required for a high maneuverability flight for hypersonic aircraft operating at high Mach numbers, and is one of the important actuators for achieving flight attitude and trajectory control [1, 2, 3]. The air rudder is usually connected to the hypersonic aircraft through a rudder shaft and a certain gap is reserved to ensure the free rotation of the rudder surface [4, 5]. However, high-temperature airflow can inevitably flow into the gap, which makes the air rudder flow field complicated due to shock wave/shock wave interference and shock wave/boundary layer interaction, resulting in severe local aerodynamic heating [6, 7, 8]. Due to extremely high aerodynamic heating, the surface of the air rudder shaft can be severely ablated, causing irreversible dam age to the aerodynamic layout of the air rudder and threatening flight safety [9]. Therefore, the effective thermal protection system is crucial for the rudder shaft component of aircraft.  
For the typical thermal protection system of high-temperature components for aircraft, the active thermal protection technology has attracted the widespread attention of researchers [10; 11; 12; 13]. This can be attributed to the fact that active thermal protection can utilize the convective heat exchange and even the latent heat during the external liquid coolant injection. It becomes a reusable and promising method without consuming thermal protection materials compared with the traditional passive thermal protection technology [14; 15]. Numerous wind tunnel experiments and numerical simulation research have been conducted to develop the active thermal protection technology, which mainly includes film cooling [16], regenerative cooling [17; 18], and transpiration cooling [19; 20], respectively. However, the experimental and numerical studies of active cooling investigations for the thermal protection system design of the air rudder shaft are still very limited in previous research.  
To address this challenge, the fluid/solid coupling calculation of the air rudder shaft considering phase change under high heat flux conditions is carried out in this research. Numerical investigations are conducted systemically to predict the active cooling performance of the rudder shaft with liquid water considering phase change. The validation of the numerical simulation method considering phase-change heat transfer is further investigated by experiments. The effect of coolant injection flow velocity on the active cooling performance is further analyzed for both the steady state and transient state. Finally, to achieve better cooling performance, an optimized design of the cooling channels is performed in this work.
# Adaptive Pre/Post-Compensation of Cascade Filters in Coherent Optical Transponders
## 3 Adaptive Filtering in Coherent Receivers
Beside ROADMs, data converters (ADC/DAC), driver and optical modulator can have some roll-off at high frequencies [25]. In [26], it is mentioned that the DAC frequency response at 80% of Nyquist sampling frequency, from the output amplitude is attenuated by 2.42 dB. We demonstrate how the amplitude of the common response \(H_{c}(f)\), presented in Equation (3), is capturing the quasi-static frequency-dependent ripple and loss from output of Tx DAC to the input of Rx ADC. In a lab experiment, with a commercial card, we captured the four vectors of FIR filters taps (\(\textbf{H}_{\text{xx}}\)/\(\textbf{H}_{\text{yy}}\)/\(\textbf{H}_{\text{yy}}\)) 50 times, with five ms period, as shown in Figure 3a. Figure 2: Block diagram showing key functional blocks in coherent optical transponder and requirements for the operation of the presented method. The latter illustrates how consistent is the common response in estimating the static frequency response, both phase (as demonstrated in [22]) and amplitude. Since the frequency responses of the filters have intense drooping at high-frequencies, and the center frequency is at an offset (which emphasizes one side of the spectrum), we can see more peaking of about 5 dB on the high positive frequencies compared to negative ones. In the mid-range of the frequency response, the ripples are compensating for both the ripples in channels and the residual ripples from Tx EO/Rx EO/DAC/ADC calibrations. The average of the retrieved common responses of three different scenarios, displayed in Figure 3b, are used in the optimization process. Figure 3: (**a**) Common response of the adaptive filter after convergence; (**b**) Magnitude of common response for link scenarios. The approximation of the shift in the center frequency, from the International Communication Union (ITU) center frequency, is embedded in the estimated frequency response; it is visualized when comparing blue and red curves below. The x-axis is the index of the frequency bins used in the operation, they are mapped to \(-20\) to \(+20\) GHz, i.e., \(+/-\)sampling frequency/2 (referred to as _Fs_/2). Since PDL accumulates in a link mainly due to birefringent elements, such as mechanical stress or variability in the manufacturing process as described in [27], and Wavelength Selective Switches (WSS), as detailed in [28]; therefore, we modeled the maximum PDL in association with filtering.
# Effects of Torso Pitch Motion on Energy Efficiency of Biped Robot Walking
## 5 Simulation Results and Discussion
### Comparison of TPM and TKV on Energy Consumption  
On the one hand, to study the relationship between gait period and walking efficiency, we fix the step length and change the gait period. In this research, we specify a fixed step length \(L\) = 0.3 m, and gait period changes from 0.7 s to 1.2 s. Figure 10 shows the energy consumption in this case.  
Figure 8: Trajectory of ZMP.  
Figure 9: Stick diagram of one step.  
From simulation results, we can obtain the following observations:  
1. The energy consumption per unit distance of TPM is less than that of TKV. When the step length is 0.3 m and the gait period changes from 0.7 s to 1.2 s, the energy consumption per unit distance of TPM is about 12.14% less than that of TKV on average;
2. The optimal period of TKV is 0.85-0.95 s, while that of TPM is 0.9-1.1 s. The effect of the period change of TPM on energy consumption is smaller than that of TKV, except for 0.7 s and 0.75 s.  
On the other hand, to study the relationship between step length and energy consumption, we fix the gait period and changed step length. In this research, we specify a fixed period of \(T_{c}\) = 1.0 s, and step length changes from 0.2 m to 0.4 m. Figure 11 shows the energy consumption in this case.  
From simulation results, we can obtain the following observations:  
1. The energy consumption per unit distance of TPM is less than that of TKV. When the gait period is 1.0 s and step length changes from 0.2 m to 0.4 m, the energy consumption per unit distance of TPM is about 12.61% less than that of TKV on average;  
Figure 11: Energy consumption of two gait patterns with different step lengths when \(T_{c}\) = 1.0 s.  
Figure 10: Energy consumption of two gait patterns with different gait periods when \(L\) = 0.3 m.  
With the increase of step length, the energy consumption per unit distance of both gait patterns increases. This is consistent with our intuition. When humans walk in larger stride, they are more likely to feel tired.
# Relationship between Accuracy, Speed, and Consistency in a Modern Pentathlon Shooting Event
## 3 Results
### Variable Correlation Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variable (Unit)**} & \multirow{2}{*}{**Groups**} & \multirow{2}{*}{**N**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**SD**} & \multirow{2}{*}{**\(t\)**} & \multirow{2}{*}{**\(p\)**} & \multirow{2}{*}{**\(d\)**} & \multicolumn{2}{c}{**95\% CI for \(d\)**} \\ \cline{5-10}  & & & & & & & **Lower** & **Upper** \\ \hline \multirow{4}{*}{Speed (seconds)} & Series-1 & 4912 & 2.354 & 0.368 & 2.344 & 2.365 & \multirow{4}{*}{3, 1, 2 \(<\) 4} & \multirow{4}{*}{0.01} \\ (seconds) & Series-2 & 4912 & 2.358 & 0.368 & 2.348 & 2.369 & \multirow{4}{*}{5.873 *} & \multirow{4}{*}{3, 1, 2 \(<\) 4} & \multirow{4}{*}{0.01} \\ (seconds) & Series-3 & 4912 & 2.346 & 0.357 & 2.336 & 2.356 & \multirow{4}{*}{5.873 *} & \multirow{4}{*}{3, 1, 2 \(<\) 4} & \multirow{4}{*}{0.01} \\ (sides-4 & 4912 & 2.376 & 0.386 & 2.366 & 2.387 & & & \\ \hline \multirow{4}{*}{Consistency (CV)} & Series-1 & 4912 & 0.107 & 0.102 & 0.104 & 0.110 & \multirow{4}{*}{6.225 *} & \multirow{4}{*}{1 \(<\) 2, 3, 4} & \multirow{4}{*}{0.01} \\ (CV) & Series-2 & 4912 & 0.111 & 0.104 & 0.109 & 0.114 & \multirow{4}{*}{6.225 *} & \multirow{4}{*}{1 \(<\) 2, 3, 4} & \multirow{4}{*}{0.01} \\ (CV) & Series-3 & 4912 & 0.113 & 0.111 & 0.109 & 0.116 & \multirow{4}{*}{6.225 *} & \multirow{4}{*}{1 \(<\) 2, 3, 4} & \multirow{4}{*}{0.01} \\ (sides-4 & 4912 & 0.117 & 0.118 & 0.113 & 0.120 & & & & \\ \hline \hline \multirow{4}{*}{Accuracy (ratio \%)} & Series-1 & 4912 & 0.739 & 0.176 & 0.733 & 0.744 & \multirow{4}{*}{45.112 *} & \multirow{4}{*}{1 \(>\) 3, 2, 4} & \multirow{4}{*}{0.07} \\ (ratio \%) & Series-2 & 4912 & 0.706 & 0.174 & 0.701 & 0.710 & & & \\ \cline{1-1} \cline{5-10}  & Series-3 & 4912 & 0.707 & 0.176 & 0.702 & 0.712 & & & \\ \cline{1-1} \cline{5-10}  & Series-4 & 4912 & 0.703 & 0.180 & 0.698 & 0.708 & & & \\ \hline \hline \end{tabular} \({}^{*}p<0.05\), 1: Series-1, 2: Series-2, 3: Series-3, 4: Series-4. Abbreviations: CV, coefficient of variation; SD, standard deviation.  
\end{table} 
Table 3: Differences in the shot characteristics according to the repetition groups. and speed was \(r=0.362\) (\(p<0.01\)), demonstrating a moderate positive, linear relationship. The shot accuracy had a weak negative linear relationship to both the shot speed and consistency (\(r=-0.018\), \(p<0.05\) and \(r=-0.117\), \(p<0.01\), respectively). In terms of the correlation between the groups' shot characteristics, the shot consistency and speed showed a moderate positive correlation in all areas. Additionally, the shot accuracy and speed demonstrated a negative correlation with both the men and the qualifying group. There were no significant correlations noted in relation to the round sequences. Finally, the shot accuracy and consistency showed a weak negative correlation in all areas.
###### Abstract  
In this study we attempted to verify the hypothesis that the mevalonate pathway affects amyloid beta precursor protein (A\(\beta\)PP) processing and regulates clusterin protein levels. _A\(\beta\)PP_ expression was monitored by green fluorescence (FL) and Western blot (WB). WB showed soluble amyloid protein precursor alpha (sA\(\beta\)PP\(\alpha\)) presence in _A\(\beta\)PP_-wt cells and A\(\beta\) expression in _A\(\beta\)PP_-sw cells. Nerve growth factor (NGF)-differentiated rat neuronal pheochromocytoma PC-12 cells were untreated/treated with statins alone or together with non-sterol isoprenoids. Co-treatment with mevalonate, dolichol, ubiquinol, farnesol, geranylgeraniol, or water-soluble cholesterol demonstrated statin-dependent neurotoxicity resulted from the attenuated activity of mevalonate pathway rather than lower cholesterol level. Atorvastatinatin (50 uM) or simvastatin (50 uM) as well as cholesterol chelator methyl-\(\beta\)-cyclodextrintrim (0.2 mM) diminished cell viability (\(p\) < 0.05) and clusterin levels. Interestingly, co-treatment with mevalonate, dolichol, ubiquinol, farnesol, geranylgeraniol, or water-soluble cholesterol stimulated (\(p\) < 0.05) clusterin expression. Effects of non-sterol isoprenoids, but not water soluble cholesterol (Chol-PEG), were the most significant in mock-transfected cells. Geranylgeraniol (GGOH) overcame atorvastatinatin (ATR)-dependent cytotoxicity. This effect does not seem to be dependent on clusterin, as its level became lower after GGGH. The novelty of these findings is that they show that the mevalonate (MEV) pathway rather than cholesterol itself plays an important role in clusterin expression levels. In mock-transfected, rather than in A\(\beta\)PP-overexpressing cells, GGGH/farnesol (FOH) exerted a protective effect. Thus, protein prenylation with GGGH/FOH might play substantial role in neuronal cell survival.  
Alzheimer's disease; statins; clusterin; cholesterol; mevalonate pathway; isoprenoids; rat neuronal pheochromocytoma PC-12 cells +
Footnote †: journal: Molecular Sciences
# Facial Privacy Protection with Dynamic Multi-User Access Control for Online Photo Platforms
## 4 Proposed System
### System Workflow
#### 4.2.5 Face Decryption by Consumer  
The decryption process for [PERSON], the image consumer, primarily follows similar steps to those performed by [PERSON], the image owner. The difference is in the method of accessing the symmetric decryption key. While [PERSON] has direct ownership of the image and can access the symmetric key directly, [PERSON] does not possess this direct ownership. Instead, [PERSON] uses key shares provided by [PERSON] during the key splitting process to reconstruct the original symmetric key. With the reconstructed key, [PERSON] decrypts the encrypted facial regions in the image to access the protected data. This decryption process for the consumer is illustrated in Figure 6.  
To initiate the face decryption process, [PERSON] sends a request to the server containing the image identifier \(a\) and his password \(pwd\). Similar to the image owner, the password is essential for each decryption request to successfully reveal the facial regions.  
Upon receiving and validating [PERSON]'s request, the server retrieves the face coordinates \(F_{a}\), the encrypted image \(I_{a}^{\text{enc}}\), along with the identifiers for [PERSON] (\(ID_{\text{alloc}}\)) and [PERSON] (\(ID_{\text{bob}}\)) from its storage. This information is then forwarded to the CSP for processing.  
Upon receiving the request from the server, the CSP retrieves the encrypted key shares (\(q_{\text{alloc},a^{\text{enc}}}^{\text{enc}}\), \(q_{\text{(Bob,a)}}^{\text{enc}}\)), [PERSON]'s encrypted private key \(sk_{\text{Bob}}^{\text{enc}}\), along with the initialization vector \(IV_{a}\) used to encrypt the facial regions in the image \(I_{a}^{\text{enc}}\). The CSP checks for the availability of both [PERSON]'s and [PERSON]'s key shares. If either share is missing, the decryption process terminates. If both shares are available, the CSP proceeds with key reconstruction.  
Figure 6: Face decryption by consumer: This figure illustrates the process by which an image consumer, such as [PERSON], decrypts facial regions in a shared image using key shares provided by the image owner, [PERSON].  
To reconstruct the symmetric key for image decryption, the CSP decrypts [PERSON]'s private key \(s^{\text{enc}}_{bbb}\) using [PERSON]'s password \(pwd\) and a salt \(salt_{bbb}\) associated with [PERSON]'s account, yielding \(sk_{bbb}\). Once \(sk_{bbb}\) is obtained, the CSP decrypts the encrypted key shares \(q^{\text{enc}}_{(silice,a)}\) and \(q^{\text{enc}}_{(boda)}\) using \(sk_{bbb}\). The decrypted key shares are then combined using the function \(\mathbf{RECON}(q_{(alice,a)},q_{(bb,a)})\), which uses [PERSON]'s secret sharing scheme to reconstruct the symmetric key \(K^{\prime}\). Using \(K^{\prime}\) and \(IV_{a}\), the CSP decrypts the facial regions in the image \(I^{\text{enc}}_{a}\), producing the original image \(I\). The CSP then returns the processed image to [PERSON] via the server, completing the decryption process.
## 2 Proposed Method
### Convolutional Neural Network  
CNN is a special neural network whose hidden units are only connected to a local receptive field. We trained a simple CNN with the features extracted from the previous step as input to improve the classification accuracy. The weights of the CNN will be randomly initialized to learn the feature space shifts between ImageNet and retinal OCT images. As a result, the features extracted from the CNN can better describe the high-level characteristics of retinal OCT images. The CNN architecture are illustrated in Table 1. The convolutional layer captures the salient features in the local area, and the batch normalization layer can avoid overfitting. More importantly, the max-pooling layer reduces the number of parameters and makes the model easier to train. After the last batch normalization layer, the flatten layer can change the dimensions of the data for the transition of the batch normalization layer and the fully connected layer. The three outputs of the fully connected layer with an activation function soft-max shows the probability that the input B-scan belongs to each category (AMD, DME, and NOR). The CNN can extract information and identify the different characteristics from the training set. Therefore, it can improve the identification accuracy of the proposed method.  
Figure 2: Retinal dataset examples. (**a**) Original B-scans. (**b**) Corresponding preprocessed B-scans.  
Figure 3: Inception V3 with filter dimensions and illustration of the inception layer.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Patch Size/Stride** & **Padding** & **Output Size** \\ \hline Convolution1 & \(3\times 3/1\) & same & \(128\times 8\times 8\) \\ Max Pooling1 & \(2\times 2/2\) & valid & \(128\times 4\times 4\) \\ BatchNormalization1 & & & \(128\times 4\times 4\) \\ Convolution2 & \(3\times 3/1\) & same & \(128\times 4\times 4\) \\ Max Pooling2 & \(2\times 2/2\) & valid & \(128\times 2\times 2\) \\ BatchNormalization2 & & & \(128\times 2\times 2\) \\ Convolution3 & \(3\times 3/1\) & same & \(128\times 2\times 2\) \\ BatchNormalization3 & & & \(128\times 2\times 2\) \\ Flatten & & & \(512\) \\ Dense & & & \(3\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the convolutional neural network (CNN) architecture.
## 3 Results and Discussion
### Measurement Results  
The BSA was tested in free space and on the human body. A network analyzer (Keysight PNA-X N5242A, Keysight, Santa Rosa, CA, USA) was used to measure S\({}_{11}\) after calibration by a Keysight calibration module (NA4691B, Keysight, Santa Rosa, CA, USA). A flat human homogeneous tissue phantom with dimensions of \(200\times 200\times 50\) mm\({}^{3}\) was utilized to assess the performance. An air gap similar to the one assumed in the simulations (5 mm) was maintained between the surface of the phantom and the antenna ground. This air gap is in a practical range and is also required for safety reasons. The constructed device  
Figure 18: Simulated radiation patterns at 2.45 (left side) and 5.6 GHz (right side): (**a**) free space, no bending, (**b**) free space after bending, (**c**) bent on the layered chest, and (**d**) bent on the cylindrical arm. E field: yellow/red (normal/large sizes, left (2.45 band) and right sides (5.6 GHz), and H-field: black/purple) (normal/large sizes, left (2.45 GHz) and right sides (5.6 GHz)).  
was also tested on the human subjects' bodies. The body affects the resonance frequency and radiation properties, but the operating frequency bands were still maintained even in the presence of the human body.