esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
To understand the real impact of a refrigeration system, the indirect emissions also need to be calculated. The energy efficiency of the refrigeration system is affected by the design of the system and the selection of the working refrigerants [10,22]. In many studies, the energy efficiency of the refrigeration systems operating with refrigerants alternative to commonly used HFCs has been investigated [23, 24, 25, 26]. [PERSON] et al. [23] performed a comparative analysis to investigate the influence of using R1234 ze(E) and R515B as low-GWP replacements to R134a energy performance and environmental impact of heat pump systems. Their results showed that the alternative refrigerants exhibited a comparable coefficient of performance (COP) to R134a. Consequently, the TEWI was reduced by 18 and 15% when using R1234 ze(E) and R515B, respectively, due to their much lower GWP values than R134a. Another analysis was done by [PERSON] et al. [24] to study the capability of R1224 yd(Z), R1233 zd(E) and R1336 mzz(Z) as low-GWP alternatives to R245 fa in a high-temperature heat pump (HTHP). According to their results, the COP of the system increased by 27, 21 and 17% when using R1233 zd(E), R1336 mzz(Z) and R1224 yd(Z), respectively. The lower GWP values, and the improvement of the system's energy performance using the alternative refrigerants, resulted in a 59 to 61% reduction in the TEWI, compared to R245 fa. [PERSON] et al. [25] experimented with studying the refrigeration system's energy performance operating with R134a and its drop-in replacements, including R450A and R513A. They found that the COP of the refrigeration system increased by 1.8% when using R513A in comparison with R134a, but the R450 COP value was 5.3% lower than that of R134a. [PERSON] et al. [26] investigated the energy performance of an HTHP cascade system equipped with an IHX that operated with low-GWP alternatives to HFCs. Their study included R1233 zd(E), R1336 mzz(Z), R1224 yd(Z) and pentane in the high-stage cycle, and R1234 yf, R1234 ze(E), butane, isobutene and propane in the low-stage cycle. Their study showed that R1336 mzz(Z) and pentane provided the highest system performance in the high-stage cycle. The system's best performance was observed when using pentane/butane with 13% higher COP compared to the R245 fa/R134a refrigerants. In this study, the warming impact of the heat pump systems operating with different low-GWP alternatives to R134a and R245 fa as the most commonly used HFCs, is investigated. The study includes the use of five low-GWP refrigerants for each baseline HFC, and it is extended to four countries with different levels of emissions in their electricity production. The aim of this work is to promote the utilization of the TEWI metric to describe the influence of different alternative refrigerants on the warming impact of heat pump systems. This study provides a comprehensive view of the potential of low-GWP refrigerants to be used as a replacement to R134a and R245 fa while considering the direct and indirect CO\({}_{2}\)-eq. emissions.
# Robotic Assembly for Irregular Shaped Peg-in-Hole with Partial Constraints
## 1 Introduction
The hole searching is a typical model-free method to detect the hole position when the peg-hole assembly task is in an unstructured environment. Some studies use a linear searching spiral searching or circle searching method to detect the hole location. [PERSON] used the spiral searching path to detect the hole center [15]. [PERSON] regarded the searching process as the problem of finding minima in depth field and proposed the searching path planning policy using convex optimization techniques [16]. Some studies focus on the dual peg-in-hole assembly. [PERSON] carried out a systematic analysis for dual peg-in-hole tasks and built contact and jamming models in the different assembly stages [17; 18]. [PERSON] analyzed the contact and jamming model to propose the vertical assembly strategies for flexible dual peg-in-hole robotic assembly [19]. Other studies considered the irregular peg-hole operation proposing the special error compensation strategies. [PERSON] used a partial spiral force trajectory (PSFT) to implement square peg-hole assembly [6]. [PERSON] built a force model about the boundary of the square hole to detect the hole position [20]. In all above the research, the workpieces are in a fully constrained environment, where the posture and position are fixed. However, in practical applications, a more common scenario is where some workpieces may have partial constraints. The components have one or two degrees of freedom. If the workpieces are not in the fixed state, the workpieces may be driven by the contact force and lead to assembly failure. It increases the difficulty of assembly. At present, very few studies focus on this topic, so it is also an open problem. [PERSON] et al. proposes a new manipulation strategy to complete sensorless to complete a peg inserting the partial constraint hole [21]. In this application, the robot has finished the partial constraint workpieces assembly with the visual and assistant fixture. Most studies about errors compensation and hole searching require fully constrained workpieces. According to our investigation, only quite a few papers focus on partial constraint assembly. Some of them are interested in posture detection with a visual sensor. To constrain the workpieces, some of them focus on fixture design, but the complex assistant device is a special design. It has two drawbacks, one is poor universality, and another is the great cost. Therefore, this paper studies the robotic irregular shaped peg-hole assembly problem where the parts are not fixed by building the kinematic model for partial constraint among existing workpieces, analyzing and using the constraint motion features to design a special irregular peg-hole robust assembly control strategy. This method implements the partial constraint peg-hole assembly without any extra accessories. The proposed method can be applied to other irregular peg-hole assemblies where the pegs have the characteristics of discontinuous curvature as shown in the following Figure 1. The adjacent edges can provide similar characters.
# MVCG-SPS: A Multi-View Contrastive Graph Neural Network for Smart Ponzi Scheme Detection
## 5 Experiments
### Results and Analysis
#### 5.4.4 Explainability Analysis  
To enhance interpretability, we conducted a SHAP (SHapley Additive exPlanations) analysis on a representative Ponzi scheme case (Table 6). The results highlight that features such as upstream invitations (SHAP value = +0.23) and investment-to-payment ratio  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Hyperparameter** & **Search Range** & **Final Value** \\ \hline Learning Rate & \{0.001, 0.005, 0.01, 0.05\} & 0.01 \\ \hline Hidden Layer Dimension & \{64, 128, 256\} & 128 \\ \hline RL Learning Rate (\(\alpha\)) & \{0.01, 0.05, 0.1\} & 0.05 \\ \hline \(\lambda_{\text{intra}}/\lambda_{\text{inter}}\) & [0.1, 1.0] & 0.7/0.3 \\ \hline Meta-Path Views (\(V\)) & \{2, 3, 5, 7\} & 5 \\ \hline Reward Discount Factor (\(\gamma\)) & \{0.5, 0.7, 0.9, 0.99\} & 0.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameter tuning summary.  
Figure 3: Sensitivity analysis of MVCG-SPS with respect to the number of meta-path views \(V\) and the reward discount factor \(\gamma\).  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Configuration** & **F1 Score** & **AUPRC** \\ \hline Baseline (Full Model) & 0.902 & 0.891 \\ \hline Replace \(P_{2}\) with temporal path & 0.874 (\(-3.1\%\)) & 0.863 (\(-3.1\%\)) \\ \hline Add extra sampling meta-path & 0.924 (+2.4\%) & 0.912 (+2.4\%) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Impact of alternative meta-path configurations on detection performance.  
(SHAP value = +0.19) are critical discriminators. These findings align with known Ponzi scheme mechanics, where hierarchical recruitment and unsustainable returns are hallmark traits. Such interpretability not only validates MVCG-SPS's decision logic but also provides actionable insights for forensic analysts.
## 5 Results
### Dimension 2  
In Dimension 2, we have already performed an analysis on target tracking in a previous article [40]. We have shown in simulation results that the LCPSO algorithm was relevant to track an underwater mobile target with communication constraints [40]. A maximal speed constraint is necessary, arbitrarily set to \(v_{max}=5\) m.s\({}^{-1}\), because the capacities to lose the swarm must be controlled. We extend the work made in this article studying two types of trajectories for the source to illustrate their impact on tracking performances:  
* The source follows an elliptical trajectory, centered on \((0,0)^{T}\), with radius \(L_{x}=15\) m and \(L_{y}=10\) m and initial position at point \((-40,-40)^{T}\). This choice is arbitrary, but the important thing is to see how the swarm reacts with violent heading changes, periodically coming back in the area that the agents are monitoring. The speed of the source oscillates between 2 and 4 m.s\({}^{-1}\).
* The source has a constant trajectory, that is, a constant heading. It always starts from the point \((-40,-40)^{T}\), with a speed of 3 m.s\({}^{-1}\) and a heading of 0.8 rad. The heading is chosen to cross the area monitored by our agents.  
We can see in Figure 8 that when \(N>15\), the tracking fail percentage increases gradually, especially for the constant trajectory. This is due to the environment function \(f\): if some agents are too far from the source, they will only be able to measure the noise; and, even worse, this will also be the case for their neighbors. Consequently, the swarm will be dislocated into packets. This phenomenon is illustrated in Figure 5b. In the case of a constant trajectory, the isolated agents have very little chance of finding the group that succeeds in tracking it, unlike the elliptical trajectory where the source comes back. Thus, with communication restrictions, it is necessary to limit the number of agents that will track the source, in order to have better tracking performance and do not waste resources; we can see that between 10 and 15 agents, the source tracking is optimal whatever its trajectory.  
We add Figure 9 to give an operational point of view of our algorithm, as a function of \(r_{com}\) and \(r_{eq}\), with the same parameters [40]. With the help of Figure 8, the number of agents is fixed to \(N=10\). In Figure 9, we can observe more restrictions than in dimension 1. Indeed, there is an area where a too low \(r_{eq}\) degrades the results whatever \(r_{com}\), because agents are too close from each other, and the swarm is unable to have a stable formation. There are also more important restrictions on communication limits, in comparison with Dimension 1. Indeed, if \(r_{eq}\) is too much important compared to \(r_{com}\), the number of isolated agents increases and the swarm is unable to track the target. Under this limit, some agentscan lose the group, but without consequences on tracking performances, as illustrated in Figure 5b.
# Satellite IoT Edge Intelligent Computing: A Research on Architecture
## 4 Results and Discussions  
In order to better evaluate the edge intelligence computing architecture of the satellite IoT, we have established simulation experiments. First, we simulate the connectivity and coverage performance of the satellite IoT. Next, we simulate the training and inference process of the satellite IoT edge intelligent computing architecture, mainly from the aspect of delay.
# Accuracy Analysis of the Measurement of Centre of Gravity and Moment of Inertia with a Swing
## 3 Results
### Centre of Gravity (CG)--Swing
Figure 5: Function \(SwingF1\): fitting of the points for the analysis cases C1,C2,C4,C5 and difference of the measured value indicated with \(M\) to the fitted value \(F\) normalised by the measured value—in relation to the calibration weights \(F_{W}\). Table 2 summarises the chosen analysis cases as well as the results based on both fitting functions \(SwingF1\) (indicated with \(F1\)) and \(SwingF2\), which introduces a constant offset \(d_{F2}(2)\). The values are in the range of \(10^{-3}\) degrees. A detailed comparison of the mean values for each of the functions is presented in Table 3. The difference between the results of \(d_{\rm S}\) based on the functions show that the difference is in the range of millimetres. An under-prediction of the theoretical approach is expected because those calculations are conducted without the inertia inclinometer and the attached cable. The difference between the two distances is for the fitting with the function \(SwingF2\) closer to the theoretical values. Furthermore, Figure 7 shows the advantage of this adapted function for small errors around the zero value. Based on those points, the function \(SwingF2\) can be seen as better suited for future analysis. Nevertheless, further measurements have to be conducted to improve the evaluation of the CG of the swing. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Case** & \(d_{\rm W}\) & **Max \(F_{\rm W}\)** & **Max \(i\)** & **Pivot Point** & \(d_{SF1}\) & \(d_{SF1}\)- & \(d_{SF2}\) (**1**) & \(d_{SF2}\) (**2**) & \(d_{SF2}\)- \\  & **(m)** & **(kg)** & & & **(mm)** & **(mm)** & **(mm)** & **(\({}^{\circ}\))** & **(mm)** \\ \hline C1 & 0.2 & 1.0 & 20 & P1 & 69.6 & 0.47 & 71.3 & 0.0011 & 0.07 \\ C2 & 0.4 & 0.5 & 9 & P1 & 68.6 & \(-0.57\) & 71.3 & 0.0019 & 0.03 \\ C3 & = C1 + C2 & 29 & P1 & 69.2 & 0.10 & 71.2 & 0.0013 & \(-0.10\) \\ C4 & 0.6 & 4.7 & 25 & P2 & 1283.9 & \(-0.33\) & 1277.4 & \(-0.0002\) & 0.73 \\ C5 & 0.4 & 6.1 & 15 & P2 & 1283.9 & \(-0.35\) & 1274.9 & \(-0.0003\) & \(-1.76\) \\ C6 & =C4 + C5 & & 40 & P2 & 1284.9 & 0.68 & 1277.7 & \(-0.0002\) & 1.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of the investigated cases and the calculated distance of the CG based on the two different fitting functions \(SwingF1\) and \(SwingF2\)—mean values for both pivot axis P1 and P2 presented in Table 3.  
Figure 6: Function \(SwingF2\): fitting of the points for the analysis cases C1,C2,C4,C5 and difference of the measured value indicated with \(M\) to the fitted value \(F\) normalised by the measured value—in relation to the calibration weights \(F_{\rm W}\). Figure 7: Detailed view of the fitting for the analysis case C1 (**left**) and C4 (**right**) with both functions.
## 1 Introduction
Due to their water solubility, the minerals will be transferred almost entirely to the final beverage [24]. The volatile organic compounds belonging to different chemical classes identified in roasted coffee, such as alcohols, aldehydes, esters, furans, ketones, phenols, pyrazines, pyridines, pyrroles, and sulfur compounds, define the sensory properties of coffee [19; 20]. Some chemicals, such as furans, may harm human health [19]. Volatile organic compounds with low odor thresholds make significant olfactory contributions to coffee flavor [27]. Volatile thiols are among the compounds that impact coffee quality and flavor most, even at very low concentrations [1]. Polyphenols are an essential source of dietary antioxidants and are widely present in fruits, vegetables, cereals, and beverages derived from plants, such as red wine, tea, cocoa, and coffee [18; 28]. The roasting temperature of coffee beans noticeably affects the polyphenolic profile through the Maillard reaction, while providing coffee with its pleasant aroma and taste [18]. Additionally, coffee is an essential source of major elements and micronutrients, such as K, Ca, P, Fe, Mn, Zn, and Cu [7]. Data available in the literature may indicate to consumers the beneficial nutritional and health effects, along with the potentially toxic effects, of coffee consumption, as well as information on the available coffee processing methods. Moreover, coffee producers may use data in the literature to explore the relationships between production processes and coffee quality, as well as possible methods for identifying adulteration, while scientists may use the available data to develop new methods for coffee characterization and new ways to fingerprint the authenticity of coffee [29; 30]. Many studies have reported on the formation of coffee flavor, mineral and antioxidant contents, and toxic compounds in coffee beans of different varieties subjected to different roasting degrees. However, only a few papers have investigated the volatile organic compound, mineral, and polyphenol contents of coffee beans according to variety and roasting degree. This study presents variations in thermal behavior, volatile organic compound profiles, and mineral and polyphenol contents in commercially available coffee samples of different varieties subjected to different roasting intensities.
## 1 Introduction
[39] found that these four products demonstrated satisfactory accuracies during non-drought years when using EC data as a reference, but the VPM GPP products performed much better compared with the other three products under drought years. Therefore, the VPM GPP products outperformed the other three GPP products when investigating the responses of GPP to variations in environmental moisture changes. The Yangtze River Basin (YRB) holds significant importance as a crucial industrial and agricultural production zone, serving as an ecological security barrier in China. As a unique and complete natural ecosystem, the YRB has great ecological functions such as water and soil conservation, and holds a pivotal role in maintain the ecological balance and security of both the surrounding regions and the entire country. Efforts had been made for scholars to investigate the dynamic changes of vegetation and its productivity in the YRB. [PERSON] et al. [8] revealed that the Normal Difference Vegetation Index (NDVI) exhibited a growing trend in the growing season from 1982 to 2015 in the YRB, which was mainly attributed to the rising temperature. [PERSON], et al. [40] found that temperature can significantly affect vegetation changes in Sichuan, which comprised 25% of the entire YRB area. Based on different scenarios, [PERSON] et al. [41] found that land use alterations resulted in a favorable impact on total production by 3.42 Tg C, and the climate changes contributed to a negative effect by \(-1.44\) Tg C. Since different data sources have different results, using more reliable data and methods can better capture the GPP changes and quantify its driving forces. Thus, using the VPM GPP data may prove to be a more precise estimation of the GPP trend and facilitate a more effective assessment of the relative contributions of climate changes in the YRB. In this study, we first evaluated the spatio-temporal variations of the GPP in the YRB based on the VPM GPP data. Then, the responses of GPP to the variations of climate factors were explored. In addition, the contributions of those driving factors were quantified based on statistical models.
# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 3 Materials and Methods
### Sample Solutions to the Optimization of Data Collection  
Let us predict the solution of the optimizer for several simple network topologies. Consider the network topologies given in Figure 4.  
When considering the ad hoc topology given in Figure 4a, two RSU nodes E and G, will be selected as agents. Agents E and G cover the whole vehicular network (A-G), thus satisfying the constraint in Equation (16). The selection of node G as an agent is obvious as it has four neighbors (A,B,C,D) broadcasting to it. According to the third term of the objective function derived in Equation (10), an agent should have a higher number of neighbors broadcasting to it. In addition, agents should be selected such that the combined cost of an agent is minimum according to the second term of the objective function given in Equation (10). RSUs will have lower \(\mathcal{B}_{i}\) value due to low communication cost and low delay in Ethernet links. Hence, RSU E will be selected as the agent which has two broadcasting neighbors and a lower \(\mathcal{B}_{i}\) value compared to vehicle F, which has only one neighbor and has a higher \(\mathcal{B}_{i}\) value.  
In order to gain more insight into the working of the optimization for data collection we can inspect the solution for the chain topology given in Figure 4b. In order to satisfy the constraints, it should be noted that two agents and two broadcasting nodes should be selected for the chain topology. The effect of the agent's cost (second term of the objective function in Equation (10)) is the same for any selection of the node. The choice of agents and broadcasting nodes depends on the minimization of payload exchanged between the neighbors and the maximization of the payload transmitted to the agents. It should be noted that when the two middle nodes (B and C) are selected as agents, each node B and C will have one broadcasting neighbor. Thus, the total payload broadcast will be limited to two packets. For any other choice of agents (other than both B and C are selected as agents), the total broadcast packets will be greater than two. Thus, the optimizer will select B and C as agents and set A and D as broadcasting nodes.
## 2 Sensitivity Analysis
### Polynomial Chaos Expansion (PCE) Metamodeling  
Metamodels (or surrogate models) are, usually, statistically equivalent and computationally less expensive functional approximations of computationally expensive models. The reason for constructing metamodels is to make stochastic modeling (e.g., Monte Carlo simulation) of computationally expensive models feasible. Polynomial Chaos Expansion (PCE) is one of the metamodeling techniques that approximates a computational model as a sum of polynomials orthonormal to the joint probability distribution of the input vector. Advantages of the PCE are that it is a universal metamodel (i.e., does not depend on the type of the computational model), non-intrusive (it is based on repeated runs of the computational model, which is treated as a black box), suited for high-performance computing (i.e., \"embarrassingly parallel\") and its accuracy converges with the increased number of computational model samples [21].  
If \(y=f(\mathbf{x})\) is a computational model with an input random vector defined as \(\mathbf{x}=[x_{1},...,x_{n}]\) that follows a joint probabiltiy density function (PDF) \(p(\mathbf{x})\), then it's PCE is:  
\[y=f(\mathbf{x})=y^{PCE}=\sum_{\alpha=0}^{\infty}y_{\alpha}\cdot\Phi_{\alpha}( \mathbf{x}) \tag{18}\]  
where \(y_{\alpha}\) are the coefficients (or coordinates) and \(\Phi_{\alpha}(\mathbf{x})\) are polynomials that are orthonormal with respect to the joint PDF of the input vector \(p(\mathbf{x})\). In practice, the infinite sum defined in Equation (18) is truncated to a finite sum, using one of several truncation schemes, such as hyperbolic norm or maximum interaction truncation [22]. If the PCE is truncated to the degree of \(P\), the truncated PCE is:  
\[y=f(\mathbf{x})=y^{PCE}=\sum_{\alpha=0}^{P-1}y_{\alpha}\cdot\Phi_{\alpha}( \mathbf{x}) \tag{19}\]Due to the orthogonality of the polynomial basis with respect to the \(p(\mathbf{x})\), the mean and the variance of a PCE are:  
\[\mu^{PCE}=E[f^{PCE}(\mathbf{x})]=y_{0} \tag{20}\]  
\[(\sigma^{PCE})^{2}=E[(f^{PCE}(\mathbf{x})-\mu_{y})^{2}]=\sum_{\alpha\in A}y_{ \alpha}^{2} \tag{21}\]  
where \(A\) is a subset of coefficients of non-constant polynomial basis elements only.  
To estimate the coefficients of the PCE, a computational model is sampled generating a set of input-output pairs, often called experimental design (ED). A commonly used approach for estimating PCE coefficients is the least-squares minimization method [23].