esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Optimization Model
### Objective Function Formulation  
The objective function for the problem of the optimal placement and sizing of STATCOMs in power systems corresponds to the minimization of the annual operating costs related to the cost of energy losses and the installation of the STATCOMs, respectively. These components of the objective function are defined in Equation (1).  
\[\min A_{\text{cost}}=f_{1}+f_{2}, \tag{1}\] \[f_{1}=C_{\text{kWh}}T\sum_{h\in\mathcal{H}}\sum_{k\in\mathcal{ N}}\sum_{m\in\mathcal{N}}Y_{km}V_{kh}V_{mh}\cos(\delta_{kh}-\delta_{mk}-\delta_{km}) \Delta_{h},\] \[f_{2}=T\bigg{(}\frac{k_{1}}{k_{2}}\bigg{)}\sum_{k\in\mathcal{N}} \Big{(}\alpha(y_{k})^{2}+\beta y_{k}+\gamma\Big{)}y_{k},\]  
where \(f_{1}\) is the component of the objective function associated with the annual operative costs produced by the energy losses; \(f_{2}\) represents the annualized investment costs in STATCOMs; \(A_{\text{cost}}\) represents the objective function value associated with the linear combination of the components \(f_{1}\) and \(f_{2}\), i.e., the total annual operating costs of the power system; \(C_{\text{kWh}}\) corresponds to the average cost of electric energy; \(T\) represents the plan horizon period (i.e., 365 days); and \(Y_{km}\) and \(\theta_{km}\) are the magnitude and angle of the admittance that relates nodes \(k\) and \(m\), respectively. \(V_{kh}\), \(V_{mh}\), \(\delta_{kh}\), and \(\delta_{mh}\) represent the voltage magnitudes and angles associated with buses \(k\) and \(m\), respectively, in the period \(h\). \(\Delta_{h}\) represents the length of the period of time where the loads are constant, i.e., 1 h in this research, and \(k_{1}\) and \(k_{2}\) correspond to positive constants associated with the installation costs of STATCOMs. \(\alpha\), \(\beta\), and \(\gamma\) are positive constants related with the variable costs of the STATCOMs (costs associated with the reactive power injection of the STATCOMs i.e., \(y_{k}\)). Note that \(\mathcal{H}\) and \(\mathcal{N}\) represent the sets that have all the periods of time and buses of the network, respectively.  
Figure 1: Schematic diagram of a STATCOM.  
**Remark 1**.: _Note that the structure of Equation (1) corresponds to a single-objective optimization problem. In the case of multi-objective optimization, the structure of the objective functions is the following:_  
\[\min f_{1}=C_{kWh}T\sum_{h\in\mathcal{H}}\sum_{k\in\mathcal{N}}\sum_ {m\in\mathcal{N}}Y_{km}V_{kh}V_{mh}\cos(\delta_{kh}-\delta_{mh}-\theta_{km}) \Delta_{h},\] \[\min f_{2}=T\bigg{(}\frac{k_{1}}{k_{2}}\bigg{)}\sum_{k\in\mathcal{ N}}\Big{(}\alpha(y_{k})^{2}+\beta y_{k}+\gamma\Big{)}y_{k}.\]  
_Note that the goal of the multi-objective model corresponds to the minimization of both objective functions due to the nature of these that correspond to the investment and operating costs, which ideally must tend toward zero._
## 3 Simulation Results and Interpretation
### Scenario 1: The Effect of Distance
#### 3.1.3 Simulation with Clutter Model 3  
We performed a simulation of the two targets using clutter model 3 (targets drowned in cluttered normal distribution). The clutter follows a normal distribution with a standard deviation \(\sigma\) and a mean \(\mu\).  
During the simulation using the clutter of model 3 with \(\mu=1\) and \(\sigma=1000\), we observed the same limitations on detection performance in both models 1 and 2 (cases where the parameters have been increased) for the conventional detector, and our proposed detector has been able to distinguish the targets at M = 33 and M = 40. This situation can be seen in Figure 25.  
We have summarized the key parameters of our simulations in the following Table 6 in order to show the effect of the clutter on the distance resolution of radar targets. The results obtained are encouraging us to use our proposed method.  
In this scenario, we have presented several Monte Carlo simulations for three different types of clutter models to demonstrate the performance of the proposed detector. In general, we observed that the proposed detector was able to reduce the false alarms and find the two targets used for the simulations, while the conventional detector (OS-CFAR) failed to  
Figure 24: Targets simulated with clutter model 2 (b = 600) using (a) conventional OS-CFAR and (b) proposed detector.  
Figure 25: Targets simulated with clutter model 3 (\(\mu=1\) and \(\sigma=1000\)) using (a) the conventional OS-CFAR and (b) the proposed detector.  
distinguish the existing targets from the false alarms, serving as a benchmark for evaluating the efficiency of the proposed detectors.
# Multi-View Gait Recognition Based on a Siamese Vision Transformer
###### Abstract  
Although the vision transformer has been used in gait recognition, its application in multi-view gait recognition remains limited. Different views significantly affect the accuracy with which the characteristics of gait contour are extracted and identified. To address this issue, this paper proposes a Siamese mobile vision transformer (SMViT). This model not only focuses on the local characteristics of the human gait space, but also considers the characteristics of long-distance attention associations, which can extract multi-dimensional step status characteristics. In addition, it describes how different perspectives affect the gait characteristics and generates reliable features of perspective-relationship factors. The average recognition rate of SMViT for the CASIA B dataset reached 96.4%. The experimental results show that SMViT can attain a state-of-the-art performance when compared to advanced step-recognition models, such as GaitGAN, Multi_view GAN and Posegait.  
multi-view gait recognition; Siamese neural network; vision transformer; view-feature conversion; gradual view +
Footnote †: journal:  
some of the gait feature information is lost. This significantly impacts the extraction of the gait contour characteristics. In response to this issue, this paper uses a Siamese neural network to calculate the posture relationship between the two views and calculate the characteristic conversion factor. Under the premise of retaining identity information, the useful high-dimensional intensive characteristics of the network are strengthened to make its high-dimensional features clearer, and the effect of the loss of gait characteristics on recognition accuracy is lessened. The SMViT model constructed using this concept can obtain higher recognition accuracy in a non-90\({}^{\circ}\) multi-view; moreover, this model is more robust.  
In summary, this paper makes the following contributions:  
1. It designs a reasonable and novel gait-view conversion method, which can deal with the problem of multi-view gait;
2. It constructs the SMViT model, and uses the view characteristic relation module to calculate the association between multi-view gait characteristics;
3. We develop a gradually moving view-training strategy that can raise the model's robustness while raising the recognition rate for less precise gait-view data.  
The structure of this paper is as follows. The technologies related to gait recognition are introduced in Section 2. Then, the SMViT is constructed and the gradual-moving-view training method is explained in Section 3. In Section 4, experimentation with the CASIA B gait dataset [8] is employed to explore the models and methods that are presented in this paper. Finally, in Section 5, we summarize the research contained herein and consider the future directions of gait recognition technology.
## 5 Results
### Signal Power Attenuation Simulation  
Based on the designed satellite terahertz communication system and inter-satellite terahertz communication scenario, the power attenuation of the inter-satellite terahertz communication signal is calculated and simulated according to the value of the calculated pointing error angle, and it can be obtained signal power attenuation as a function of time. Assuming that the radius of the receiving antenna is 0.5 m, the signal power attenuation under the condition of no pointing error and under the condition of pointing error is obtained through simulation, as shown in Figure 12.  
It can be seen from Figure 12 that, under the condition of no pointing error, the signal power attenuation is comprised between \(-40\) and \(-60\) dB, which is relatively stable. Under the condition of pointing error, the signal power attenuation is as periodic distribution as the pointing error angle. As the pointing error angle increases, the signal power attenuation also increases. The minimum value of uplink signal power attenuation is \(-175.5\) dB, the maximum value is \(-248.4\) dB, the minimum value of downlink signal power attenuation is \(-164.1\) dB, and the maximum value is \(-357.8\) dB. After the attitude adjustment of the ground station, the signal power attenuation decreases rapidly. It can be seen from the comparison that the signal power attenuation caused by the pointing  
Figure 11: Relationship between transmitter pitch angle, azimuth angle and pointing error angle.  
Figure 10: Variation of pointing error angle caused by satellite orbit perturbation with time: (**a**) uplink terahertz communication link for LEO-GEO; (**b**) downlink terahertz communication link for GEO-LEO.  
system. Therefore, the higher the operating frequency of the inter-satellite terahertz communication system, the higher the demand for pointing error accuracy, and the slight perturbation of the satellite may have a greater impact on the communication system.
## 6 Network Layer for WVSN
### _Energy level based packet selection_  
This selection benefits from video compression layer prioritization. The probability that a packet is dropped depends on the priority level of the packet and energy level of the node. The packet with first priority level never drops.  
Dropping probabilities \(P_{dr(1)}\), \(P_{dr(2)}\),..., \(P_{dr(k)}\),..., \(P_{dr(N-1)}\) are assigned to each priority level \(P_{1}\), \(P_{2}\),..., \(P_{k}\),..., \(P_{N-1}\) respectively. Where \(P_{k}\) stands for both \(P_{M(k)}\) and \(P_{D(k)}\) and for all \((k)\in\mathbb{N}\), \(P_{dr(1)}\)=0, \(P_{dr(k)}\in\) [0,1]. Since the content of packets with priority level (\(k\)) is most important than content of packets with priority level (\(k\)+1) then \(P_{dr(k)}\)\(<P_{dr(k+1)}\).  
Furthermore, the values of dropping probabilities and energy state of the node have negative correlation in a way that when battery level decreases, dropping probability increases. When the energy level of a node is more than \(ES_{(N-1)}\)(node has enough energy) the packet is selected with probability as shown in Figure 14.a. When the energy decreases to less than \(ES_{(k)}\) the dropping probability increases as shown in Figure 14.b. The selection probability is constant between two consequent normalized energy levels and changes when it passes an energy threshold level (as has been shown in Figure 12).  
The packet selection probability function can be defined with regards to application requirements. The function used for probabilities calculation should have a domain based on priority levels that node is allowed to send, and range between \(0\) and \(1\). Also, it should be strictly increasing. The suggested function is:  
\[P_{dr(1)}=\begin{cases}\dfrac{1}{P_{k}-1}\times\big{(}l-1\big{)}\times\beta& \quad\text{if}\quad 0<l<P_{k}\\ 1&\quad\text{Otherwise}\end{cases} \tag{6}\]  
In this equation, \(P_{dr(l)}\) is the dropping probability for packet with priority level (\(l\)), \(P_{k}\) is the maximum priority level that VSN can transmit see Seccion 6.2.1, \(l\) is the priority level of current packet, and \(\beta\) is a coefficient which improves the flexibility of the Equation (6) which is defined between [0, 1]. After specifying dropping probability (\(P_{dr}\)) for each packet, a random number \(R\) selected from [0, 1], if \(R\)\(<=P_{dr(l)}\) packet is eligible for dropping, otherwise the packet is transmitted.
## 6 Papain Use as an Exfoliant Agent in Cosmetic Applications  
### General Profile and Source of Papain  
The cysteine protease enzyme of papain is mainly sourced from _Carica papaya_ latex, a plant originated from the _Caricaaceae_ plant family [96, 97, 98]. Unlike bromelain and ficin, which exist as enzyme mixtures, papain is a single major proteolytic enzyme, even though papaya latex also contains chymopapain, caricain, and glycol endopeptidase [98]. Papain latex refers to a milky fluid harvested by making incisions on the surface of unripe fruits; nonetheless, papain can also be extracted from other plant parts, like peels and leaves [74, 99]. Although papain is an endopeptidase, it also functions as an amidase, esterase, transamidase, transesterase, and thiolesterase [74, 100, 101]. Papaya production is highly prominent in tropical and subtropical areas, like India, Brazil, and Nigeria [102, 103].  
Papain is distributed throughout different papaya plant parts like latex (16.76% \(w/w\)) [96], fruit, peel, seeds, and leaves, and its concentration highly differentiates among ripe and unripe fruit [104]. Traditionally, unripe papaya fruit latex has been the primary commercial source of papain, as it contains high proteolytic activity (1834-4293 units/mg), varying across different studies [105]. However, with increasing interest in sustainability and waste utilization, studies now focus on extracting papain from papaya by-products, including peels or seeds, to minimize environmental waste and align with green chemistry principles. The enzymatic activity of papain is challenging and varies depending on fruit maturity, processing techniques, geographic origin, etc., [74; 100; 101].
# Robust Visual Localization with Dynamic Uncertainty Management in Omnidirectional SLAM
## 3 Robust Visual Localization
### Dynamic Uncertainty Management
\begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{3}{c}{**Filter-Based SLAM Stages**} \\ \hline
**State** & **Expression** & **Terms** \\ \hline \multirow{4}{*}{**Prediction**} & \(\hat{x}_{t+1|t}=\hat{t}(\hat{x}_{t|t},u_{t})\) & \(f_{t}\): control input-current state relation \\  & \(\hat{z}_{t+1|t}=\hat{n}(\hat{x}_{t+1|t},x_{i})\) & \(u_{t}\): control input, initial prediction seed \\  & \(P_{t+1|t}=\frac{\partial f_{tt}}{\partial x}\hat{r}_{t|t}\frac{\partial f_{tt} }{\partial x}^{T}+Q_{t}\) & \(h_{t}\): observation-current relation \\ \hline \multirow{4}{*}{**Innovation**} & \multirow{4}{*}{\(S_{t+1}=\frac{\partial f_{tt}}{\partial x}P_{t+1|t}\frac{\partial f_{tt} }{\partial x}^{T}+R_{t+1}\)} & \(R_{t}\): camera covariance noise \\  & & \(Q_{t}\): odometer covariance noise \\  & \(v_{t+1}=z_{t+1}-\hat{z}_{t+1|t}\) & \(\frac{\partial f_{tt}}{\partial x}\): [PERSON] of \(f_{t}\) at \(x_{t}\) \\  & \(S_{t+1}=\frac{\partial h_{t|t}}{\partial x}P_{t+1|t}\frac{\partial h_{t|t}}{ \partial x}^{T}+R_{t+1}\) & \(\frac{\partial h_{t}}{\partial x}\): [PERSON] of \(h_{t}\) at \(x_{t}\) \\  & & \(z_{t}\): observation measurement \\  & & \(v_{t}\): innovation measurement \\  & & \(S_{t}\): innovation covariance \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Filter-based Simultaneous Localization and Mapping (SLAM) stages. Notice the dynamic adaptive nature of this procedure, which always depends on the current uncertainty, thus adapting to the changing conditions of the system. It is closely related to inconsistencies and instabilities, which are normally caused by non-linear noise. Therefore, this contribution assures a reliable control for the uncertainty management, which implicitly performs false positive avoidance and, thus, non-linear effects reduction. A corresponding analysis of the results will be presented in Section 5 for further validation.
# Coupling Denoising to Detection for SAR Imagery
## 2 Materials and Methods
### SAR Remote Sensing Dataset
#### 2.1.2 Comparison to other SAR Detection Datasets  
Table 1 summarizes the detailed comparisons between our own constructed dataset and other publicly available SAR detection datasets, i.e., AIR-SARShip-1.0 [16], SSDD [18], SAR-Ship-Dataset [17], and HRSID [19]. SAR-Ship-Dataset is the dataset with the largest number of instances, followed by our own dataset. The primary differentiator of our dataset as compared with other datasets lies in (1) class diversity such as ships, aircrafts, and etcetera classes, and (2) the number of scene areas. We obtained the SAR images from a variety of harbor and airport peripheral areas around the world wide and annotated different shapes of objects.
## 2 Materials and Methods
### Antioxidants Content and Antiradical Activity of Bread and Preparations
#### 2.7.5 Antioxidant Activity  
The evaluation of antioxidant activity was performed using the ABTS analytical method [37]. A 7 mM concentration of ABTS was prepared by dissolving it in water. The ABTS radical cation (ABTS\({}^{\star\star}\)) was generated by mixing the ABTS stock solution with 2.45 mM potassium persulfate (final concentration) and allowing the mixture to stand in the dark at room temperature for 12-16 h before use. The reduction of ABTS\({}^{\star\star}\) in the presence of the sample was measured at 734 nm using a Helios Gamma 100-240 spectrophotometer (Runcorn, UK). To analyze the extracts, the ABTS\({}^{\star\star}\) solution was diluted in PBS buffer (pH 7.4) to achieve an absorption value of 0.700 \(\pm\) 0.05. A volume of 2.00 mL of the ABTS\({}^{\star\star}\) solution and the corresponding extract in PBS buffer were used for the measurement. The bleaching of ABTS\({}^{\star\star}\) was monitored at 30 \({}^{\circ}\)C, and the decolorization after 6 min was utilized as the indicator of antioxidant activity. The radical scavenging activity was expressed as Trolox equivalents antioxidant capacity (mM of Trolox per kg of sample). For the calibration curve, Trolox solutions in the concentration range of 0-2.5 mM (R\({}^{2}\) = 0.9957) were used.
# A Method of Predicting Critical Derailment Speed and Analysis of Derailment Process
## 1 Introduction  
Train derailment is one of the complex and difficult problems in the field of vehicle dynamics. Since 1908, when the famous derailment coefficient criterion was proposed by [PERSON], the derailment problem has been researched for more than a century, but there is no good solution [1].  
The derailment processes of trains under high-speed and low-speed conditions show different patterns. Under low-speed conditions, the quasi-static model generally is used to study the train-derailment process [2, 3, 4, 5]. Tests can be conducted through the derailer, and the simulation method is used to analyze the running attitude of the train during derailment [6, 7]. The theoretical system of the lateral motion stability of vehicles at low speed is relatively perfect, but there are still many unsolved problems of high-speed derailment. Under high-speed conditions, the train derailment form is variable, the mechanism of action is complex, and the nonlinear characteristics are more obvious [8]. The effects of side wind, earthquake, track bulge, or structural component failure on the operational safety of high-speed trains have been studied by many scholars [9, 10, 11, 12, 13, 14]. However, research on the derailment process which is caused by over-speed on a curve is scarcer.  
[PERSON] [15] et al. proposed a method to calculate the whole process of train derailment caused by over-speed on curves and proved that the derailment coefficient and wheel weight reduction rate make it difficult to discern whether the train is derailed or not. The article puts forward a new method to calculate the critical derailment speed of a vehicle ondifferent radius curves. The wheel-rail contact state and dynamic response at the critical derailment speed are analyzed. The research results could provide a reference basis for train-derailment early warning.
## 2 Simulation Methodology
### Laminar and Turbulent Reacting Flows  
The Reynolds number equation in the internal flow is:  
\[Re=\frac{\rho VL}{\mu}=\frac{\rho VD_{h}}{\mu},\ (D_{h}=\frac{4A_{c}}{p}) \tag{6}\]  
Here, \(Re\), \(\rho\), \(V\), \(L\), and \(\mu\) are the Reynolds number, density of the fluid, velocity of the fluid, characteristic length, and dynamic viscosity of the fluid, respectively. Additionally, \(D_{h}\), \(A_{c}\), and \(p\) are the hydraulic diameter, cross-sectional area of the fluid flow, and wetted perimeter that is in contact with the fluid, respectively.  
In previous research, the slurry flow was normally treated as laminar flow [18; 20; 23]. The transition from laminar flow to turbulent flow is generally known to occur around \(Re=2300\)[24; 25]. The characteristic length can be defined as the hydraulic diameter (\(D_{h}\)) of the slurry flow along the pad groove. According to the Reynolds numbers calculated for different hydraulic diameters, the slurry flow between a wafer and a pad is not fully laminar, but is turbulent vortex flow while rotating. Other exposed surfaces of the pad belong to the open laminar flow regime. Briefly, the slurry flow during the CMP process exhibits both laminar flow and weak turbulent flow, showing small eddies and vortices. To simulate the slurry flow between the wafer and the pad for pads with different geometric shapes, the turbulence model was chosen to capture the swirl flows and vortices. In addition, the realizable k-\(\varepsilon\) model (RKM) was used to show more accurate predictions of the separation flow and rotational flow [22; 26].
# Robust and Efficient Corner Detector Using Non-Corners Exclusion
## 4 Experimental Results and Analysis
### Consistency of Corner Numbers
performs worst for JPEG compression seen from Figure 11. Figure 7. Shows the average CCN under each transformation for five corner detectors. It can be seen that the average CCN of the RECD detector is the second best under compression and slightly more than SUSAN and Harris. For image rotation as shown in Figure 12, the average consistency of the RECD is highest at most of the rotation angles. Its invariance to rotation is better than the other four corner detectors. The Moravec corner detector shows the worst consistency, because the corner measures of the detector are highly sensitive to rotation. Although the average consistency of the RECD is highest, its consistency is obviously lower than the FAST-9 corner detector and SUSAN corner detector in the angle range of 20\({}^{\circ}\) to 30\({}^{\circ}\) and significantly higher than both of them in the angle range of 40\({}^{\circ}\) to 55\({}^{\circ}\). No matter how much the angle of rotation is, the consistency of the Moravec corner detector is the worst. Through observation of Figures 7 and 12, The RECD detector performs best under rotation transformation and is followed by the FAST-9 corner detector. The Harris detector performs better than the SUSAN corner detector and worse than the FAST-9 corner detector. Compared with other four detectors, the proposed RECD detector achieves better consistency under shearing, scaling, brightness variation and rotation. It has the second best consistency under lossy JPEG compression. The RECD corner detector combines the merits of FAST and Harris. The non-corners exclusion step eliminates the interference of many non-corners and reduces the amount of computation of following steps. Thus, the following steps can detect fewer false corners without interference of those non-corners. As a result, the final corners obtained by RECD are more robust than the FAST-9 detector and Harris corner detector. Figure 11: CCN of each method under JPEG compression. Figure 8:0 CCN of each method under rotation transformation. Image compression can retain the high-frequency information such as edges in an image. The Moravec detector chooses the edge direction with minimum variance to detect corners. It can extract many pixels on image edges although some are not corners. This is the reason why the Moravec detector performs best under JPEG compression.