esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Normalization  
Since size, pixel-level noise, symbols, bright text, and other characteristics of the images vary, we normalized the images of the Kvasir-Capsule dataset. By normalizing the data in a variety of ways, the effect of various pixel intensities can be defined. From the pixel intensity that is recorded as \(P_{I}\), the normalized datum \(P_{I}\)* is obtained using the normalization method. For each of the three primary colors, the value of this pixel ranges from 0 to 255. To achieve normalization, the value of each pixel was divided by 255. The experiment's maximum and minimum values served as the foundation for normalization, as shown in Equation (1).  
\[P_{I}^{*}=(P_{Il}-Min_{old})\frac{Max_{new}-Min_{new}}{Max_{old}-Min_{old}}+ Min_{new},\;l\in[0,n] \tag{1}\]  
Scaling  
In CV, resizing an image is an important step before processing it. Primarily, small images allow DL models to train more quickly. The neural network must learn from four times as many pixels in a larger input image, which increases the architecture's training time. Therefore, images are frequently resized to a smaller dimension when training DL models so that mini-batch learning can continue, and the computed limitations can be maintained [25]. Common DL models like DenseNet-121, ResNet-50, MobileNetV2, and EfficientNets, which were pre-trained on ImageNet, consistently benefit from this concept. In this research, we scaled the images of the Kvasir-Capsule dataset, as shown in Table 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attribute** & **Value** \\ \hline horizontal\_flip & True \\ rotation\_range & 20 \\ width\_shift\_range & 0.2 \\ height\_shift\_range & 0.2 \\ zoom\_range & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The used attributes in the data augmentation techniques and their values.  
Hence, the images were resized to a fixed 224-by-224 resolution.
# Detachment Energy Evaluation in Nano-Particle Cleaning Using Lateral Force Microscopy
## 4 Theoretical Calculation of Interaction Energy
### Potential Energy of van der Waals Interaction  
The van der Waals force generated between atoms and molecules is generally attractive in nature due to the motion of electrons (Figure 6). For a deformed particle of a diameter \(D\) on a flat surface, the interaction energy \(E_{\text{VdW}}\) is given by the following [36]:  
\[E_{\text{VdW}}=-\frac{AD}{12z_{\text{sep}}}\left[1+\frac{(a_{\text{def}})^{2}} {Dz_{\text{sep}}}\right] \tag{5}\]  
where \(A\) is the Hamaker constant, \(z_{\text{sep}}\) is the separation distance between the particle and the substrate, and \(a_{\text{def}}\) is the contact radius. The typical calculated results are shown in Table 3.  
\begin{table}
\begin{tabular}{c|c c c c} \hline
**Diameter [nm]** & \(E_{\text{S-bond(total)}}\) [f]** & \(E_{\text{H-bond(total)}}\) [f] & \(S_{\text{area}}\) [nm\({}^{2}\)] & \(a_{\text{def}}\) [nm] \\ \hline
10 & 0.13 & 0.01 & 38 & 2.2 \\
100 & 1.92 & 0.09 & 567 & 10.4 \\
1000 & 32.5 & 1.53 & 9595 & 48.3 \\ \hline \end{tabular}
\end{table}
Table 2: Typical calculated results of energy of siloxane bond and hydrogen bond.
# A Novel Multipath Transmission Scheme for Information-Centric Networking
## 2 Related Work  
Multipath transmission techniques have been extensively studied due to their improvement in transmission robustness and efficiency. Since the multipath transmission scheme we propose is applied in an IP-compatible ICN architecture, we present a comprehensive study on the multipath transmission mechanism of IP networks and ICN, respectively, in this section.
# Remote Sensing for International Peace and Security: Its Role and Implications
## 3 Results
### Remote Sensing and Human Rights
These events and attributes suggest that non-governmental actors have been active in using remote sensing techniques to identify and report on human rights violations. There are often three common components of humanitarian remote sensing projects: (1) they usually involve a combination of different actors, including the provider of images, (2) they consist of imagery analysis experts partnering with an advocacy organization, and, (3) typically, the funder is directly involved with the project [96]. Looking at the collaboration networks among various actors and agencies, the Satellite Sentinel Project (SSP) is an important example [97]. The project is funded by the American actor [PERSON]'s organization Not On Our Watch [98]. The goal of the project is to prevent the return of all-out civil war between South Sudan and Sudan. In the SSP program, a non-governmental organization is working with a private donor with aid through remote sensing for assessing, deterring, and documenting human rights violations [97]. For SSP to accomplish this goal, they have partnered with the imagery provider DigitalGlobe and their imagery analysis experts. DigitalGlobe's high-resolution commercial satellites, known as QuickBird, WorldView-1, and WorldView-2, pass over Sudan and South Sudan in order to understand the impact on civilians. After collecting the satellite images, analysts at DigitalGlobe work with the rights-based group called the Enough Project to analyze imagery and ground sourcing information. If experts detect human rights abuses, then the project releases a report to the press and policymakers to generate a rapid response when it comes to human rights abuses and human security concerns [96]. An example of the SSP using remote sensing to document atrocities is the Mass Atrocity Alert report called _Cover-Up: New Evidence of Three Mass Graves in South Kordofan_. With the analysis of the imagery and eyewitnesses who admitted to seeing the government of Sudan's militia groups dumping white body bags in the area, SSP analysts were able to conclude that the suspected areas were the locations of mass graves. After concluding that these areas were, in fact, efforts by the government of Sudan to cover up large-scale burial operations, SSP immediately sent out _Mass Atrocity Alerts_ in hopes of alerting the press and policymakers of the human rights abuses happening in the area [97]. According to [PERSON] et al. [96], the Satellite Sentinel Project [97] is one of the few non-governmental organizations that can afford the high cost of near-real-time imagery from high-resolution commercial satellites. It is not surprising that most human rights organizations and advocacy groups are unable to use remote sensing techniques to uncover and report on mass atrocities in locations where international crimes like genocide and other gross human rights violations are taking place [96].
## 4 Experiment
### Homography Estimation Experiment  
Achieving high-precision feature matching does not necessarily ensure the accurate recovery of the geometric relationship (e.g., relative pose) between two images. In addition to matching accuracy, the number of feature points and their spatial distribution across the image are also critical factors. To further assess the applicability of the proposed method for geometric estimation tasks, a homography estimation experiment was conducted.
# Correction Strategy of Online Midcourse Guidance for High-Speed Gliding Target Interceptor
## 3 Design of Online Dynamic Correction Strategy for Interceptor
### Modeling of the Interceptor-Interceptable Area  
The interceptor-interceptable area (IIA) is the maximum maneuverable range of interceptors in the terminal guidance stage. When the starting time of the interceptor seeker is \(t_{f0}\), the maximum working time of the terminal guidance is \(t_{max}\), the steady thrust of the engine is \(P\), the specific impulse is \(I_{g}\), and the mass of the interceptor missile at the starting time of the seeker is \(m_{f0}\), so the mass change rate is  
\[\dot{m}=-\frac{P}{I_{g}g} \tag{23}\]  
at a certain instant \(t\) in the terminal guidance stage, the lateral maneuvering acceleration of the interceptor is  
\[a_{max}(t)=\frac{P}{m_{f0}+\dot{m}t}=\frac{P}{m_{f0}-\frac{P}{I_{g}g}t} \tag{24}\]  
if the interceptor maneuvers with the maximum maneuvering acceleration, the lateral speed and the maximum lateral maneuvering displacement of the interceptor at instant \(t\) are  
\[v(t)=\int_{t_{f0}}^{t}a_{max}(\tau)d\tau \tag{25}\]  
\[L_{m}=\int_{t_{f0}}^{t}\int_{t_{f0}}^{t}a_{max}(\tau)d\tau \tag{26}\]  
the interception instant is denoted as \(t_{ff}\). If \(t_{ff}-t_{f0}\leq t_{max}\), the maximum lateral displacement of the interceptor perpendicular to the direction of the initial velocity vector is  
\[S_{max}=\int_{0}^{t_{ff}-t_{f0}}\int_{0}^{t_{ff}-t_{f0}}a_{max}(\tau)d\tau dt \tag{27}\]  
if \(t_{ff}-t_{f0}>t_{max}\), the maximum lateral displacement perpendicular to the initial velocity vector is  
\[S_{max}=\int_{0}^{t_{max}}\int_{0}^{t_{max}}a_{max}(\tau)d\tau dt \tag{28}\]  
therefore, the IIA of the interceptor can be expressed as  
\[M=\{\left(x,y,z\right)||x-x_{d}|=0,|y-y_{d}|\leq S_{max},|z-z_{d}|\leq S_{max}\} \tag{29}\]  
where \(x_{d},y_{d},z_{d}\) are the coordinates of the PIP point in the East-North-Up coordinate system. The IIA is shown in Figure 6.
# Lightweight End-to-End Speech Enhancement Generative Adversarial Network Using Sinc Convolutions
## 3 Speech Enhancement GANs  
Given a dataset \(\mathcal{X}=\{(\mathbf{x_{1}^{*}},\mathbf{\tilde{x}_{1}}),(\mathbf{x_{2}^{*}},\mathbf{\tilde {x}_{2}}),\cdots,(\mathbf{x_{N}^{*}},\mathbf{\tilde{x}_{N}})\}\) consisting of \(N\) pairs of raw signals--clean speech signal \(\mathbf{x}^{*}\) and noisy speech signal \(\mathbf{\tilde{x}}\), speech enhancement aims to find a mapping \(f_{\theta}(\mathbf{\tilde{x}}):\mathbf{\tilde{x}}\rightarrow\mathbf{\tilde{x}}\) to transform the raw noisy signal \(\mathbf{\tilde{x}}\) to the enhanced signal \(\mathbf{\tilde{x}}\). \(\theta\) contains the parameters of the enhancement network.  
Conforming to GAN design [18], the generator learns an effective mapping that can imitate the real data distribution to generate novel samples related to those of the training set, i.e., \(\mathbf{\hat{x}}=G(\mathbf{\tilde{x}})\). In contrast, the discriminator plays the role of a classifier which distinguishes real samples, coming from the dataset that the generator is imitating, from fake samples, made up by the generator. The discriminator guides \(\theta\) towards the distribution of clean speech signals, by classifying \((\mathbf{x}^{*},\mathbf{\tilde{x}})\) as real and \((\mathbf{\hat{x}},\mathbf{\tilde{x}})\) as fake. Eventually, the generator learns to produce enhanced signals \(\mathbf{\hat{x}}\) good enough to fool the discriminator such that the discriminator classifies \((\mathbf{\hat{x}},\mathbf{\tilde{x}})\) as real. The objective function of SEGAN reads  
\[\begin{split}\underset{\text{Dis}}{\text{min}}\ \mathcal{L}(\text{Dis})=& \frac{1}{2}\mathbb{E}_{\mathbf{x}^{*},\mathbf{\tilde{x}}\sim p_{data}(\mathbf{x}^{*}, \mathbf{\tilde{x}})}[\text{Dis}(\mathbf{x}^{*},\mathbf{\tilde{x}})-1]^{2}+\\ &\frac{1}{2}\mathbb{E}_{\mathbf{z}\sim p_{\mathbf{z}}(\mathbf{z}),\mathbf{\tilde {x}}\sim p_{data}(\mathbf{\tilde{x}})}[\text{Dis}(\text{Gen}(\mathbf{z},\mathbf{\tilde{x}} ),\mathbf{\tilde{x}})]^{2},\end{split} \tag{1}\]  
\[\begin{split}\underset{\text{Gen}}{\text{min}}\ \mathcal{L}(\text{Gen})=& \frac{1}{2}\mathbb{E}_{\mathbf{z}\sim p_{\mathbf{z}}(\mathbf{z}),\mathbf{\tilde{x}} \sim p_{data}(\mathbf{\tilde{x}})}[\text{Dis}(\text{Gen}(\mathbf{z},\mathbf{\tilde{x}}), \mathbf{\tilde{x}})-1]^{2}\\ &+\lambda\|\text{Gen}(\mathbf{z},\mathbf{\tilde{x}})-\mathbf{x}^{*}\|_{1}, \end{split} \tag{2}\]  
\(\text{Dis}(\cdot)\) is the discriminator module, \(\text{Gen}(\cdot)\) is the generator module, and \(\mathbf{z}\) denotes a latent variable. Inspired by the effectiveness of the \(L_{1}\) norm in the image manipulation domain [32; 33], it is added to the generator loss function to encourage the generator to gain more fine-grained and realistic results. The influence of the \(L_{1}\) norm is regulated by the hyper-parameter \(\lambda\). [PERSON] et al. [19] set \(\lambda=100\) empirically, and hence we take over this value in all experiments.
## 5 Discussion
### Performance Analysis
Furthermore, a real-life test is needed with multiple users, as well as a method to predict how many resources are needed with respect to the users. This is a topic for future work, resulting in a feature added to the deployment platform to advise the users regarding the sizing of the cluster, in relationship with the maximum number of users that are expected.
## 2 Formulation of Optimal Scheduling of Wind-Hydro-Thermal System  
In this section, the optimal scheduling problem of the wind-hydro-thermal system with the fixed-head short-term model of a hydropower plant is mathematically expressed considering the objective function and constraints. A typical wind-hydro-thermal system is shown in Figure 1. From the figure, \(N_{h}\) hydropower plants, \(N_{t}\) thermal power plants, and \(N_{w}\) turbines in a wind farm are generating and supplying electricity to loads via different buses. The purpose of the system is to minimize the total electricity generation cost of \(N_{t}\) thermal power plants, considering the available water in reservoirs and the intermittent nature of wind power. The cost of generated power by hydropower plants and the wind farm is neglected, but all constraints from the plants are supervised. The objective function and all constraints can be mathematically formulated as follows:
# Fault Location and Restoration of Microgrids via Particle Swarm Optimization
## 3 Derivation of Fault Location Approach  
The fault location approach proposed in this study is described in detail. The first step of the proposed method is to calculate the pre-fault and fault bus voltages. Therefore, a fast and systematic power flow method should be developed to avoid rebuilding \(Z_{Bus}\) due to the different network topologies before and after the occurrence of a fault. Consequently, graph theory is applied to develop the bus impedance matrix for the power flow solution and fault current calculation. Finally, the proposed voltage error matrix (**VEM**) and particle swarm optimization (PSO) are used to determine the fault location. The detailed theory of the proposed method is described here.
# A Variational Neural Network Based on Algorithm Unfolding for Image Blind Deblurring
## 4 Experiments and Conclusions
### Ablation Studies
#### 4.4.2 Effectiveness of the \(L_{0}\) Regularizer  
Two experiments were first conducted to evaluate the effectiveness of the \(L_{0}\) regularizer. As shown in (e)-(g) of Table 5, significant performance improvement was observed after the \(L_{0}\) regularizer was used during training. Simultaneously, it is found that the introduction of the \(L_{0}\) regularizer accelerates the convergence of the model, and the blur kernel can be estimated more quickly and accurately. To validate its effectiveness, two sets of training experiments were conducted, and their performance was compared. The experimental results are shown in Figure 7. Among them, Figure 7a and 7b, respectively, show the change in MSE of the recovered images and the SSIM of the estimated blur kernel during training. From these graphs, it is evident that the incorporation of the \(L_{0}\) regularizer results in faster convergence of the model and more precise estimation of the blur kernel.  
Figure 8 shows an example of blur kernel with an \(L_{0}\) regularizer and without any regularizer. It can be observed that the estimated blur kernel after adding the regularizer is more accurate and clearer. Adding the \(L_{0}\) regularizer improves model performance by setting irrelevant or redundant feature weights to zero during optimization. This reduces model complexity, lowers the risk of overfitting, enhances generalization, and decreases training computational cost.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \(\mathbf{L_{0}RR}\) & \(\mathbf{SAR}\) & \(\mathbf{CAR}\) & \(\mathbf{Iters}\) & \(\mathbf{PSNR(dB)}\uparrow\) & \(\mathbf{SSIM}\uparrow\) & \(\mathbf{MS}\)-\(\mathbf{SSIM}\uparrow\) & \(\mathbf{Time(s)}\downarrow\) \\ \hline (a) & \(-\) & \(-\) & \(-\) & \(1\) & \(24.9802\) & \(0.8517\) & \(0.9483\) & \(0.69\) \\ (b) & \(-\) & \(-\) & \(-\) & \(2\) & \(26.2611\) & \(0.8644\) & \(0.9521\) & \(0.71\) \\ (c) & \(-\) & \(-\) & \(-\) & \(4\) & \(27.8909\) & \(0.8720\) & \(0.9617\) & \(0.80\) \\ (d) & \(-\) & \(-\) & \(-\) & \(6\) & \(28.7649\) & \(0.8783\) & \(0.9682\) & \(0.86\) \\ (e) & \(-\) & \(-\) & \(-\) & \(8\) & \(29.3473\) & \(0.8801\) & \(0.9722\) & \(0.87\) \\ (f) & \(-\) & \(-\) & \(-\) & \(10\) & \(29.3506\) & \(0.8819\) & \(0.9735\) & \(0.91\) \\ (g) & \(\surd\) & \(-\) & \(-\) & \(8\) & \(29.3514\) & \(0.8814\) & \(0.9726\) & \(0.68\) \\ (h) & \(\surd\) & \(\surd\) & \(-\) & \(8\) & \(29.3571\) & \(0.8819\) & \(0.9738\) & \(0.67\) \\ (i) & \(\surd\) & \(-\) & \(\surd\) & \(8\) & \(29.3598\) & \(0.8821\) & \(0.9731\) & \(0.65\) \\ (j) & \(\surd\) & \(\surd\) & \(\surd\) & \(8\) & \(\mathbf{29.3616}\) & \(\mathbf{0.8827}\) & \(\mathbf{0.9741}\) & \(\mathbf{0.64}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Quantitative analysis of ablation studies.
## 2 Preliminaries
### Aczel-Alsina \(t\)-Norm & \(t\)-Conorm  
**Definition 4**.: _[_19_]__, A function \(Z:[0,\ 1]^{2}\to[0,\ 1]\) is called TN if for \(r,\ s,\ p\ e[0,\ 1]\), Z satisfy the following properties of Symmetry \(Z(r,\ s)=T(s,\ r)\); Monotonicity \(Z(r,\ s)\leq Z(s,\ p)\) if \(s\leq p\); Associativity \(Z(r,\ Z(s,\ p))=Z(Z(r,\ s),\ p)\); and one identity \(Z(r,\ 1)=r\)._  
**Examples 1**.: _A few examples of TN, such as Product of TN, is \(Z_{P}(r,\ s)=r.s\); Minimum TN is \(Z_{M}(r,\ s)=min(r.s)\); Lukasiewicz TN is \(Z_{L}(r,\ s)-min(r+s-1,\ 0)\); and Drastic TN is given by:_  
\[Z_{D}=(r,\ s)=\left\{\begin{array}{ll}r&if\quad s=1\\ s&if\quad r=1\ \ \ \forall\ r,\ s\in[0,1].\\ 0&\text{otherwise}\end{array}\right.\]  
**Definition 5**.: _[_37_]_ _A function \(H:[0,\ 1]^{2}\to[0,\ 1]\) is called TCN if \(\forall\ r,\ s,\ p\ e[0,\ 1]\), \(H\) satisfy the following properties of Symmetry \(H(r,\ s)=H(s,\ r)\); Monotonicity \(H(r,\ s)\leq H(s,\ p)\) if \(s\leq p\); Associativity \(H(r,\ H(s,\ p))=H(H(r,\ s),\ p)\); and null identity \(H(r,\ 0)=r\)._  
**Examples 2**.: _Few examples of TN, such as a probable sum of TCN \(H_{P}(r,\ s)=r+s-r.s\); Maximum TCN \(H_{M}(r,\ s)=max(r.s)\); Lukasiewicz TCN \(H_{L}(r,\ s)-min(r+s-1)\); and Drastic TCN is given by:_  
\[H_{D}=(r,\ s)=\left\{\begin{array}{ll}r&if\quad s=0\\ s&if\quad r=0\ \ \forall\ r,\ s\in[0,1].\\ 1&\text{otherwise}\end{array}\right.\]  
Ref. [37], When \(H\) is TCN and \(T\) is TN then \(H(r,s)\geq\max(r,s)\) and \(H(r,s)\geq\min(r,s)\forall\ r,s\in[0,\ 1]\), respectively.  
**Definition 6**.: _[PERSON] et al. [18; 38] proposed these TNs and TCNs classes for functional equations in the early 1980s._  
The AATN is given by \(\left(Z_{A}^{\lambda}\ \right)=\left\{\begin{array}{rl}Z_{D}(r,\ s)&if\ \lambda=0\\ min(r,\ s)&if\ \lambda=\infty\\ e^{-\left(\left(-\ln\left(r\right)\right)^{\lambda}+\left(-\ln\left(s\right) \right)^{\lambda}\right)^{1/\lambda}}&\text{otherwise}\end{array}\right.\)The AATCN is given by \(\left(H_{A}^{\lambda}\right)=\left\{\begin{array}{c}H_{D}(r,\,s)\,\,if\,\,\lambda= 0\\ max(r,\,s)\,\,if\,\,\lambda=\infty\\ e^{-\left(\left(-\ln\left(1-s\right)\right)^{\lambda}+\left(-\ln\left(1-s \right)\right)^{\lambda}\right)^{1/\lambda}}\text{ otherwise}\end{array}\right.\)  
Cases: \(Z_{A}^{0}=Z_{D}\), \(Z_{A}^{1}=Z_{P}\), \(Z_{A}^{\infty}=min\), \(H_{A}^{0}=H_{D}\), \(H_{A}^{1}=H_{P}\), \(H_{A}^{\infty}=max\,\forall\,\lambda\in[0,\,1]\), the TN \(Z_{A}^{\lambda}\) and TCN \(H_{A}^{\lambda}\) are twice each other. The number of AATNs is steadily increasing, while the number of AATCNs is steadily decreasing.
###### Abstract  
The windage loss in impeller back gap and labyrinth seal cavities significantly impacts the aerodynamic performances of supercritical carbon dioxide (sCO\({}_{2}\)) compressors. To accurately calculate windage loss, essential factors affecting the skin friction coefficients \(C_{\text{f,d}}\) (disk-type gap) and \(C_{\text{f,s}}\) (shaft-type gap), including Reynolds number \(Re\), pressure ratio \(\pi\), and radius ratio \(\eta\), are investigated in this paper. The flow characteristics of the gap are analyzed and prediction models are proposed. The results indicate that both \(C_{\text{f,d}}\) and \(C_{\text{f,s}}\) decrease with increasing \(Re\) and grow with \(\pi\) and \(\eta\), attributable to expanded high-vorticity regions caused by enhanced flow instability and larger vortices. The leakage flow rate \(m\) is unchanged for \(Re<10^{6}\) since the fluid can flow into the impeller back gap, and slightly decreases for \(Re\geq 10^{6}\) due to the centrifugal force and the inhibition effect of the vortices filling inlet regions. Moreover, the \(m\) grows with \(\pi\) and \(\eta\) due to a larger pressure difference and through-flow area. Maximal relative deviations of 6.23% and 6.83% can satisfy the requirements for calculating accurate windage loss in the impeller back gap and labyrinth seal cavities, which help the primary design of sCO\({}_{2}\) compressors.  
skin friction coefficient; impeller back gap; labyrinth seal; supercritical CO\({}_{2}\) compressor +
Footnote †: journal: Applied sciences  
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