esa-sceva/satcom-chunk-collection huggingface dataset:
# Part-Treatment Efficacy Prediction Using a CMA-ES-Based Dendritic Neuron Model
## 3 Methods
### Applying the CMA-ES to Train the DNM  
Figure 4 shows the training process of the DNM using the CMA-ES. The CMA-ES follows the general framework of evolutionary algorithms. It optimizes the problem by iteratively evolving a population of candidate solutions using the aforementioned operations. Since the DNM has two parameter vectors, \(\mathbf{w}\) and \(\mathbf{q}\), to be optimized, these two vectors form the solution vector of the CMA-ES algorithm as follows:  
\[\boldsymbol{\alpha}_{\lambda}=\{x_{\lambda}^{1},x_{\lambda}^{2},\ldots,x_{ \lambda}^{2\cdot I\cdot B}\}=\{w_{1,1},w_{1,2},\ldots,w_{I,B},q_{1,1},q_{1,2}, \ldots,q_{I,B}\} \tag{13}\]  
where \(\boldsymbol{\alpha}_{\lambda}\) is the \(\lambda\)-th candidate solution in the population of the CMA-ES. The loss function of the DNM is calculated as the fitness value of \(\boldsymbol{\alpha}_{\lambda}\). Finally, the CMA-ES terminates when the stopping criterion is met, and the optimal solution \(\mathbf{m_{best}}\) is outputted.  
The mean square error (MSE) is commonly used as the loss function of the DNM. It can be calculated as follows:  
\[MSE=\frac{1}{2S}\sum_{i=1}^{S}(O_{i}-T_{i})^{2} \tag{14}\]  
where \(O_{i}\) is the actual output of the DNM, \(T_{i}\) is the label value, and \(S\) is the number of data samples.  
When the dataset is unbalanced and the MSE function is used as the loss function, it makes the machine learning model more inclined to predict classes with large sample numbers [41]. Data sampling is commonly used in many studies to address data imbalance, but such methods can have an unexpected impact on the data: undersampling can result in missing data, oversampling is blind in generating the data, and data sampling, in general, can easily marginalize data [42; 43; 44]. In this study, we attempted to use a focal loss (FL) [45] to address the data imbalance problem. The FL function makes is designed to bring attention to the imbalanced data during the model training process, allowing the model to focus more attention on a fewer number of class samples in the dataset. It can improve the accuracy of the hard-to-classify samples by increasing the weights of fewer samples. For binary classification, the FL adds a power modifier \((1-p_{i})^{u}\) to the cross entropy and can be calculated as follows:  
\[FL=-\frac{1}{S}\sum_{i=1}^{S}(1-p_{i})^{u}\text{log}(p_{i}) \tag{15}\]  
\[p_{i}=\begin{cases}O_{i}&,\text{if }T_{i}=1\\ 1-O_{i}&,\text{otherwise}\end{cases} \tag{16}\]  
where the output \(O_{i}\) of the DNM indicates the probability of the prediction with the label \(T_{i}=1\). \(u\) is a positive constant and is set to 2. A larger value of \(p_{i}\) indicates that the model's prediction is closer to the ground truth.
# Learning Curve in Robotic-Assisted Total Knee Arthroplasty: A Systematic Review of the Literature
###### Abstract  
Several innovations have been introduced in recent years to improve total knee arthroplasty (TKA). Robotic-assisted surgery is gaining popularity for more precise implant placement while minimizing soft tissue injury. The main concerns are increased cost, operative time, and a significant learning curve. This systematic review aims to analyze the surgical time learning curve, implant placement accuracy, and complications related to robotic-assisted TKA (raTKA). A systematic literature review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The research was conducted up to September 2022 in four databases (PubMed/MEDLINE, Embase, Scopus, and the Cochrane Database of Systematic Reviews), with the following key terms: \"robotic-assisted\", \"total knee arthroplasty\", \"robotic\", \"TKA\", \"learning\", and \"TKR\". The methodology quality of the studies was assessed using the Methodological Index for Non-Randomized Studies (MINORS) criteria. This systematic review was registered on the International Prospective Register of Systematic Reviews (PROSPERO), ID: CRD42022354797, in August 2022. Fifteen clinical studies that analyzed the raTKA learning curve of 29 surgeons and 2300 raTKAs were included in the systematic review. Fourteen surgeons reported the presence of an inflection point during the learning curve. Few studies have reported the learning curve of raTKA regarding lower limb alignment, component position, and intraoperative and postoperative complications. The main finding of this systematic review is that the procedure number required to reach the learning plateau is about 14.9 cases. Furthermore, an average decrease in surgical time of 23.9 min was described between the initial and proficiency phases; the average surgical time in the two phases was 98.8 min and 74.4 min, respectively. No learning curve was observed for implant placement and lower limb alignment because the implants were correctly placed from the first raTKA. No significant complication rates were reported during the raTKA learning curve.  
TKA; total knee arthroplasty; knee replacement; orthopedic; robotic; robotic-assisted; raTKA; learning; curve; limb alignment +
Footnote †: journal: Medical Image Analysis
## 3 Results and Discussion
### Emulsion Structure
#### 3.3.5 Storage Stability  
As shown in Figure 6, two treatment groups of the fish broth samples were stored for 1, 3, 5 and 7 days at 4 \({}^{\circ}\)C. The appearance and the status (stratification, flocculation, sedimentation, upward and other fluctuation) of the fish brohs reflect the physical stability of the samples during storage. Flocculation and sedimentation were observed in groups A1 and B1 on Day 1. The reason for this phenomenon may be that less protein was dissolved in the fish broth at the beginning of the boiling process and the lack of tumbling action of the boiling water resulted in a weak emulsification of the fish broth. Samples from groups A2 and A3 showed significant stratification with extended storage time, while A4 and A5 showed a more stable emulsion system. This was probably due to the formation of an emulsified system. However, in order to develop nutritious and healthy fish broth products, the thermo-ultrasound group had significantly more nutrients in the fish broth than the boiling water group (Figures 1 and 2). Samples B2, B3 and B4 of the thermo-ultrasonic treatment group showed varying degrees of fat uplifting during storage. Group B5 samples were stable on the first day with no fat uplifting, but slowly showed a small degree of fat uplifting in storage afterward. The storage state of the samples indicated that the emulsified system of the fish broth treated with thermo-ultrasonic treatment for 120 min was the most stable and its nutrient content was the highest. Increased total protein content promotes increased surface protein loading, which can affect emulsion stability. Broths treated with thermo-ultrasonic for 120 min had the highest protein content (Figure 1A), which enhanced the steric hindrance between oil droplets [17]. This may be one of the reasons for the higher stability of the broth samples that were thermo-ultrasonically treated for 120 min. In summary, the thermo-ultrasonic treatment method promoted better nutrients and stability in the fish broth, in support of the development of ready-to-eat fish broth products.  
Figure 5: Confocal laser scanning micrographs of fish broth prepared by boiling water (**A**) and thermo-ultrasonic treatment (**B**) with different treatment times (**1–5—0**, 30, 60, 90, 120 min; Bar = 10 \(\upmu\)m).  
Figure 6: Appearance of the fish broth after storage at 1, 3, 5, 7 days by boiling water (A) and thermo-ultrasonic treatment (B) with different treatment times (1–5—0, 30, 60, 90, 120 min).
# Investigation of a Precise Control Scheme for Rice Quality
## 3 Results and Discussion
### Drying Time
To enhance the descriptive power of the model and ensure its simplicity and reliability, insignificant interaction and quadratic terms (\(p\) > 0.05) were eliminated to derive a regression equation for each response variable, and the results are presented in Table 5. The Dtn and Dtt regression models demonstrate a remarkably good agreement between the predicted and measured results, with corresponding R\({}^{2}\) values of 0.9545 and 0.9399, respectively. Additionally, the high reproducibility of these models is evidenced by their low CV values (CV < 10%) [24]. The ANOVA results indicate a strong agreement between the fitted models and experimental data for the regression equation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & \multirow{2}{*}{**df**} & \multicolumn{2}{c}{**Drying Time (Net)**} & \multicolumn{2}{c}{**Drying Time (Total)**} \\ \cline{2-5}  & & **Sum of Squares** & **F-Value** & **Sum of Squares** & **F-Value** \\ \hline Model & 20 & 84.52 & 123.79 ** & 56.09 & 25.05 ** \\ T: Temperature & 1 & 39.22 & 1148.95 ** & 33.93 & 303.1 ** \\ RH: Relative air humidity & 1 & 4.82 & 141.21 ** & 0.48 & 4.32 * \\ MC: Moisture content & 1 & 2.44 & 71.54 ** & 2.59 & 23.14 ** \\ V: Air speed & 1 & 3.56 & 104.42 ** & 0.2 & 1.83 \\ TR: Tempering ratio & 1 & 26.98 & 790.24 ** & 14.56 & 130.03 ** \\ T*RH & 1 & 2.28 \(\times\)\(10^{-3}\) & 0.067 & 0.07 & 0.63 \\ T*MC & 1 & 0.15 & 4.47 * & 0.025 & 0.23 \\ T*V & 1 & 0.013 & 0.39 & 7.81 \(\times\)\(10^{-3}\) & 0.07 \\ T*TR & 1 & 0.029 & 0.86 & 0.015 & 0.14 \\ RH*MC & 1 & 1.73 & 50.81 ** & 0.26 & 2.35 \\ RH*V & 1 & 2.28 \(\times\)\(10^{-3}\) & 0.067 & 0.09 & 0.81 \\ RH*TR & 1 & 0.34 & 10.03 ** & 0.17 & 1.48 \\ MC*V & 1 & 0.029 & 0.86 & 0.38 & 3.42 \\ MC*TR & 1 & 2.81 \(\times\)\(10^{-5}\) & 8.24 \(\times\)\(10^{-4}\) & 0.038 & 0.34 \\ V*TR & 1 & 0.083 & 2.43 & 3.13 \(\times\)\(10^{-4}\) & 2.79 \(\times\)\(10^{-3}\) \\ T\({}^{2}\) & 1 & 1.22 & 35.81 ** & 0.48 & 4.26 \\ RH\({}^{2}\) & 1 & 0.1 & 2.99 & 1.58 & 14.11 \\ MC\({}^{2}\) & 1 & 0.9 & 26.43 ** & 7.84 \(\times\)\(10^{-3}\) & 0.07 \\ V\({}^{2}\) & 1 & 2.17 & 63.63 ** & 0.69 & 6.19 \\ TR\({}^{2}\) & 1 & 0.65 & 19.13 ** & 0.48 & 4.26 \\ Residual & 38 & 1.3 & & 4.25 & \\ Lack of Fit & 22 & 0.82 & 1.24 & 1.9 & 0.59 \\ Pure Error & 16 & 0.48 & & 2.36 & \\ Cor Total & 58 & 85.82 & & 60.34 & \\ \hline \hline \end{tabular} Note: \(p\)-value < 0.01 is represented by **; \(p\)-value < 0.05 is represented by *.  
\end{table} 
Table 4: Analysis of variance (ANOVA) results of milling quality models.
## 2 A New Routing Architecture
### Architectural Design Options  
A distinctive feature of a separation architecture is the location _where_ the separation of core and edge addressing takes place. In a host-based architecture, each participating host performs the separation. Packets on the transport layer are addressed with identifiers and the protocol that handles the separation adds locators before the packets leave the host. On the network, only locators are used to forward packets. In a network-based architecture, on the other hand, identifiers are used both on transport and network layer inside a host, while the translation from identifiers to locators is done inside the network at a middlebox, e.g., a border router. In mixed architectures, both options are used and there is a separation at the host and at a network middlebox. The location of the separation and, thus, the entity that requires mappings from identifier to locator has direct influence on the design of the mapping system, which we studied in [10].  
Another design question is _how_ the separation is implemented. One option is map-and-encaps [11], where global locator information is added to packets destined to a different domain by tunneling them across the Internet backbone to the gateway with a specific global locator. This requires an additional IP header which increases the IP packet size and can cause MTU issues. An alternative is address rewriting, where global locator information is added to packets destined for a different domain by coding this information into source and destination addresses. The last option is source routing, whereby locator and identifiers are encoded by the source host in the destination address. In this case, no modifications of packets along the path to the destination are necessary.  
In addition, it is important to decide which _advanced mechanisms_ or services should be natively supported by the architecture that are not directly required for the plain architecture to work. This could be, for example, support for interworking, mobility, multipath routing, or traffic engineering, as well as built-in security. All these options have direct influence on the design of the architecture and may influence each other so that a multitude of different scenarios has to be considered. Hence, care has to be taken when choosing the right combination of design options.  
The _GLI-Split architecture_ is a separation scheme which uses address rewriting to translate between identifiers and routing locators. The separation is done both at hosts and at border routers. Hosts use identifier addresses on the transport layer and _local_ routing locator addresses on the network layer. Border routers then translate between _local_ and _global_ routing locator addresses. Due to this mixed architecture, mapping lookups are done both at hosts and at border routers. GLI-Split natively supports communication with classic IPv6 nodes and implements features to support mobility, multihoming, traffic engineering, and multipath transfer.
## 3 Method
### The Multi-Agent Communication and Negotiation Mechanisms in the CoReaAgents Framework
#### 3.3.1 Autonomous Tool Filter and Tool Augmentation Between the Plan Agent and the Tool Agent  
To avoid irrelevant and redundant information from impacting task planning, we design a synergy mechanism between the Plan Agent and the Tool Agent. Specifically, the Plan Agent performs tool filtering on the toolset prior to task planning, while the Tool Agent augments each tool in the filtered toolset.  
As shown in Figure 2, Step1 gives a toolset \(TS=\{T_{1},T_{2},T_{3},\ldots,T_{n}\}\) containing all the tools and a brief description of each tool \(T_{i}\) in the \(TS\), including tool-related parameters and roles, to make it easy to select the tools that are highly relevant to this task. As shown in Equation (1), the Plan Agent \(M_{plan}\) combines prompts \(P_{filter}\) and complex task information \(I\) to select a related toolset \(RTS=\{T^{\prime}_{1},T^{\prime}_{2},T^{\prime}_{3},\ldots,T^{\prime}_{m}\}\) where \(m<n\) from \(TS\), which reduces the negative impact of irrelevant information on task planning.  
\[RTS=M_{plan}(P_{filter},TS,I) \tag{1}\]  
After the Plan Agent completes tool filtering, the Tool Agent performs tool augmentation in \(RTS\). As shown in Figure 4, the Tool Agent combines the task scenario with a general description of the tool to further provide supplementary details specific to the current command scenario. This mechanism can enhance the accuracy of the tools' application during subsequent task planning.
###### Abstract  
Selective laser melting (SLM) is a rapidly evolving technology that requires extensive knowledge and management for broader industrial adoption due to the complexity of phenomena involved. The selection of parameters and numerical analysis for the SLM process are both costly and time-consuming. In this paper, a three-dimensional radial basis function-finite difference (RBF-FD) meshless model is introduced to accurately and efficiently simulate the molten pool size and temperature distribution during the SLM process for austenitic stainless steel (AISI 316L). Two different volumetric moving heat source models were presented, namely the ray-tracing method heat source model and the double-ellipsoidal shape heat source model. The temperature-dependent material properties and phase change process were also considered, based on experiments and effective models. Results of the model for the molten pool size were validated with those of the literature. The proposed approach can be used to predict the effect of different laser power and scan speed on the molten pool size and temperature gradient along the depth direction. The result reveals that the depth of the molten pool is more sensitive to laser power than scan speed. Under the same scan speed, a 22% change in laser power (45 \(\pm\) 10 W) affects the maximum temperature proportionally by about 9%. The developed algorithm is computationally efficient and suitable for industrial applications.  
selective laser melting (SLM); meshless method; radial basis function-finite difference (RBF-FD); thermal analysis; molten pool; phase change
## 4 Applications and Discussions
### Elastic Buckling Analysis of Shear Deformable Columns
Based on Figure 3 in Section 4.3, \(S_{\text{TB}}\) decreases to 0 as \(\lambda\) increases to 1.43\(\pi\) (\(\pi\)/0.7). Therefore, the buckling condition in this case can be solved as  
\[S_{\text{TB}}=0\Rightarrow\tan\lambda_{\text{cr}}=\chi\lambda_{\text{cr}}= \frac{\lambda_{\text{cr}}}{1+\lambda_{\text{cr}}^{2}\varphi/12}\Rightarrow P _{\text{cr}}=\frac{\lambda_{\text{cr}}^{2}EI/L^{2}}{1+\lambda_{\text{cr}}^{2} \varphi/12}. \tag{19}\]  
Figure 3: Influence of axial force factor on element stability functions by Equation (15) (exact expression) and Equation (5) (simplified expression by [PERSON] et al. [18]). In addition, for a fixed-fixed Timoshenko column (case 5), the buckling state is corresponding to the condition that the denominator \(\phi_{\text{TB}}\) in \(T_{\text{TB}}\), \(Q_{\text{TB}}\), \(S_{\text{TB}}\), and \(C_{\text{TB}}\) equals zero [13]. Based on Equation (15), \(\phi_{\text{TB}}\) decreases to 0 as \(\lambda\) increases to \(2\pi\). Therefore, the buckling condition in this case can be solved as  
\[\phi_{\text{TB}}=0\Rightarrow\lambda_{\text{cr}}=2\pi\Rightarrow P_{\text{cr }}=\frac{4\pi^{2}EI/L^{2}}{1+\pi^{2}\varphi/3}. \tag{20}\]  
It is noted that this section gives the same results as those from classical analyses by [PERSON] et al. [3], but the matrix analysis procedure is more simplified and systematic. Therefore, the derived Equation (15) for the stability stiffness matrix of an axial-loaded Timoshenko beam-column is verified.
## 3 Results
### Built-up Land Expansion Since 1990  
Built-up land expansion was the major land-use conversion process in the ZXEB from 1990 to 2017. The total area of built-up land increased significantly, from 3930 km\({}^{2}\) in 1990 to 6755 km\({}^{2}\) in 2017, of which urban land increased by 1431 km\({}^{2}\), the infrastructure area increased by 1181 km\({}^{2}\), and rural settlements increased by a minimum of 212 km\({}^{2}\) (Figure 6). Regarding the two periods, 1990-2000 and  
Figure 5: The spatial pattern of land-use and land-cover change (LUCC) in the Zhujiang–Xijiang Economic Belt (ZXEB). (**top**): 1990–2000, (**bottom**): 2000–2017.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Cropland** & **Woodland** & **Grassland** & **Water Body** & **Built-Up Land** & **Unused Land** \\ \hline \multirow{4}{*}{2000–2017} & Cropland & 0.9591 & 0.0040 & 0.0003 & 0.0034 & 0.0331 & 0.0000 \\  & Woodland & 0.0008 & 0.9909 & 0.0028 & 0.0005 & 0.0050 & 0.0000 \\  & Grassland & 0.0004 & 0.0212 & 0.9697 & 0.0015 & 0.0072 & 0.0000 \\  & Water body & 0.0080 & 0.0016 & 0.0085 & 0.9186 & 0.0628 & 0.0004 \\  & Built-up land & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 0.0000 \\  & Unused land & 0.0008 & 0.0171 & 0.0008 & 0.0171 & 0.0630 & 0.9012 \\ \hline \end{tabular}
\end{table}
Table 5: Land-use transition possibilities, 2000–2017.  
2000-2017, the most obvious built-up land expansion occurred in Guangzhou and Foshan, followed by Nanning and Zhaoqing (Figure 7). Yunfu, Chongzuo, and Laibin experienced the least amount of expansion. During both periods, all sub-classes of built-up land (urban land, rural settlements, and infrastructure area) continued to increase. Rural settlements constituted the most significant sub-class of built-up land in the ZXEB, showing a steady increase in size (Tables 3 and 4).  
To determine the regional differences in urban expansion, the authors analyzed the built-land expansion and mapped the urban land expansion (Class Code 51, Table 1, Figure 8). Urban land increased in size significantly, from 739 km\({}^{2}\) in 1990 to 2169 km\({}^{2}\) in 2017. Guangdong, Foshan, and Nanning experienced the largest increase in urban land in both periods, followed by Nanning and Zhaoqing; generally, the cities in Guangdong province evolved the fastest (Figure 8), and such urban expansion was at the expense of croplands. Guangxi province, Nanning, followed by Liuzhou and Baise, was found to have experienced the greatest increase in urban land from 1990 to 2017, while Wuzhou, Chongzuo, and Yunfu (Guangdong) saw the least growth.
# Tag-to-Tag Interference Suppression Technique Based on Time Division for RFID
## 2 RFID Collision Problem
### Types of RFID Collision
#### 2.1.2 Reader-to-Tag Collision  
A reader-to-tag collision (tag interference) occurs when a tag receives multiple concurrent queries from different readers. This problem can be solved by assigning different channels to nearby readers [13].
## 2 Materials and Methods
### Incomplete Gamma Function  
The incomplete Gamma Function is obtained from the gamma function, \(\Gamma(a)\), which is defined as:  
\[\Gamma(a)=\int_{0}^{\infty}t^{a-1}\cdot exp(-t)\cdot dt, \tag{7}\]  
where \(a>0\). The interval of integration of \(\Gamma(a)\) can be split into two parts rendering two functions:  
\[\gamma(a,x)=\int_{0}^{x}t^{a-1}\cdot exp(-t)\cdot dt, \tag{8}\]  
\[\Gamma(a,x)=\int_{x}^{\infty}t^{a-1}\cdot exp(-t)\cdot dt, \tag{9}\]  
where \(x\geq 0\). The functions \(\gamma(a,x)\) and \(\Gamma(a,x)\) are, respectively, called Lower and Upper Incomplete Gamma Functions. The latter being also called Complementary Incomplete Gamma Function or Prym's function. Both are defined on complex numbers, but only on those with a positive real part in the case of \(\gamma(a,x)\). More specifically, the function \(\Gamma(a,x)\) will be used in this study to conduct the thermal analysis of the SC cell. In this regard, Figure 3 shows the evolution of \(\Gamma(a,x)\) for different values of \(a\).  
Another function,  
\[\Gamma(a,x_{1},x_{2})=\int_{x_{1}}^{x_{2}}t^{a-1}\cdot exp(-t)\cdot dt=\Gamma( a,x_{1})-\Gamma(a,x_{2}), \tag{10}\]  
called the Generalized Incomplete Gamma Function, can be defined from \(\Gamma(a,x)\) and is also used in this study. All the functions mentioned in this section are widely applied in other fields of research, such as astrophysics, probability theory, and engineering, particularly in heat transfer studies [57; 58; 59; 60; 61; 62].