esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model
### The Optimization Mode
#### 3.4.2 Constraint Condition  
1. Segment Point Uniqueness  
Between each two adjacent segments, the candidate point of the previous segment can only be the starting point of the next segment, which should meet the following conditions:  
\[\sum_{k=1}^{n_{k}}\chi_{u,v}^{k}=1. \tag{22}\]  
where, \(n_{k}\) is the amount of segments in route \(j\).  
2. Obstacle Clearance  
The height of route in the terminal area should keep the minimum obstacle clearance (MOC) with the obstacles, that is:  
\[h_{MOC}=\left\{\begin{array}{cc}\text{MOC}&0<\left|d_{0}^{k}\right|<\frac{w }{4}\\ \text{MOC}\Big{(}\frac{2\cdot w-4\cdot d_{0}^{k}}{w}\Big{)}&\frac{w}{4}\leq \left|d_{0}^{k}\right|<\frac{w}{2}\end{array}\right., \tag{23}\]  
\[d_{o}^{k}=\sqrt{\left(x_{u,v}^{k}-x_{o}\right)^{2}+\left(y_{u,v}^{k}-y_{o} \right)^{2}}. \tag{24}\]  
where, \((x_{o},y_{o})\) is the two-dimensional coordinates of the obstacle \(\circ\), and the value of MOC refers to Doc8168 [26].  
3. Segment Turning Constraint  
Segment turning constraint is an indicator of route smoothness. In order to meet the requirements of aircraft maneuverability and passenger comfort, the deflection angle between segments should meet the following conditions:  
\[0\leq\alpha<\frac{l_{k}}{\overline{V}_{T}}\cdot\omega_{\text{min}}. \tag{25}\]  
where, \(l_{k}\) is the length of segment \(k\), \(\overline{V}_{T}\) is the average true speed of aircraft on segment \(k\), and \(\omega_{\text{min}}\) is the minimum turning angular speed of all types of aircrafts.  
4. Flight Around the Restricted Area  
The routes in the terminal area need to give priority to the flight around the restricted area or the dangerous area, and the following conditions should be met:  
\[D_{S}\leq d_{R}. \tag{26}\]  
where, \(d_{R}\) is the actual distance between the aircraft and the restricted area \(R\), and \(D_{S}\) is the safety interval between the aircraft and the restricted area which is about 33,000 feet (10,000 meters).  
4. Numerical Case Study  
In this chapter, Shanghai terminal area is selected as the research object. Runway 34 and 35L of Shanghai Pudong international airport (ZSPD) are used for arrival, while runway 35R is used for departure. Runway 36R for arrival and 36L for departure at Shanghai Hongqiao international airport (ZSSS). In the experiment, 16 typical routes are used as the analysis objects, and their names and codes are shown in Table 1.  
Typical busy days in Shanghai terminal area (25 July 2017) were selected to provide the required atmospheric environment, flight plan, aircraft type ratio and other data. The total number of flights in Shanghai terminal area are 1782, among which the number of flights in ZSPD and ZSSS are 1086 (arrival 542, departure 544) and 696 (arrival 349, departure 347) respectively.
# Parameter Analysis and Optimization of Annular Jet Pump Based on Kriging Model
## 6 Conclusions  
In this paper, an optimization method based on the Kriging model and MMFD is used to obtain the maximum efficiency which is closer to the global optimal solution and to solve the constrained optimization problem with a given head ratio by building the functional relationship between efficiency and design parameters, which makes the method more applicable. The CFD simulation scheme is created and verified with experiment data, which ensures the reliability of the simulation results. Based on the sampling points generated by the OSF method, a Kriging approximate model is established, and the accuracy of the Kriging approximate model is verified by CFD simulation results. To the end, the parameters of the annular jet pump are optimized with the MMFD algorithm.  
Figure 11: Velocity field of jet pump with different \(m\).  
Figure 10: The axial velocity profile of the throat inlet.  
According to the results above, the following conclusions can be drawn:  
1. The \(R^{2}\) of the Kriging approximate model constructed based on OSF sampling method is more than 0.9, so the proposed modeling method meets the accuracy requirements.
2. The optimal efficiency of the annular jet pump is 33.89% predicted in optimization and 34.15% by simulation with the combination of pump parameters: \(\alpha=26.6^{\circ}\), \(\beta=4.12^{\circ}\), \(m=2.11\), \(q=0.6\).
3. The area ratio \(m\) is a key parameter affecting the efficiency of the jet pump, and an analysis of the flow field and performance with different \(m\) was performed. By decreasing the area ratio, \(m\), the jet mixing effect at the outlet of the suction chamber can be improved, but leads to the frictional resistance increase. In this case the optimal value of \(m\) is 2.11.  
Formal analysis, K.X. and G.W.; methodology, K.X.; writing--original draft preparation, K.X., and G.W.; writing--review and editing, L.W., F.Y., W.S. and X.C.; validation, L.W. and X.W.; supervision L.W.; funding acquisition, G.W. and F.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Project (Grant No. 2018 YFF01012900), National Natural Science Foundation of China (Grant No. 51779059, 52001116, 52001089), National Natural Science Foundation of Heilongjiang Province (Grant No. YQ2020E028, YQ2020E033), China Postdoctoral Science Foundation (Grant No. 2020M670889, 2018M630343), Fundamental Research Funds for the Central Universities (Grant No. 3072020 CF0702, 3072020 CF0105, 3072020 CFT0704), and the School Land Integration Development Project of Yantai (Grant No. 2019 XDRHXMPT29).  
The authors declare no conflict of interest.
## 3 Theoretical Formulation and Numerical Modeling
### Dynamic Mesh Algorithm  
The main idea of the dynamic mesh method is to simplify each grid edge into a spring with certain stiffness. The entire computational domain is discrete into a system consisting of a finite number of springs. Boundary motion is equivalent to applying a displacement-dependent force to the system. The force of the spring can be expressed as follows:  
\[\overrightarrow{F_{i}}=\sum_{j}^{n_{i}}k_{ij}(\Delta\overrightarrow{x_{j}}- \Delta\overrightarrow{x_{i}}) \tag{8}\]  
where \(n_{i}\) is the number of grid edges connected to node \(i\); \(\Delta\overrightarrow{x_{i}}\) and \(\Delta\overrightarrow{x_{j}}\) are the displacements of the nodes \(i\) and \(j\); and \(k_{ij}\) is the stiffness of the spring connecting the nodes \(i\) and \(j\). The stiffness can be given by Equation (9):  
\[k_{ij}=\frac{1}{\sqrt{\left|\overrightarrow{x_{i}}-\overrightarrow{x_{j}} \right|}} \tag{9}\]  
where \(\overrightarrow{x_{i}}\) and \(\overrightarrow{x_{j}}\) are the coordinates of the nodes \(i\) and \(j\). They can be given by Equation (10):  
\[\overrightarrow{x_{i}}^{n+1}=\overrightarrow{x_{i}}^{n}+\Delta\overrightarrow {x_{i}}^{m,\varepsilon} \tag{10}\]  
where the superscripts \(n\) and \(n+1\) represent the current time step and the next time step, respectively. \(\Delta\overrightarrow{x_{i}}^{m,\varepsilon}\) is the displacement increment of node \(i\) when the iteration is convergent. It can be calculated as follows:  
\[\Delta\overrightarrow{x_{i}}^{m+1}=\frac{\sum_{j}^{n_{i}}k_{ij}\Delta \overrightarrow{x_{j}}^{m}}{\sum_{j}^{n_{i}}k_{ij}} \tag{11}\]
## 4 Key Enabling Techniques
### Multiple Access Techniques  
Various 5G multiple access techniques have been proposed so far by the telecom industry and academia including Interleave Division Multiple Access (IDMA) [69], Low Density Spreading Multiple Access (LDSMA) [70], Lattice Partition Multiple Access (LPMA) [71], Sparse Code Multiple Access (SCMA) [72; 73], Pattern Division Multiple Access (PDMA) [74; 75; 76; 77], Power-domain NOMA [78; 79] and various others are briefly explained in Figure 4.  
Figure 4: Summary of key enabling techniques for channel capacity and SE improvement.
# Multi-Modal 3D Shape Clustering with Dual Contrastive Learning
## 3 The Proposed Method  
### Architecture of DCL-Net  
The overview architecture of the proposed DCL-Net is illustrated in Figure 1. Let \(D=\{I_{i}^{\mathrm{I}},I_{i}^{\mathrm{D}}\}_{i=1..N}\) denote a 3D shape dataset with \(N\) shapes, where \(I_{i}^{\mathrm{p}}\) denotes the \(v\)-th view in the multiple views of the \(i\)-th 3D shape and \(P_{i}\) denotes the corresponding point cloud of the \(i\)-th 3D shape. As shown in the figure, the proposed DCL-Net includes a multi-modal feature extractor, a representation-level dual contrastive learning (RDCL) module, and an assignment-level dual contrastive learning (ADCL) module. Taking two views from different angles and the corresponding point cloud as inputs, the multi-modal feature extractor was adopted to extract the view and point cloud features. Afterward, the RDCL module was designed to capture more discriminative 3D shape features by simultaneously applying cross-view contrastive learning within the multi-view modality and cross-modal contrastive learning between the point cloud and multi-view modalities in the representation space. Moreover, by effectively applying cross-view and cross-modal contrastive learning to the clustering assignments, the ADCL module was designed to ensure clustering assignment consistency among different views and the corresponding point cloud, thus further boosting the 3D shape clustering performance. Finally, the clustering results are obtained from the soft labels predicted by the ADCL module.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 2 Materials and Methods
### PCA-Based Weight Determination Framework  
Our data fusion methodology employs principal component analysis (PCA) to determine optimal reliability weights for provincial forecasts. This approach effectively preserves information while accommodating the multi-source heterogeneity of load forecasting data.
# Jointly Optimize the Residual Energy of Multiple Mobile Devices in the MEC-WPT System
## 5 Problem Solving
### Solving the Simplified Problem through the ALM Method  
The augmented Lagrangian method is used to reconstruct the constrained problem into an unconstrained problem, and then solve the unconstrained optimization problem. Augmented Lagrangian function:  
\[L_{\text{C}}(R,\alpha,\beta,\theta,\mu)=\sum_{i=1}^{N}\sum_{j=1} \operatorname{\textit{E}}_{i,j}^{con}(R_{i,j}^{off})+\sum_{i=1}^{N}\alpha_{i}( \sum_{j=1}^{M}R_{i,j}^{off}C_{j}-F)+\sum_{i=1}^{N}\sum_{j=1}^{M}\beta_{i,j}A \tag{19}\] \[+\sum_{i=1}^{N}\sum_{j=1}^{M}\theta_{i,j}(-R_{i,j}^{off})+\sum_{i =1}^{N}\sum_{j=1}^{M}\mu_{ij}D+\frac{c}{2}(\sum_{i=1}^{N}\left\|\sum_{j=1}^{M} R_{i,j}^{off}C_{j}-F\right\|_{2}^{2}+\sum_{i=1}^{N}\left\|A\right\|_{2}^{2}\] \[+\sum_{i=1}^{N}\sum_{j=1}^{M}\left\|R_{i,j}^{off}\right\|_{2}^{2}+ \sum_{i=1}^{N}\sum_{j=1}^{M}\left\|D\right\|_{2}^{2})\]In addition, \(c\) is the penalty factor. In order to make the formula concise, we make the following expression:  
\[A =\frac{\kappa_{j}((R_{i,j}-R_{i,j}^{off})C_{j})^{3}}{T^{2}}+\frac{R_ {i,j}^{off}\ln 2}{h_{i,j}^{m}B[W_{0}(\frac{h_{i,j}^{m}(f(i,j)+p_{m,j})}{N_{0}d_{i,j} e}-\frac{1}{e})+1]}\] \[f(\frac{B[W_{0}(\frac{h_{i,j}^{m}(f(i,j)+p_{m,j})}{N_{0}d_{i,j} e}-\frac{1}{e})+1]}{\ln 2})+\frac{p_{m,j}R_{i,j}^{off}\ln 2}{B[W_{0}(\frac{h_{i,j}^{ m}(f(i,j)+p_{m,j})}{N_{0}d_{i,j}e}-\frac{1}{e})+1]} \tag{20}\] \[-(f(i,j)T-f(i,j)\sum_{j=1}^{M}\frac{R_{i,j}^{off}\ln 2}{B[W_{0}( \frac{h_{i,j}^{m}(f(i,j)+p_{m,j})}{N_{0}d_{i,j}e}-\frac{1}{e})+1]})\]  
\[D=R_{i,j}^{off}-\min(R_{i,j},\frac{TB}{\ln 2}[W_{0}(\frac{h_{i,j}^{m}(f(i,j)+p_{ m,j})}{N_{0}f_{i,j}e}-\frac{1}{e})+1]) \tag{21}\]  
We select the ALM to solve the unconstrained problem (18) in each iteration, the optimal solution is denoted by \(R_{i,j}^{(K^{*})}\). \(k\) is the number of iterations, given the termination limits \(\varepsilon\), the initial candidate point \(R_{i,j}^{(0)}\), the initial penalty factor \(c_{0}\) and the penalty factor growth coefficient \(\rho\).  
The penalty factor will be updated iteratively until the termination condition \(c_{k}>\varepsilon\) or the iteration time \(k\) is larger than 50, and then the optimal solution \(R_{i,j}^{(K^{*})}\) is obtain. The pseudo code of the ALM algorithm in Algorithm 1:  
```
0:\(R_{i,j}^{(0)}:\) initial candidate point; \(c_{k}:\) penalty factor; \(\varepsilon:\) termination limits; \(\rho:\) growth coefficient of penalty factor; \(k=0:\) iteration times
1:repeat
2: solving the unconstrained problem (18) by ALM method, get \(R_{i,j}^{(K^{*})}\);
3:\(c_{k+1}=\rho c_{k}\);
4:\(k=k\ +\ 1\);
5:until\(c_{k}>\varepsilon\) or \(k=50\)
6: optimal \(R_{i,j}^{(K^{*})}\)
```  
**Algorithm 1** Augmented Lagrange Method
###### Abstract  
This paper focuses on predicting the End of Life and End of Discharge of Lithium ion batteries using a battery capacity fade model and a battery discharge model. The proposed framework will be able to estimate the Remaining Useful Life (RUL) and the Remaining charge through capacity fade and discharge models. A particle filter is implemented that estimates the battery's State of Charge (SOC) and State of Life (SOL) by utilizing the battery's physical data such as voltage, temperature, and current measurements. The accuracy of the prognostic framework has been improved by enhancing the particle filter state transition model to incorporate different environmental and loading conditions without retuning the model parameters. The effect of capacity fade in the reduction of the EOD (End of Discharge) time with cycling has also been included, integrating both EOL (End of Life) and EOD prediction models in order to get more accuracy in the estimations.  
Keywords:IVHM (Integrated Vehicle Health Monitoring); probability density function; capacity fade; Remaining Useful Life (RUL) estimation; State of Charge +
Footnote †: journal: Applied Sciences
# Molecular Profile of Skin Cancer
## 3 Gene Mutations in Melanoma
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Gene** & **Type** & **Incidence (\%)** & **Type of Melanoma** & **Comments** & **Therapeutic Modalities** \\ \hline CTNNB1 & & & & - is a central component of the & \\ ([PERSON] et al., 2012) & tumor-suppressor gene & 23 & Malignant melanoma & & Wnt (wingless) & TTK inhibitors \\  & & & & & signal-transduction pathway & \\ \hline GNA11 and GNAQ & & & & & - the reduction in melanoblast & \\ ([PERSON] et al., 2010) & proto-oncogene & 50-85 & & Non-epithelial melanocytic & numbers & Selumetinb \\  & & & & - encode G-protein alpha subunit & \\  & & & & & q and alpha subunit 11, & \\ \hline C-KIT & & & & & \\ ([PERSON] et al., 2017) & proto-oncogene & 11 & & - resistance to anti-8 RAF or & Imatinib \\  & & & & - anti-MER targeted therapy & Militinib \\ \hline \end{tabular}
\end{table} 
Table 1: _Cent._BRAF gene mutations may be inherited or acquired. Inherited mutations can cause birth defects, and acquired (somatic) mutations occur later in life and are present only in certain cells. Somatic mutations cause the BRAF protein to be continuously active and to transmit messages to the nucleus even in the absence of chemical signals. Somatic mutations cause continuous activation of BRAF protein to transmit messages to the nucleus even in the absence of chemical signals. This increase in protein activity interrupts the regulation of signaling pathways. This misregulation can result in heart defects, growth problems, skeletal abnormalities and other features found in Noonan syndrome. At least four BRAF gene mutations were found in patients with Noonan syndrome [21]. Mutations in the BRAF V600 protein are found in approximately 50% of melanomas and it is estimated that approximately 8% of solid tumors contain this mutation. Of these, approximately 80-90% result from the substitution of glutamic acid for valine at position 600 (V600E) [22; 23]. 600K BRAF mutation occurs in about 20% of melanoma cases, and most frequently in melanoma in situ or lentigo maligna. V600E BRAF mutation is found in cancers such as hairy cell leukemia, colon cancer, papillary thyroid carcinoma, Langerhans cell histiocytoma and astrocytoma [24; 25; 26]. V600E BRAF mutations lead, by the hyperactivation of MAPK pathway, to a change in cell division rate and induce the proliferation of neoformation vessels by promoting the release of EGF (endothelial growth factor) or the overexpression of proinflammatory cytokines, such as IL-8 [27; 28]. The current gold standard for detecting BRAF mutation remains direct sequencing of tumor DNA, and polymerase chain reaction is a more efficient additional method that is used successfully in such cases. In Table 2, we present the modern methods for BRAF detection; the major disadvantage is the high cost, which makes the method less accessible (Table 2).
# A Pedestrian Detection Network Based on an Attention Mechanism and Pose Information
## 2 The Related Literature
### Object Detection  
Object detection methods can be classified into two types. The first type includes single-stage algorithms, such as the YOLO series [18; 19; 20; 21] and SSD [22], which eliminate the generation stage of candidate proposals and directly predict the boundary frame and category probability of the target in a whole image, thus increasing detection speed while retaining accuracy. The second type includes two-stage algorithms, such as faster R-CNN [10], R-FCN [23], and PV-RCNN [24]. These methods generate a large number of target proposal regions in advance and then optimize them through classification and regression networks. However, two-stage algorithms require the repeated extraction of convolutional features, resulting in a slower model execution, which fails to meet the needs of applications with real-time requirements. Anchor-free detectors, such as CornerNet [25] and CenterNet [26], have also been constructed. [PERSON] et al. developed a pedestrian detection network based on vertical lines that uses the vertical characteristics of upright pedestrians [13]. With these approaches, additional prior box parameters do not need to be set, asfeature maps can be directly used for classification prediction and location regression.
## 5 Challenges and Open Issues
### Technology Demands
#### 5.1.2 Computing and Networking Infrastructure  
In the metaverse, many users will need to be online at once and to interact simultaneously and ubiquitously, generating unfathomably massive amounts of data. Thus, a smooth metaverse experience will require to build and operate sophisticated storage and computing capabilities over a stable, low-latency and high-bandwidth connection.
# Self-Optimizing Traffic Steering for 5G mmWave Heterogeneous Networks
## 3 Self-Optimizing Traffic Steering
### The Concept of Traffic Steering  
As described earlier, the 5G network has a more heterogeneous structure with various spectra. Different networks and spectra have different characteristics and can support different kinds of user devices. To balance the network load, the traditional traffic offloading technique offloads users from heavily loaded networks to lightly loaded networks. As an enhancement of traffic offloading, traffic steering can intelligently manipulate and manage the traffic across different networks and spectra to further optimize the network performance, improve the resource utilization, and deliver better services for users. The applications of traffic steering can be diverse and beneficial for many aspects, as summarized in the following:  
* **Resource utilization:** Due to traffic dynamics, the number of users may exceed the capacity limitation in some cells. Hence, users with different data rate requirements can be steered to corresponding nearby cells with sufficient resources to improve resource utilization.
* **Interference mitigation:** With the changing environment, a large number of users in a single network may cause severe packet congestion and interference. Therefore, traffic steering can also be used to re-distribute users across networks to mitigate interference.
* **Energy saving:** By considering the variation in traffic requirements, some cells might be under-utilized. In such a scenario, we can switch off these cells to save energy and users in these cells can be consequently steered to nearby cells.
## 4 Conclusions  
Taking THz active imaging array as the application background, 0.34 THz triplers, and mixers based on 12-\(\mu\)m-thick GaAs monolithic integrated process and Schottky diode chips, are proposed in this article. The diodes' geometry was optimized first to achieve the best performance by quantitatively analyzing the physical dimensions of diodes. Based on the use of precise simulation tools, such as Ansoft HFSS and Agilent ADS, accurate electromagnetic modeling of the diode was carried out and the performance of triplers or mixers was predicted through co-simulation. Experimental results show that, at room temperature, by injecting 100 mW, the THz tripler could achieve a peak efficiency of 6.8% at 338 GHz. The output power of greater than 1 mW was achieved in the range of 324 GHz to 352 GHz. The THz SHM showed good performance, in that the DSB noise temperature was lower than 1500 K and the DSB conversion loss was lower than 9 dB in the frequency range of 325-352 GHz. Three frequency triplers and three mixers were processed and assembled and their performance was tested and compared. It is shown that they all show a high consistency in performance. The results of both the THz tripler and SHM agreed well with the simulations, thus verifying the validity of the design method proposed in this paper. To sum up, the GaAs monolithic integrated THz tripler and SHM proposed in this paper show good performance and provide support for future applications to THz active imaging arrays. However, in actual THz imaging applications, we are still facing challenges, such as limited operating range caused by large atmospheric attenuation, which can be tackled by power combining technology and III/V compound-based power amplifications to further increase transmit power.  
Conceptualization, Y.L., B.Z., and X.L.; methodology, Y.L. and Y.F. ([PERSON]); software, Y.L. and Y.Y.; validation, Y.L., B.Z., and D.J.; investigation, Y.L. and Z.N.; resources, Y.L. and X.Z.; writing--original draft preparation, Y.L.; writing--review and editing, B.Z. and Y.F. ([PERSON]); supervision, Y.F. ([PERSON]); funding acquisition, B.Z. 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 (NSFC) under Grant 91738102, Grant 61771116, and Grant 62022022.  
The authors declare no conflict of interest.
## 2 Background
### Pallet Exchange in Italy  
In Italy, the standard EPAL pallet exchange system involves two main stakeholders:  
* Trademark industries: these companies purchase and own EPAL pallets, using them to ship branded products to their destinations.
* Large-scale retailers: these are the stores where the products shipped by trademark industries are sold.  
Given the distances between these two entities across Italy, the standard exchange model can be logistically challenging and environmentally taxing. Nolpal addresses these issues by acting as the pallet owner and offering a leasing system supported by a national network of over 40 collection sites, known as Nolpal Points. These collection sites serve as distribution hubs, enabling efficient returns and exchanges of pallets. Nolpal Points function as loading and unloading stations, ensuring punctual and precise delivery and collection of rented pallets.  
This system enhances logistic efficiency, reduces the distance traveled by trucks transporting goods and pallets, minimizes transportation impacts, and lowers operational costs and pallet wear.  
The differences between the two logistics models are depicted in Figure 3, where:  
* The red bullet represents the trademark industry under consideration.
* The green bullets represent the retailers associated with it.
* The blue triangles mark the Nolpal points, which are distributed across Italy.
* The blue arrow consists of the distance traveled between the trademark industry and a retailer.
* The cyan arrows depict shorter distances, specifically between one Nolpal points and the retailers and the trademark industry respectively.