esa-sceva/satcom-chunk-collection huggingface dataset:
# Separation of Albumin from Bovine Serum Applying Ionic-Liquid-Based Aqueous Biphasic Systems
## 2 Materials and Methods
### SDS-PAGE and Circular Dichroism  
The protein profile of bovine serum and of the IL-rich phase after an ultrafiltration (UF) step was evaluated by sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) using an Amersham ECLTM Gel from GE Healthcare Life Sciences. For the analysis of the IL-rich phase an UF step was performed to remove the IL and respective interferences, dissolving the retenate in PBS buffer for the analysis. Samples were mixed with the sample buffer 1:1 (_v:v_), consisting of: 2.5 mL of 0.5 M Tris-HCl pH 6.8, 12.0 mL of glycerol, 4.0 mL of 10% (_w/v_) SDS, 2.0 mg of bromophenol blue and 310 mg of dithiothreitol (DTT); and heated for denaturation at (95 \(\pm\) 1) \({}^{\circ}\)C for 5 min. Electrophoresis was run on polyacrylamide gels 4-20% at 105 V for 90 min. The running buffer used was Tris-Tricine-SDS Buffer 10\(\times\)x (1.0 M Tris, 1.0 M Tricine, 10% SDS, pH 8.3). The gel was stained with Coomassieie Brilliant Blue G-250. A molecular weight full-range marker (VWR) was used as protein standards.  
To evaluate the maintenance of the BSA secondary structure after the extraction step and UF applied to the IL-rich phase, circular dichroism (CD) was applied. The analysis of pure BSA and of the IL-rich phase after the UF step was performed by CD spectroscopy (JASCO-1500), with three spectra recorded consecutively with a 1 mm path length quartz cuvette and the parameters: 100 nm per minute, at 20 \({}^{\circ}\)C cell temperature, response time and bandwidth were 2 s and 0.2 nm, respectively. The baseline was established with a phosphate-buffered saline (PBS) solution. The secondary structure content of BSA was calculated using DichroWeb with the Analysis Program CDSSTR and the reference set 4 (optimized for the range 190-240 nm) [31].
# Mobile Network Performance and Technical Feasibility of LTE-Powered Unmanned Aerial Vehicle
###### Abstract  
Conventional and license-free radio-controlled drone activities are limited to a line-of-sight (LoS) operational range. One of the alternatives to operate the drones beyond the visual line-of-sight (BVLoS) range is replacing the drone wireless communications system from the conventional industrial, scientific, and medical (ISM) radio band to a licensed cellular-connected system. The Long Term Evolution (LTE) technology that has been established for the terrestrial area allows command-and-control and payload communications between drone and ground station in real-time. However, with increasing height above the ground, the radio environment changes, and utilizing terrestrial cellular networks for drone communications may face new challenges. In this regard, this paper aims to develop an LTE-based control system prototype for low altitude small drones and investigate the feasibility and performance of drone cellular connectivity at different altitudes with measuring parameters such as latency, handover, and signal strength. The measurement results have shown that by increasing flight height from ground to 170 m the received signal power and the signal quality levels were reduced by 20 dBm and 10 dB respectively, the downlink data rate decreased to 70%, and latency increased up to 94 ms. It is concluded that although the existing LTE network can provide a minimum requirement for drone cellular connectivity, further improvements are still needed to enhance aerial coverage, eliminate interference, and reduce network latency.  
U  
27 March 2021  
18 April 2021  
**Keywords:** UAV; drone; BVLoS; wireless; cellular; 4G; LTE
# Visual Static Hand Gesture Recognition Using Convolutional Neural Network
## 2 Materials and Methods
### Structure of the CNN  
The motivation for proposing the chosen _CNN_ architecture is the aim to leverage the hierarchical features of hand gestures, exploit translation invariance, reduce dimensionality, and efficiently extract discriminative features. The cascaded layers of convolutional and max-pooling operations in the architecture allow the network to progressively learn and extract increasingly complex features from the input images as a hierarchical feature extraction. The initial convolutional layer with 15 output maps captures low-level visual patterns, while subsequent layers with increasing output maps can learn more abstract and high-level features relevant to hand gesture recognition. This hierarchical feature extraction enables the network to effectively model the intricate details and variations present in hand gestures. The max-pooling layers in the architecture help reduce the spatial dimensions of the feature maps, resulting in a more compact representation of the learned features. By downsampling the feature maps, the max-pooling layers retain the most salient information while discarding less relevant or redundant details. This dimensionality reduction helps to mitigate the impact of background noise, variations in hand pose, and other sources of variability in hand gesture images, making the model more robust and efficient. We resize the input size of images to 32 \(\times\) 32 to reduce overfitting. If we use a bigger size, the model will overfit, as we will show in the results of Section 3.6 by using image sizes of \(64\times 64\) and showing the increase in the overfitting.  
As shown in Figure 1, the _CNN_ model consists of two convolutional layers, followed by a _ReLU_ activation function for each one, and two pooling layers apart from the input and output layers. As shown in Figure 1, the input image of 32 \(\times\) 32 pixels is convolved with 15 filter maps of size \(6\times 6\) to produce 15 output maps of \(29\times 29\) in the first layer. These output maps are operated upon with a _ReLU_ activation function. Downsampling of the output convolutional maps is completed with max-pooling of \(2\times 2\) regions to yield 15 output maps of \(14\times 14\) in layer 2. The 10 output maps of layer 2 are convolved with each of the 30 kernels of size \(3\times 3\) to obtain 30 maps of size \(14\times 14\). These maps are further downsampled by a factor of 2 by max-pooling to produce 30 output maps of size \(7\times 7\) of layer 4. The output maps from layer 4 are concatenated to form a single vector during training and fed to the next layer, which is the fully connected layer. A dropout probability of 0.5 is used to reduce overfitting.
## 2 Preliminaries
### Motivation  
A \((k,n)\)-XVC scheme provides a simple way to greatly increase the contrast of a \((k,n)\)-OVC scheme from Examples 1 and 2. It provides the possibility to reveal secret image on lightweight equipments, such as mobile phone and iPad, which can be used in the transmission of the secret images, security authentication, and access control. A \((k,n)\)-XVC scheme greatly enhances the utility and flexibility of secret image sharing. However, it requires the same pixel expansion as a \((k,n)\)-OVC scheme. [PERSON] [26] proved that the contrast of a \((k,n)\)-XVC scheme is \(2^{k-1}\) times better than that of a \((k,n)\)-OVC scheme if two kind of schemes are built based on the same basis matrix of a \((k,n)\)-OVC scheme. [PERSON]'s specific conclusion is described in Theorem 1.  
**Theorem 1** (Theorem 1 in [26]).: _Basis matrices satisfying the conditions of Definition 1 also satisfy the conditions of Definition 2. The relative difference of the gray levels of the recovered white and black pixels is \(h^{\prime}-l^{\prime}=2^{k-1}(h-l)\), where \(l\) and \(h\) (resp. \(l^{\prime}\) and \(h^{\prime}\)) are the gray levels of recovered white and black pixels for a \((k,n)\)-OVC (resp. a \((k,n)\)-XVC) scheme, respectively._  
[PERSON]'s conclusion gives us such inspiration; we can construct a new \((k,n)\) scheme by replacing \"OR\" operation with \"XOR\" operation directly without changing the basis matrices. Although the pixel expansion of the two schemes is the same, the contrast of a \((k,n)\)-XVC scheme is enhanced \(2^{k-1}\) times than that of a \((k,n)\)-OVC scheme. To solve the problem of pixel expansion in a \((k,n)\)-XVC scheme, we can allow the difference of the gray levels of recovered white and black pixels to be changed among the interval \([1,2^{k-1}]\). To ensure such revealing condition, the corresponding new basis matrices can take any \(m-2^{k-1}+1\) columns to any \(m\) columns in the original basis matrices of a \((k,n)\)-OVC scheme. This process allows pixel expansion to be changed among a lightly large interval, and it can ensure each pixel being exactly recovered with the same average contrast no matter it takes any pixel expansion.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pixel** & **Shadow 1** & **Shadow 2** & **XOR** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The encoding and decoding process of the shadows in a (2, 2)-XVC scheme.  
The least pixel expansion \(m-2^{k-1}m\alpha+1\) is much smaller than that of the previous schemes. Our scheme can be easily implemented on any conventional \((k,n)\)-OVC scheme.
## 3 Numerical Calculation Application and Experimental Setup
### Steady State CFD Model Setup  
#### 3.2.1 Mesh Generation and Turbulence Model  
Hexahedral dominated grids for CFD analysis were generated by the meshing module of ANSYS Workbench. Meanwhile, transition regions like casing tongue were processed by mesh refinement. For near-wall grids, the allowable maximum \(y^{+}\) was set as 50 by with the meshing adaptive tool of Fluent after the first CFD analysis (trial computation) [30]. Therefore, the mesh refinement function was initiated and grids in the near-wall region were re-meshed based on the flow distribution results.  
In this study, the SST \(k\)-\(\omega\) turbulence model was chosen, whose parameters were listed in Table 2.  
Figure 4: Middle section plane view of the hydraulic model.  
Figure 5: Three-dimensional structure and parameters of main shaft.  
Figure 3: Three-dimensional structure of the research object: 1—Pump body; 2—Outlet; 3—Base; 4—Coupler; 5—Rolling bearing; 6—Mechanical seal; 7—Main shaft; 8—Pump cover; 9—Sealing ring; 10—Impeller.
## 3 Adaptive Sliding Mode Control (ASMC) Design
### SMC Design  
The system position tracking error is defined as  
\[e=y-y_{d} \tag{5}\]  
For the FTS system that performs a periodic motion in the iterative domain [0, T], the input trajectory \(y_{d}\) is recorded as the expected position function, and \(y\) is the actual position trajectory.  
Based on the position tracking error, the PID sliding mode surface [27] is defined as  
\[\sigma=\dot{e}+\lambda_{1}e+\lambda_{2}\int ed\tau \tag{6}\]  
where the constant \(\lambda_{1}\)\(\lambda_{2}\) is strictly positive. \(\tau\) is the integral variable. The integral term is used to improve the transient response and reduce the steady-state error in the sliding surface compared with the traditional sliding mode surface. When the system state approaches the sliding mode surface, \(\dot{e}+\lambda_{1}e\) can ensure good dynamic performance. In addition, \(\lambda_{2}\int ed\tau\) can improve the stability of the control method.  
The control law \(u\) is selected as follows  
\[u=u_{eq}+u_{saw} \tag{7}\]  
Let Equation (4) be zero and ignore the disturbance, the equivalent control \(u_{eq}\) is derived as follows [28].  
\[u_{eq}=\frac{1}{b}\big{[}a_{1}x_{1}+a_{2}x_{2}+\ddot{y}_{d}-\lambda_{1}(x_{2}- \dot{y}_{d})-\lambda_{2}(x_{1}-y_{d})\big{]} \tag{8}\]  
Switching control input \(u_{sw}\) for tracking system uncertainty is chosen as  
\[u_{sw}=\frac{1}{b}(-\eta sgn(\sigma)-k_{n}\sigma) \tag{9}\]  
where \(\eta>f_{0}\), \(k_{n}>0\). \(f_{0}\) is the upper bound of disturbance, thus ensuring the controller's suppression of system disturbances. \(\eta\) and \(k_{n}\) are the adjustable switching gains of the sliding mode controller, which are used to change the error convergence speed of the approaching phase and sliding phase of the SMC. It can make the initial point far away from the sliding mode surface converge quickly in the process of approaching the sliding mode surface, and the speed is small when approaching the sliding mode surface, which not only ensures the convergence speed of the algorithm, but also weakens the control torque buffeting to ensure system control performance.
## 2 Theoretical Background
It is evident from Equation (3) that the equilibrium extraction time can be reduced by (i) reducing the coating thickness of the sorbent, (ii) increasing the primary contact surface area of the extracting media (smaller B value), and (iii) increasing the analyte diffusion in the sample matrix by applying external energetics such as magnetic stirring, sonication, orbital shaking, etc. The rate of extraction in the FPSE membrane, like other microextraction techniques, is not linear. The extraction proceeds very fast in the beginning of the extraction process, and the extraction rate steadily decreases as the extraction progresses towards the equilibrium. The initial rate of extraction (\(\frac{\text{dn}}{\text{dt}}\)) is directly proportional to the surface area of the extracting phase A, as shown in Equation (4). \[\frac{\text{dn}}{\text{dt}}=\left(\frac{\text{Ds }\Delta}{\delta}\right)\text{. Co} \tag{4}\]  
As such, if one is to increase the sensitivity of the microextraction technique, the volume of the extracting sorbent must be increased. In the same time, in order to reduce the extraction equilibrium time, the primary contact surface area of the extraction device must be augmented. Fabric phase sorptive extraction has eloquently exploited both the thermodynamic and the kinetic criteria of the microextraction process. It utilizes sorbent loading approximately 40,000\(\times\) times higher than SPME fiber and 150\(\times\) times higher than stir bar. Regarding primary contact surface area, the FPSE membrane (in its typical 2.5 cm \(\times\) 2.0 membrane size) is 50-100 times higher than SPME fiber and 10 times higher than stir bar. In addition, the sponge-like porous architecture of sol-gel-derived hybrid inorganic-organic sorbent is highly favorable for achieving a fast extraction equilibrium compared to their classical counterparts: highly viscous pristine organic/inorganic polymers traditionally used in most of the microextraction techniques.
# SemABC: Semantic-Guided Adaptive Bias Calibration for Generative Zero-Shot Point Cloud Segmentation
## 4 Experiments
### Ablation Studies
#### 4.4.4 Hyperparameters  
To verify the impact of hyperparameter values on network performance, we conduct experiments on the S3 DIS and ScanNet datasets for the hyperparameter \(\beta\) used to control the confidence weight in Equation (10) and the hyperparameter \(\lambda\) used to balance the two branches in Equation (13), as shown in Figure 5. Figure 5a demonstrates that on the S3 DIS dataset, setting the hyperparameter \(\beta\) to 1.2 results in the optimal HmIoU value. Figure 5b reveals that on the ScanNet dataset, the best HmIoU value is achieved when the hyperparameter \(\lambda\) is 0.7. These findings indicate that during network training, the main segmentation branch plays a more important role than the auxiliary branch.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**ScanNet**} & \multicolumn{4}{c}{**S3 DIS**} \\ \cline{2-9}
**Method** & \multicolumn{3}{c}{**mIoU**} & \multicolumn{3}{c}{**mIoU**} & \multicolumn{3}{c}{**mIoU**} & \multirow{2}{*}{**HmIoU**} \\ \cline{2-9}  & \(\mathbf{C}^{\mathbf{S}}\) & \(\mathbf{C}^{\mathbf{U}}\) & & & \(\mathbf{All}\) & & \(\mathbf{C}^{\mathbf{S}}\) & \(\mathbf{C}^{\mathbf{U}}\) & \(\mathbf{All}\) \\ \hline Baseline & 34.5 & 14.3 & 30.4 & 20.2 & 58.9 & 9.7 & 43.8 & 16.7 \\ Max & 34.8 & 13.3 & 30.5 & 19.3 & 67.7 & 9.8 & 49.9 & 17.1 \\ Entropy & 34.4 & 14.1 & 30.3 & 20.0 & 68.1 & 9.3 & 50.0 & 16.4 \\ Energy & 34.6 & 14.1 & 30.5 & 20.0 & 67.3 & 9.5 & 49.5 & 16.7 \\ Variance & 34.8 & 13.8 & 30.6 & 19.8 & 66.9 & 9.8 & 49.3 & 17.1 \\ Kurtosis & **34.9** & **14.9** & **30.9** & **20.9** & **68.1** & **10.4** & **50.3** & **18.0** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The influence of common uncertainty algorithms on segmentation performance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Channel**} & \multirow{2}{*}{**Spatial**} & \multicolumn{3}{c}{**mIoU**} & \multirow{2}{*}{**HmIoU**} \\ \cline{3-4} \cline{6-6}  & & \(\mathbf{C}^{\mathbf{S}}\) & & \(\mathbf{C}^{\mathbf{U}}\) & & \\ \hline \multirow{2}{*}{\(\mathbf{\times}\)} & \multirow{2}{*}{\(\mathbf{\times}\)} & 32.8 & 13.9 & 29.0 & 19.5 \\  & & 32.0 & 13.8 & 28.3 & 19.3 \\ \multirow{2}{*}{\(\mathbf{\times}\)} & \multirow{2}{*}{\(\mathbf{\times}\)} & 31.8 & 14.4 & 28.4 & 19.9 \\  & & **34.9** & **14.9** & **30.9** & **20.9** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experiments of the VSFM on the ScanNet dataset, including Channel and Spatial Attention.
# Research on Optimization of Target Positioning Error Based on Unmanned Aerial Vehicle Platform
## 3 Error Transmission Theory
### Error Transfer from Camera Coordinate System to Airframe Coordinate System  
For the camera system coordinates \(A_{c}=(x_{c},y_{c},z_{c})^{T}\), \(A_{c}\sim N(\mu_{c},P_{c})\), the pod heading angle \(\alpha\), and the pitch angle \(\beta\) error \(\Omega=(\alpha,\beta,0)^{T}\sim N(\mu_{\Omega},P_{\Omega})\), where \(\mu_{\Omega}=(\overline{\alpha},\overline{\beta},0)^{T}\), the diagonal matrix \(P_{\Omega}=diag(\rho_{\alpha}^{2},\rho_{\beta}^{2},0)\) can be obtained by transforming the fuselage system coordinates \(A_{h}=(x_{h},y_{h},z_{h})^{T}\).  
The coordinates are transformed into  
\[\begin{bmatrix}x_{h}\\ y_{h}\\ z_{h}\end{bmatrix}=\begin{bmatrix}-\sin(\alpha)&\cos(\alpha)&0\\ \cos(\alpha)&\sin(\alpha)&0\\ 0&0&1\end{bmatrix}^{-1}\begin{bmatrix}1&0&0\\ 0&\cos(\beta)&\sin(\beta)\\ 0&-\sin(\beta)&\cos(\beta)\end{bmatrix}^{-1}\begin{bmatrix}x_{c}\\ y_{c}\\ z_{c}\end{bmatrix}\Bigg{|}_{\Omega=[\overline{x},\overline{\beta},0]} \tag{4}\]  
\[P_{h}=\begin{pmatrix}J_{c}^{h}\\ \end{pmatrix}P_{\Omega}\begin{pmatrix}J_{c}^{h}\\ \end{pmatrix}^{T} \tag{5}\]  
where  
\[J_{c}^{h}=\begin{bmatrix}\frac{2\alpha_{x}}{\beta\alpha}&\frac{2\alpha_{y}}{ \beta\beta}&0\\ \frac{\alpha_{y}}{\alpha\alpha}&\frac{2\alpha_{y}}{\beta\beta}&0\\ \frac{\alpha_{z}}{\beta\alpha}&\frac{2\alpha_{z}}{\beta\beta}&0\end{bmatrix} \tag{6}\]
## 3 Results and Discussion
### The UWOC Application
To demonstrate image transmission via the UWOC system, a grayscale image of 256 \(\times\) 256 pixels and a RGB image of 128 \(\times\) 128 pixels were first generated using Python, with a total of 65,536 and 49,152 pixels, respectively. Figure 7 shows the changes in the images after transmission at a data rate of 7.14 Mbps and 8.33 Mbps for the grayscale and RGB images, respectively. From these images, one can easily perceive that the errors in the images received by the silicon PD are mainly the difference in gray value and color, while that of the perovskite PD are pixel offset and deformation. This difference in image distortion is attributed to different error mechanisms for silicon and perovskite PDs. The bit error rate is typically not suitable for assessing image quality due to its disregard for visual perception. To better assess the fidelity of the images after transmission, mean squared error (MSE) and structural similarity index (SSIM) are introduced. The MSE takes into account the difference in each pixel to reflect the pixel divergence of an image, and the SSIM considers contrast, brightness, and structure of the image, providing a more accurate assessment of image fidelity, especially in contexts involving human perception. The calculated MSE and SSIM are plotted in Figure 8. Similar to the text transmission, the perovskite PD exhibits better fidelity than the silicon PD before reaching a limit. Once the perovskite PD operated above the data rate threshold, the image fidelity decreased rapidly and was worse than that of the silicon PD, which is due to the data losses of the perovskite PD leading to entire data misalignment. For the perovskite PD, the errors in the pixel are relatively few, and the grayscale and color information in the images are of good fidelity. This kind of error from packet loss can potentially be avoided by using automatic repeat request (ARQ) and forward error correction (FEC) coding methods [36; 37]. Conversely, the images from the silicon PD mainly have the most errors in the pixel itself, with brighter and noticeable white spots and no obvious region misalignment. This can be ascribed to the misinterpretation of the \"0\" (corresponding to a high voltage level in the waveform, where the light source is on) due to the time span of the high voltage gradually reducing  
Figure 6: The waveform of the transient response of (**a**) the silicon PD and (**b**) the perovskite PDs; (**c**) the background photocurrent of the PDs under different intensities of environment illumination; (**d**) noise current of silicon and perovskite PDs under dark and 1 lux white light illumination. with increasing data rate. When the \"0\" of a pixel is misinterpreted as \"1\" in every case, the data become \"255\", resulting in a white spot in the image.
# An Accelerated Maximum Flow Algorithm with Prediction Enhancement in Dynamic LEO Networks
## 3 System Model and Problem Formulation
### Capability Constraints of Satellites  
For any satellite node \(r_{i},i\in[1,N]\), its processing capacity, denoted as \(\mathcal{C}_{i}\), defines the maximum amount of data it can handle within a given time period. This capacity is determined by several factors, including the inherent processing capacity \(f_{i}\) of the node and its available energy reserves \(E_{i}\) for data processing and forwarding. The energy consumption per unit of processing \(p\) further constrains the amount of data the satellite can handle, which is mathematically captured by the following equation:  
\[\mathcal{C}_{i}=(min(f_{i},\frac{E_{i}}{p}))\times T^{\zeta} \tag{8}\]  
where \(T^{\zeta}\) accounts for the temporal dynamics and scaling effects associated with data processing over the time period, \(T\). Therefore, the data volume processed on the satellite \(r_{i}\) during the time horizon cannot exceed \(\mathcal{C}_{i}\), considering its processing capacity and available energy.  
As discussed in Section 3.1, the processing capacity constraints, combined with visibility constraints define the available communication links and data transmission paths across the satellite network. We abstract the transmission paths across different time slots, taking into account both visibility and the satellite's maximum processing capacity, as illustrated in Figure 1. The time-varying constraints and resulting transmission capacities are presented in Figure 2, visually capturing the network's operational limits over time.  
Figure 2: Transmission paths over time slots and satellite processing capacity constraints as represented in the modeled satellite network in Figure 1.