esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Machine Learning-Based Prediction of Solar Irradiance Using Multi-Site Data
## 2 Proposed Solution
### Proposed Framework
* Incremental feature selection: To further refine the input space and evaluate the contribution of individual features, an incremental feature selection strategy was applied. Starting from the most important feature, models were iteratively trained by progressively adding one feature at a time. At each step, model performance was evaluated using validation metrics (e.g., RMSE and R\({}^{2}\)). This approach enabled the identification of the optimal subset of features that maximized predictive performance while minimizing the risk of redundancy and overfitting. * Model training and evaluation strategy:
* Training pipeline: The predictive task is formulated as a regression problem. The following steps were performed for each station independently:
* Train/test split: Each station's cleaned and feature-selected dataset was split into 80% training and 20% testing sets. To avoid data leakage and ensure temporal integrity, the dataset for each site was chronologically ordered. We then used a single-split strategy, where the first 80% of the observations (based on timestamps) were used for training and the remaining 20% were reserved for testing. This approach avoids shuffling or interleaving of future observations into the training set. * Model initialization: Each model was initialized using a fixed random seed (42) to ensure reproducibility. * Multiple regressors were trained: MLPRegressor (3 \(\times\) 100 hidden layers), GradientBoosting, RandomForest, CatBoost, ExtraTrees, etc. All models used fixed seeds and default hyperparameters for reproducibility. The set of employed models includes:
* MLPRegressor: The best configuration for the model was with 3 hidden layers, each with 100 neurons, relu activation, Adam optimizer, and 10,000 epochs. * GradientBoosting, RandomForest, ExtraTrees, CatBoost, XGBoost, LightGBM: All tree-based regressor models were initialized using Scikit-learn default parameters, with hyperparameter tuning skipped for uniform comparison. * Model evaluation: Each model's performance was evaluated using five metrics: R\({}^{2}\), RMSE, MAE, median absolute error, and MAPE. * Ensemble modeling: In addition to standalone models, to improve generalization, a stacking-based ensemble was built for each station. Stacking ensemble models were implemented using Scikit-learn's StackingRegressor with 5-fold internal cross-validation.
## 3 Numerical Example
### Example 1
Figure 4, respectively, shows the relative error between the FEM result and the analytical solution at positions (50,0) and (75,0). By comparing the relative errors of Refined1 and Refined2 with or without adaptive DOFs in Figure 4a, it can be seen that the maximum relative error at position (50,0) of Refined1 with adaptive DOFs is not larger than that of the initial coarse mesh, and the maximum relative error of Refined2 with adaptive DOFs is only slightly larger than that of Refined1, which indicates that the timing of adaptive condensation of DOFs is appropriate. As some of the master DOFs transform into slave DOFs, the other untransformed master DOFs do not introduce large errors. As can be seen from Figure 4b, due to the small number of elements in the initial coarse mesh, the position (75,0) has no corresponding DOFs, and its result is calculated by the linear interpolation of the DOFs of the elements it is in, which has very poor accuracy, while in Refined1 and Refined2, the corresponding positions have DOFs and the calculation accuracy is much better than that of the initial coarse mesh when the temperature gradient is large initially. When using ADOF-FEM, the position (75,0) is transformed from the master DOF into the slave DOF, and its value is obtained by the linear interpolation of the surrounding master DOFs. At this time, the relative error at the position is only related to that of the surrounding master DOFs and not to the error of this DOF itself due to discretization. According to the previous content, the relative error of the other unchanged master DOFs is small on its own, while the additional error introduced by the DOF transformed based on the coarsening criterion is less than the error tolerance. Therefore, the relative error can be controlled within a small range when the master DOF is transformed into the slave DOF. Furthermore, although the number of DOFs continues to decrease, the loss of accuracy gradually decreases instead of increasing, which indicates that when the temperature of this position is gradually stable, fewer DOFs can meet the computational accuracy requirements. Based on the above performance, ADOF-FEM effectively takes into account both the computational cost and the computational accuracy requirements.
## Appendix A Calibration Tables.  
Tables A1-A3 show the calibration factor determined for each cell; the average characterising each cell is also provided.  
Figure A1. Force in the \(y\)-axis.  
Figure A2. Force in the \(z\)-axis.  
Figure A3. Force in the \(x\)-axis.  
Figure A4. Force in the \(x\)-axis.  
Figure A5. Force in the \(y\)-axis.  
Figure A6. Force in the \(x\)-axis.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Test 3** & \(\beta\) = 43\({}^{\circ}\) & \(\alpha\) = 25\({}^{\circ}\) & & & & & \\ \hline
**W (N)** & \(\varepsilon_{H}\) (\(\mu\varepsilon\)) & \(\varepsilon_{V}\) (\(\mu\varepsilon\)) & \(\varepsilon_{L}\) (\(\mu\varepsilon\)) & **K (Pa)** & **Fx (N)** & **Fy (N)** & **Fz (N)** & **FR** &
\begin{tabular}{c} **\% of** \\ **Deviation** \\ \end{tabular} \\ \hline
0.981 & 4 & 4 & 4 & 70,958,849,724 & \(-\)0.395 & 0.531 & \(-\)0.632 & 0.915 & 7.17 \\
1.962 & 8 & 8 & 8.5 & 70,958,849,724 & \(-\)0.790 & 1.062 & \(-\)1.344 & 1.886 & 4.02 \\
2.4525 & 10.5 & 10.5 & 11 & 70,958,849,724 & \(-\)1.038 & 1.393 & \(-\)1.739 & 2.458 & 0.23 \\
3.4335 & 15 & 15 & 15 & 70,958,849,724 & \(-\)1.482 & 1.991 & \(-\)2.371 & 3.433 & 0.02 \\
4.4145 & 19 & 19 & 19 & 70,958,849,724 & \(-\)1.877 & 2.522 & \(-\)3.004 & 4.348 & 1.53 \\
4.905 & 21 & 21 & 21 & 70,958,849,724 & \(-\)2.075 & 2.787 & \(-\)3.320 & 4.806 & 2.06 \\
5.886 & 25 & 25 & 26 & 70,958,849,724 & \(-\)2.470 & 3.318 & \(-\)4.111 & 5.832 & 0.93 \\
8.3385 & 36 & 36 & 36.5 & 70,958,849,724 & \(-\)3.557 & 4.778 & \(-\)5.771 & 8.293 & 0.54 \\
9.81 & 42 & 42 & 43 & 70,958,849,724 & \(-\)4.150 & 5.574 & \(-\)6.798 & 9.722 & 0.91 \\
10.791 & 46 & 46 & 48 & 70,958,849,724 & \(-\)4.545 & 6.105 & \(-\)7.589 & 10.748 & 0.40 \\
12.2625 & 53 & 53 & 54 & 70,958,849,724 & \(-\)5.237 & 7.034 & \(-\)8.537 & 12.239 & 0.19 \\
14.715 & 63 & 63 & 64 & 70,958,849,724 & \(-\)6.225 & 8.361 & \(-\)10.118 & 14.527 & 1.29 \\
17.1675 & 74 & 74 & 75 & 70,958,849,724 & \(-\)7.312 & 9.821 & \(-\)11.857 & 17.044 & 0.72 \\
19.62 & 84.5 & 84.5 & 86 & 70,958,849,724 & \(-\)8.350 & 11.214 & \(-\)13.596 & 19.502 & 0.60 \\
22.0725 & 95 & 95 & 97 & 70,958,849,724 & \(-\)9.387 & 12.608 & \(-\)15.336 & 21.960 & 0.51 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental evaluation for an applied force with angles of \(\alpha\) = 25\({}^{\circ}\) and \(\beta\) = 43\({}^{\circ}\).
# A Systematic Experimental and Computational Analysis of Commercially Available Aliphatic Polyesters
## 1 Introduction
Of course, homogeneous and heterogeneous degradation represent asymptotic cases and the observed experimental behavior is often one of the many shades of gray in between. For the sake of clarity, as highlighted by [PERSON] [14], it is necessary to distinguish between degradation and erosion, whose meanings are not equivalent in this regard. Degradation identifies the chain scission process, while erosion is related to mass loss. The main factors that influence degradation kinetics are [15] polymer composition (high content of glycolic acid enhances hydrophilicity and thus hydrolysis), molecular weight (high molecular weight polymers degrade slower), crystallinity (semicrystalline polymers exhibit a slower degradation than amorphous ones), pH, addition of drugs (they can enhance or hinder hydrolysis depending on the specific interactions), plasticizers (they promote water penetration), mechanical stress, sterilization, and the fabrication process. This picture is more complicated in an in vivo environment, due to the contribution of enzymes to the degradation process [16]. The importance of degradation rate (i.e., molecular weight decrease over time) cannot be underestimated, because it contributes to drive the choice of the material and determines the success of a specific device. A bone fixation screw, e.g., must assure suitable mechanical properties during a given time interval that allows a proper bone healing. On the other hand, a rapid adsorption for drug-loaded microparticles can be necessary, in order to limit the duration of foreign body reaction after complete release. The wide availability of materials formulations (in terms, e.g., of copolymer composition and molecular weight) and the long time required by degradation inevitably lead to time-consumingexperimental campaigns. Indeed, although degradation phenomena have been rationalized and well understood, suitable correlations for quantitatively estimating hydrolysis kinetics are still lacking. This work aims at filling this gap through a systematic experimental analysis of commercially available PLGA formulations, which include a range of copolymer composition and molecular weights of practical interest. PLGA has been selected as the material of choice because of its widespread utilization in biomedical field, since its properties can be easily tailored by changing the monomeric unit content of glycolic acid and/or molecular weight. A comprehensive experimental campaign has been devoted to evaluate degradation kinetics as well as their impact on glass transition temperature, copolymer composition, and mass loss. The rationalization of experimental outcomes has been supported by first-principles mathematical modeling, which allows a quantitative evaluation of degradation kinetics. Here, the discussed analysis is thus intended to provide a fundamental baseline, to both academia and industry, for the choice of the optimal material for each specific application.
## 7 Numerical Study
### Network Setting  
In the study, we consider irregular networks that were generated by [38] of three different sizes: small networks with 18 nodes, medium networks with 24 nodes, and large networks with 30 nodes. In each network, the nodes were placed in a given square area according to the uniform distribution. The size of the network area is equal to 220 m \(\times\) 220 m, 250 m \(\times\) 250 m, and 280 m \(\times\) 280 m, respectively, for small, medium, and  
In the study, we assume that the noise power \(\eta\) is equal to \(-101\) dBm and that the network nodes are operating in 5 GHz band. The power that is received at the nodes is calculated by means of the simplified path-loss model from [39]:  
\[p(w,u)=P\cdot G(w,u)=P\Big{(}\frac{\lambda}{4\pi d_{0}}\Big{)}^{2}\Big{(}\frac{d _{0}}{d}\Big{)}^{\alpha}, \tag{10}\]  
where \(p(w,u)\) (as expressed in mW) is the power that is received by node \(u\) when node \(w\) is broadcasting, \(P\) is the transmission power of node \(w\), \(d_{0}\) (in m) is a reference distance, \(d\) (in m) is the distance between \(w\) and \(u\), \(\lambda\) (in m) is the signal wavelength, and \(\alpha\) is the path-loss exponent. In the study, we assume \(d_{0}=10\) m and \(\alpha=4\). As mentioned above, the transceivers are operating in 5 GHz band and, thus, \(\lambda=0.06\) m.  
Clearly, the maximum transmission range depends on the transmission power and the MCS used by the broadcasting node. In the following, we assume that the set of available MCSs \(\mathcal{M}\) is composed of three MCSs: BPSK 3/4, 16-QAM 1/2, and 16-QAM 3/4. The power ratio thresholds \(\gamma(m)\) and transmission rates \(B(m)\) of these MCSs are, respectively, equal to 6.5 dB and 12 Mbps, 12.8 dB and 18 Mbps, and 16.2 dB and 24 Mbps [34]. The maximum transmission range equals 170 m for the assumed MCS and the maximum transmission power considered in this study that is equal to 130 mW.
# SybilHP: Sybil Detection in Directed Social Networks with Adaptive Homophily Prediction
## 4 Methodology
### Initial Homophily Strength Parameters
#### 4.1.2 The Case When Message Sender \(u\) Is a Follower/Tail  
(2) As shown in Figure 3(2), given a benign tail \(u\), from \(u\)'s standpoint, we can assume that \(v\) is also benign with high confidence because most human users hold an inherent discernment to follow human users. We use parameter \(\overrightarrow{w}_{b}\) (\"b\" stands for \"benign\") to represent such initial homophily strength.  
(3) As shown in Figure 3(3), if the tail \(u\) is a Sybil, we assume Sybils are group controlled and share a similar \"following\" pattern [40], that is, the probability that the head \(v\) is also Sybil, which we denote as a homophily strength parameter \(\overrightarrow{w}_{s}\) (\"s\" stands for \"Sybil\").
## 1 Introduction
In addition, a small number of studies have examined the effect of two additional bridging molecules on RET, and the complicated distance and orientational dependences exhibited by the rate [34, 35, 36]. Furthermore, these three-body, free-space QED results have been supplemented by accounting for a dielectric medium using the polariton exchange approach, in which amplitude and phase damping, vibrational relaxation, and dissipative effects on the rate were investigated [24, 37, 38]. Interest in three-center RET has been driven by potential applications involving sensitization and energy pooling in organic systems, as well as up-conversion and down-conversion in rare-Earth doped materials [39, 40, 41], in addition to testing mixed QM/MM methods for treating a solvent [42]. Until now, the problem of RET between two chiral molecules mediated by a third body has not been considered. This is the aim of the present contribution. As in previous studies, the mediator is taken to be achiral, characterized by its electric dipole polarizability. To deal with the potential optical activity of \(D\) and \(A\), the electric dipole approximation is relaxed for these two species, with magnetic dipole and electric quadrupole coupling terms included in the description of the response properties of the emitter and absorber molecules. This will not only allow a comparison to be made with previous work involving RET modified by one or two mediators in the electric dipole approximation, but also with earlier molecular QED calculations of discriminatory pair RET [43, 44]. In these last works, interference between electric and magnetic dipole coupling terms led to a transfer rate that depended on the handedness of the donor and acceptor, changing sign when its mirror image form replaces one enantiomer. It is examined whether such behavior is replicated in the mediated case. The paper is organized as follows: first the problem is set up within the framework of molecular QED theory and basic equations are given. Time-dependent perturbation theory is used to evaluate the matrix element for mediated RET. This is done together in Section 2. Specific higher-order multipole moment-dependent contributions of \(D\) or \(A\) contributing to the oriented or isotropic Fermi golden rule are then extracted in Sections 3 and 4, with special emphasis placed on the term proportional to the handedness of the emitter or absorber in order to elucidate possible discriminatory effects. A summary is given in Section 5.
## 2 Calculation of the Surface of the Drum-Shaped Grinding Tool
### Calculation of the Transverse Face Profile of the Drum-Shaped Grinding Tool  
The instantaneous contact lines \(\mathbf{R}_{s}\) at the different transverse faces of the drum-shaped grinding tool are obtained by connecting the meshing points obtained from solving the meshing equation. The instantaneous contact lines are transformed into the coordinate system \(S_{1}\) of the drum-shaped grinding tool, which is given as:  
\[\mathbf{R}_{s}^{1}=M_{1w}\mathbf{R}_{s} \tag{10}\]  
\[\mathbf{M}_{1w}=\left[\begin{matrix}\cos\varphi_{1}&-\sin\varphi_{1}&0&0\\ \sin\varphi_{1}&\cos\varphi_{1}&0&0\\ 0&0&1&0\\ 0&0&0&1\end{matrix}\right]\]  
where \(\varphi_{1}\) means the rotation angle of the drum-shaped grinding tool.  
The different transverse faces profile curves \(\mathbf{R}_{1}\) of the drum-shaped grinding tool are acquired by rotating and projecting \(\mathbf{R}_{s}^{1}\), which can be represented as:  
\[\mathbf{R}_{1}=\mathbf{M}_{1s}\mathbf{R}_{s}^{1} \tag{11}\]  
\[\mathbf{M}_{1s}=\begin{bmatrix}\cos\delta&-\sin\delta&0&0\\ \sin\delta&\cos\delta&0&0\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix}\]  
where \(\delta\) means the rotation angle of the instantaneous contact lines onto the different transverse faces of the drum-shaped grinding tool, which can be expressed as:  
\[\delta=\frac{Z_{2}}{Z_{1}}(\lambda+\frac{b^{\prime}\cos\sum}{P_{2}}) \tag{12}\]  
Taking the parameters listed in Table 1 as an example, the tooth surface of the drum-shaped grinding tool and internal helical gear are calculated. Figure 5 shows the calculation results of the tooth surfaces of the drum-shaped grinding tool.  
The three-dimensional models of the drum-shaped grinding tool and the internal helical gear are created by importing the obtained points into UG and are assembled according to the preset center distance and shaft angle, as shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Internal Helical Gear** & **Drum-Shaped Grinding Tool** \\ \hline Module(mm) & & 2.75 & \\ No. of teeth & 86 & 20 \\ Pressure angle (\({}^{\circ}\)) & 20 & 20 \\ Helical angle (\({}^{\circ}\)) & 12 & 47 \\ Modification & \(-0.3555\) & 0.3555 \\ Hand of thread & & Right-hand \\ Shaft angle (\({}^{\circ}\)) & & 35 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic parameters of the internal helical gear and the drum-shaped grinding tool.  
Figure 5: tooth surfaces of drum-shaped grinding tool and internal helical gear: (**a**) left tooth surface; (**b**) right tooth surface.
## 6 Results and Analysis
### Test Environment  
Figure 3 shows the test topology of RSCR, namely the GEANT topology. This topology is an international network in Europe used for education and research. GEANT consists of 23 switches and 37 links, with 50% of the links at 10 Gbps, 40% at 2.5 Gbps, and 10% at 155 Mbps. We deployed the mentioned GEANT topology in Mininet 2.3.1, with each switch connected to a host responsible for forwarding and receiving traffic. As Mininet's virtual hosts cannot effectively provide real and valid computing resource information, the computing resource data were based on real-time data from a Kubernetes cluster.  
The data plane of RSCR is composed of the GEANT topology. In the control plane, a Ryu controller is deployed, and topology discovery, link-state perception, and flow table installation are all developed based on this controller. The management plane involves data processing using Python 3.9 with Pandas 1.5.3 and Numpy 1.24.3 libraries. The knowledge plane, developed using Python 3.9 and RL, is responsible for computing algorithmic routing decisions. We store link-state information in the CSV format and the computed routing results in the JSON format. The experimental environment is de an 11 th Gen Intel(R) Core(TM) i7-1165G7 processor and 16 GB RAM running Ubuntu Desktop 20.04.
## 3 Results and Discussion
### Antioxidant Activity of Yogurts during 21 Days of Storage
In their study, [PERSON] and [PERSON] [71] used Neem (_Azadirachta indica_) leaf extracts as an additive and noted an increase in the free radical scavenging activity (DPPH) of manufactured yogurts versus traditional yogurts until storage day 14, followed by a decline in the activity. In another study conducted by [PERSON] [52], yogurts supplemented with aqueous extracts of rosemary, dill, and oregano exhibited significantly higher (_p_ < 0.05) activity (61.15 \(\pm\) 1.2, 58.92 \(\pm\) 1.3, and 66.97 \(\pm\) 0.7 \(\upmu\)g GAE/mL, respectively) than the control (34.79 \(\pm\) 1.0 \(\upmu\)g GAE/mL). During the 21-day storage period, the values decreased significantly (_p_ < 0.05) in both herb-enriched and control groups [48]. [PERSON] et al. [53] found that the antioxidant activity of yogurts supplemented with an aqueous solution of fennel seeds increased significantly (_p_ < 0.05) in comparison with natural yogurt samples. In turn, a study conducted by [PERSON] and [PERSON] [51] showed that herbal yogurts had higher (_p_ < 0.05) antioxidant activity (DPPH) than natural samples throughout the storage period, but the activity gradually declined between storage days 7 and 28. [PERSON] et al. [41] reported a significant increase in the DPPH and ABTS values during the first two weeks of storage of kefir made from cow milk and a decrease in these activities in the third week of storage. [PERSON] et al. [50] observed higher (_p_ < 0.001) values of antioxidant activity (FRAP) in morning fruit-enriched yogurts. A significant increase in the FRAP value was noted during 28 days of storage, but the activity declined in the consecutive weeks. Many authors suggest that the high oxidative stability of yogurt during the first storage weeks is associated with the release of antioxidant peptides during milk fermentation [3; 11; 68]. Figure 4: _Cont._
## 3 Results
### Self-Calibration  
The self-calibration of LRNTS is performed as an iterative adjustment using the model elaborated in Section 3.3. Input parameters, in the sense of a priori quality characteristics and accuracy, are chosen by adapting standard error budget for GNSS positioning [68], adopting 300 cm for coarse-code code range accuracy and 5 mm for phase ranges. Multipath and atmospheric influences are neglected here. In addition, receiver clock bias is not treated in this study, because it refers to receiver crystal clocks, while all LRNTS transceivers hold atomic clocks aboard and use the same timescale for transmitting and receiving. The adjustment was performed adopting a 100 m spatial error of approximate LRNTS transceiver positions, resulting from DEM. The adjustment process converged after the third iteration in all tested models.  
Table 2 summarizes the obtained results for all 12 tested models. Columns 2 to 7 contain minimum and maximum standard deviations of the points' spatial (3D), horizontal (2D), and vertical (1D) positions, respectively, which is further used for analyzing performance of the network geometry.
## 3 Non-Isolated Single Tree Radio Channel Characterization
### D Ray Launching Simulations  
The specific scenario under analysis is shown in Figure 12a, and it corresponds to the zoomed area shown in Figure 10 (i.e., measurements zone). The scenario has been created for simulations by the 3D Ray Launching tool. All the materials as well as the real distances and sizes of the surrounding trees have been taken into account in order to obtain accurate estimations of the RF power distribution and therefore, the path loss. Both the material properties and simulation parameters are the same as those used for the isolated tree case, and can be seen in Tables 1 and 2, except the transmitted power, which in this case was 8.35 dBm.  
Figure 10: Location of the Orgi Forest and its different zones, including the measurement zone (zoomed). Extracted from Google maps.  
Figure 11: Picture of _Tomaszelaiteta_ zone within the Orgi Forest.  
Figure 12: (**a**) Picture of the measurements zone. (**b**) The scenario created for the simulations. (**c**) Detail of the position of the dense vegetation wall.  
Figure 12b shows the schematic view of the created scenario for simulation purposes. As can be seen, the morphology and size of each of the single trees surrounding the tree under analysis have been taken into account. In the same way, a specific wall has been created in the scenario in order to model the thick in-leaf trees background that exists behind the tree under analysis (the wall in green in Figure 12c). This wall has been defined with the material Foliage (see Table 1), while the rest have been defined as air.