esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
In this section, we examine topside Ne latitudinal profiles in the vicinity of midlatitude SF events. As detailed in Section 2, we identified SF Digisonde ionograms (Table 1) only when corresponding Swarm satellite tracks were available. From 2015 to 2017, a period characterized by moderate to low solar activity, we identified ten such instances where multiple Digisonde stations recorded SF effects. Out of these ten cases, seven involved Swarm B, while in the remaining three cases, Swarm A and C were involved. Notably, the irregularities in Swarm A-C profiles were restricted within the time window from 02:00 to 04:00 UT. Similar findings were reported by [PERSON] et al. 2022 [43], who observed that midlatitude topside ionospheric irregularities predominantly occur at the Swarm B altitude range of Swarm B. Their study concluded that under low solar activity, Swarm B recorded a lower overall occurrence rate of irregularities at both low and high latitudes but noted a slight increase at midlatitudes compared to Swarm A-C, suggesting a higher occurrence of midlatitude irregularities at the altitude of Swarm B.  
In Section 3, we provide a discussion of the irregularity features observed in both the topside and bottomside ionosphere, focusing on SF events and associated Swarm Ne fluctuations. Furthermore, we investigate the correlation between SF and Ne fluctuations to explore MSTID and ROTI activity around SF events.
## 3 Soil Samples and Water Retention Characteristics
### Water Retention Characteristics
Thus, the unsaturated hydraulic conductivities (\(k_{w}\)) of two samples were derived from the relationship between the saturated hydraulic conductivity (\(k_{sat}\)) and the relative hydraulic conductivity function (\(k_{r}\)). Figure 3b shows the results of the hydraulic conductivities of Toyoura sand and silica No. 1. It was found that the hydraulic conductivity of Toyoura sand is larger for the suction value of about 0.07 kPa or more, which is the intersection of the two results. This difference can also be understood in relation to the CB system's capability of retaining infiltrated water. \[k_{r}=\Theta^{\text{f}}(\psi)\frac{\int_{\ln(\psi)}^{b}\frac{\theta(e^{\prime} )-\theta(\psi)}{e^{\prime}}\theta^{\prime}(e^{\prime y})dy}{\int_{\ln(\psi_{ av})}^{b}\frac{\theta(e^{\prime})-\theta_{s}}{e^{\prime}}\theta^{\prime}(e^{ \prime y})dy} \tag{5}\]  
\[k_{w}=k_{r}\cdot k_{sat} \tag{6}\] \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Soil Sample** & \(\theta_{s}\) & **AEV** & **EX** & \(a\) & \(n\) & \(m\) \\ \hline Toyoura sand & 0.432 & 2.45 & - & 2.80 & 10.02 & 0.75 \\ Silica sand No. 1 & 0.389 & - & 0.05 & 0.39 & 0.015 & 0.07 \\ \hline \hline \end{tabular} Note: AEV is the air entry value (kPa), EX is the air expulsion value, and \(\theta_{s}\) is the saturated volumetric water content.  
\end{table} 
Table 2: Fitting parameters for the SWCCs. Figure 3: SWCCs and hydraulic conductivities of Toyoura sand and silica sand No. 1: (**a**) SWCCs; (**b**) hydraulic conductivities. where \(k_{r}\) is the relative hydraulic conductivity, \(k_{sat}\) is the saturated hydraulic conductivity, \(\Theta^{q}(\psi)\) is a correction factor (1.0), \(b\) is a constant of ln(10\({}^{6}\)), \(\theta^{\prime}\) is the derivative of Equation (4), \(y\) is a dummy variable of integration representing suction, \(\psi_{ave}\) is the AEV of the soil, and \(\theta_{s}\) is the saturated volumetric water content.
## 2 Vehicle State Estimation
### The Design of the State Estimator  
Combining the information of GPS and IMU, the estimation system is described as:  
\[\left\{\begin{array}{l}\mathbf{u}=\left\{u_{1},u_{2},u_{3}\right\}^{T}=\left\{a _{x},a_{y},M_{c,z}\right\}^{T}\\ \mathbf{x}=\left\{x_{1},x_{2},x_{3},x_{4},x_{5},x_{6}\right\}^{T}=\left\{X,Y,\psi, \omega,v_{x},v_{y}\right\}^{T}\\ \mathbf{z}=\left\{z_{1},z_{2},z_{3},z_{4}\right\}^{T}=\left\{\widetilde{X}_{\text{GPS}}, \widetilde{Y}_{\text{GPS}},\widetilde{\varphi}_{\text{GPS}},\widetilde{ \omega}_{\text{IMU}}\right\}^{T}\end{array}\right. \tag{3}\]  
where \(a_{x}\), \(a_{y}\) and \(\widetilde{\omega}_{\text{IMU}}\) are measured by IMU; \(M_{c,z}\) is from the tire-force estimation module; \(\widetilde{X}_{\text{GPS}}\), \(\widetilde{Y}_{\text{GPS}}\) and \(\widetilde{\varphi}_{\text{GPS}}\) are based on GPS.  
The state formulations are regulated as:  
\[\left\{\begin{array}{l}\dot{X}=v_{x}\ cos\ \varphi-v_{y}\ sin\ \varphi+w_{1}\\ \dot{Y}=v_{x}\ sin\ \varphi+v_{y}\ cos\ \varphi+w_{2}\\ \dot{\psi}=\omega+w_{3}\\ \dot{\omega}=\frac{M_{c,z}}{z}+w_{4}\\ \dot{v}_{x}=a_{x}+\omega v_{y}+w_{5}\\ \dot{v}_{y}=a_{y}-\omega v_{x}+w_{6}\\ \Rightarrow\ \dot{\mathbf{x}}(t)=f_{c}(\mathbf{x}(t),\mathbf{u}(t))+\mathbf{W}(t)\end{array}\right. \tag{4}\]  
where \(w_{i}(i=1,2,\dots,6)\) represents the error of each state variable based on the estimation of (4).  
The observation formulations are regulated as:  
\[\left\{\begin{array}{l}z_{1}=x_{1}+v_{1}\\ z_{2}=x_{2}+v_{2}\\ z_{3}=x_{3}+v_{3}\\ z_{4}=x_{4}+v_{4}\\ \end{array}\right. \tag{5}\] \[\Rightarrow\mathbf{z}(t)=h(\mathbf{x}(t),\mathbf{u}(t))+\mathbf{V}(t)\]  
where \(v_{i}\ (i=1,2,3,4)\) represents the error of each observation variable based on the measurement of (5).
## 2 Deep Neural Networks for Inverse Kinematics
### Deep Reinforcement Learning  
Deep Reinforcement Learning (DRL) combines reinforcement learning (RL) [77] and deep learning. RL deals with the problem of learning through interaction by trial and error, allowing the agent to make decisions from unstructured data by trying to maximize the reward received for its actions on each state. DRL techniques aim to solve RL via DNNs and have a clear advantage over supervised learning in that they do not require a previously generated labeled training dataset, which reduces the time and computation cost. Compared to unsupervised learning, DRL can be architecturally simpler and less sensitive to hyperparameters. Additionally, they have a notion of objective via the reward signal, which can explicitly map to reaching a target and/or attempt to enforce a certain degree of collision avoidance.  
DRL is used in [78] to solve the IK of a high-DOF robotic arm using the Deep Q-Network (DQN) algorithm resulting in a maximum error in end-effector position of 1 mm. In [79], Deep Deterministic Policy Gradient (DDPG) is used to solve the problem, at the same time focusing in possible issues with data privacy. A modified version of said algorithm is used in [80]. In [81] the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm is successfully applied to the problem, overcoming the shortcomings of traditional methods and learning autonomously based on the environment. An actor-critic multi-agent approach is followed in [82] where each joint is a separate agent. The results are compared to standard Jacobian-based and neural network methods and proved to converge faster. DDPG, Twin Delayed DDPG (TD3) and Soft Actor-Critic (SAC) are compared in [83], with TD3 obtaining the best results. The approach followed in this reference is to integrate the manipulator's forward kinematics model into the control framework and conceptualize the control task as a Markov decision process.
# Adaptive Neural Network Control of Zero-Speed Vessel Fin Stabilizer Based on Command Filter
## 4 Simulation
In Figure 1c, the curve of the estimated disturbance \(\hat{e}\), \(\hat{e}^{*}\) and the actual disturbance \(e\) almost completely coincide, indicating that both control strategies have good estimation performance for unknown disturbances. Figure 1d is the fin angular velocity \(\omega\) and \(\omega^{*}\). From Figure 1e, it is known that at the beginning, in order to resist the large angle rolling of the vessel, a large roll stability force was generated. When the vessel tends to be stable, the roll stability force \(F_{z}\) and \(F_{z}^{*}\) also tend to be stable. Figure 1f shows the trajectory of the estimate of control gain \(\hat{b}\) and the estimate of control gain reciprocal \(\hat{\beta}\). From the graph, we can see that the value of the control gain is around 0.03 and the value of the inverse of the control gain is around 34. Based on the above simulation results, the zero-speed vessel fin stabilizer control system designed in this paper can quickly reduce the roll angle and restore the vessel to a stable state when large angle rolling occurs; therefore, it has good control performance. To further verify the effectiveness of the proposed scheme, a simulation comparison with the conventional PID is carried out under two different ocean conditions. In case 1, the ocean disturbance is set as \(e=0.1 sin(0.5t)-0.11 cos(0.35t)+0.2 sin(0.4t)\); in case 2, the ocean disturbance is set as \(e=0.5 sin(0.5t)-1.1 cos(0.35t)+2 sin(0.4t)\). In simulation, the control gains of PID are set as \(k_{p}=10\), \(k_{i}=1\), \(k_{d}=10000\). The simulation results are shown in Figure 2.  
From Figure 2a, it can be seen that the control performance under two control laws is almost identical, which indicates that the conventional PID can also ensure the stability of the closed-loop control system and achieve satisfying control performance. However, Figure 2b shows that the control performance degenerates when the ocean disturbance is magnified, and that the proposed control scheme in this work shows the advantage of robustness and adaptive ability. The simulation comparison indicates that, compared with the conventional PID, the proposed adaptive NN-based disturbance observer control scheme is of strong robustness against the system dynamic and external disturbance, which attributes to the adaptive NN and disturbance observer techniques. Due to the adaptive ability, the proposed control scheme does not need to adjust the control gains repeatedly. Such an advantage is absent in the conventional PID, which needs to repeatedly adjust gain to restrain the time-vary ocean environmental, since it is not endowed with the adaptive ability.
# Survivability Prediction of Open Source Software with Polynomial Regression
## 2 Background
### Polynomial Regression
#### 2.2.3 Evaluation Metrics  
When evaluating the prediction accuracy of regression models, the RSE or RMSE can be used to assess the performance of the model. The RSE is an estimate of the standard deviation of the residuals (errors) in the regression model. It quantifies the model's lack of fit to the actual data points. Lower RSE values indicate a better model performance. The RSE can be calculated using the following Equation (3), where \(n\) is the number of data points, \(p\) is the number of independent variables used in the model, \(y_{i}\) represents the actual observed values, and \(\hat{y}_{i}\) represents the predicted values by the model.  
\[\text{RSE}=\sqrt{\frac{1}{n-p-1}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{3}\]  
RMSE is an indicator used to measure the prediction error of a regression model, which is the square root of the average of the squared differences between the predicted values and the actual values. It is the square root of MSE (Mean Squared Error). While MSE averages the squares of errors, potentially overestimating the magnitude of errors, RMSE adjusts this by bringing the error magnitude back to its original units, making it more intuitive to understand. RMSE is calculated through Equation (4), where \(y_{i}\) are the actual values, \(\hat{y}_{i}\) are the predicted values, and \(n\) is the number of observations.  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{4}\]  
We evaluate the polynomial regression model using these two metrics: RSE assesses how well the model has learned from the training data, and RMSE checks the model's accuracy on the test data. Since a model can still perform poorly if not properly trained, we use both RSE to confirm the model's fit and RMSE to verify its accuracy simultaneously.
## 3 Performance Analysis
### Computational Complexity  
The assessment of the computational complexity is dependent on the number of multiplication operations for spreading/de-spreading during one symbol duration [35; 36; 37; 38]. In the source node S of the proposed system, \(NJ\times U\) multiplication operations are required. In the relay node R of the proposed system, \(NJ\times U+N(N+3)\times U\) multiplication opera  
Figure 5: EE comparisons with the systems.  
Figure 6: Data rate comparisons with the systems.  
tions are necessary. In the destination node D of the proposed system, \(NJ\times U+2 NQ\times U\) multiplication operations are needed. Therefore, the computational complexity of the proposed system is \(3 NJ\times U+N(N+3)\times U+2 NQ\times U\). Likewise, the computational complexity of CIM-SR-DCSK-CC is \(3J\times U+2\times U+Q\times U\). Obviously, the proposed system shows higher computational complexity than the CIM-SR-DCSK-CC system.
## 3 The Mathematical Model of the Single-Phase FRM  
The three-phase FRM is composed of three single-phase FRMs in which the stator magnetic cores are separated from each other by the end winding parts, as well as insulation. Therefore, the electromagnetic coupling between the phases of the three-phase FRM can be considered as negligible.  
A detailed description of the mathematical model for the high-torque single-phase FRM powered by piecewise constant voltage is disclosed in [8]. The magnetic flux at different rotor positions is determined by integrating the voltage over time. Then, the 2D magnetostatic boundary problems are solved only for the actual pairs of the rotor position and the flux. Thanks to symmetry, the computational domain is reduced to a quarter. The computational domain is shown schematically in Figure 3. Boundaries \(I\) and \(II\) are joined with an aperiodic boundary condition. The boundary condition joining Boundary \(III\) takes into account the rotor position. In addition, the magnetostatic boundary problems can be solved only for the rotor positions in the range from 0 to 90\({}^{\circ}\), which corresponds to the electric period.  
In this paper, the mathematical modelling of the high-speed FRM is based on assuming a sinusoidal current waveform. Therefore, the 2D magnetostatic boundary value problems are solved for the current position pairs (Figure 4).  
Figure 3: The design of three-phase high-speed FRM.  
Figure 2: The three-phase FRM geometry according to [6].  
In this paper and in [8], the magnetic flux \(\Phi\) and current density \(J\) are defined by the following expressions:  
\[\Phi=\frac{4 LN}{S}\iint AdS \tag{1}\]  
\[J=\frac{IN}{S} \tag{2}\]  
where \(A\) is the \(z\)-component of the vector potential, \(L\) is the stack length of the magnetic cores, the integer \(4\) is used to account for the reduction of the computational domain, \(N\) is the number of turns in the slot, \(S\) is the slot area, and \(I\) is the current. The integration is carried out over the slot area.  
Although in this paper and in [8], the same calculation area is used and the magnetostatic boundary problems are solved for the rotor positions of the same range, Equations (1) and (2) are treated differently in this paper compared to [8].  
In [8], since the flux \(\Phi\) is found by integrating the piecewise constant voltage and is considered to be given by solving the magnetostatic boundary problem for each rotor position, Equation (1) is treated as an additional equation, and the current defining the current density according to Equation (2) is taken as an additional variable.  
In contrast to [8], herein the mathematical model is simplified by the fact that the currents are assumed to be predetermined and sinusoidal, and additional equations are not required. After solving the boundary value problem with the current density given Equation (2) for each rotor position, the fluxes \(\Phi\) can be determined using the expression (1). Other aspects of the mathematical model, such as calculating the instantaneous torque of the single-phase FRM as well as the core and winding losses, are described in [8].
# The Influence of Friction and Twisting Angle on the Tensile Strength of Polypropylene Baling Twine
###### Abstract  
Polypropylene is a widely used linear hydrocarbon polymer with diverse applications due to its exceptional physicochemical characteristics and minimal changes during the recycling process. Numerous studies have focused on factors influencing the mechanical properties of polypropylene and its application in composites. However, despite their significance in the agricultural industry, there is limited research on polypropylene baling twines. This study analyses the behaviour of polypropylene baling twine under tensile loading, exploring the influence of fibre friction and twisting angle on the material's tensile strength. Experimental investigation indicated that tensile strength increases with twisting angle, but only until the angle reaches a critical value. Further increase in the twist angle led to a decrease in tensile strength. The increase in tensile strength is attributed to the rise in the coefficient of friction between fibres in the twine. An experimental approach was employed to evaluate the mechanical characteristics of the twine, including the effect of prestressing by twisting. Understanding these characteristics is crucial for enhancing the quality of polypropylene baling twines and optimising their application in the agricultural industry.  
baling twine; tensile strength; fibre friction; twisting angle +
Footnote †: journal: Applied sciences
# Data-Driven Decision Support in SaaS Cloud-Based Service Models
## 3 Research Methodology  
The research questions that we aim to answer in this survey are the following:  
* RQI: What is the main focus of the papers in the area of data-driven methods for supporting decision-making in SaaS?
* RQI: What kind of data is utilized?
* RQI: Are machine learning methods employed and how effective are they?
* RQI: How are the results presented to SaaS providers to support decision-making?  
The survey was conducted following the PRISMA-ScR methodology. The research was conducted using the Scopus and Scholar bibliographic databases that contain papers from libraries like Springer, Elsevier, IEEE, etc. The following query was posed on 5 March 2025 to Scopus:  
(\"web usage mining\" OR \"customer segmentation\" OR \"log analysis\" OR \"churn\" OR \"user engagement\" OR \"customer lifetime value\" OR \"user lifetime value\" OR \"user segmentation\") AND \"SaaS\" AND PUBYEAR > 2013 AND PUBYEAR < 2026.  
This query returned 426 results. Screening was performed by reading the title and abstract in order to identify the completely irrelevant ones. This process resulted in 147 papers. These papers were searched and retrieved in order to read them thoroughly. During this second screening phase, there were excluded all papers that were not relevant to our subject. Relevancy was based on three main rules: papers should be about SaaS, decision-support and based on data. Thus, the excluded papers of the second phase fall into one of the three categories:  
* Not related to SaaS;
* Although related to SaaS they were not about supporting decisions;
* Not data-driven, theoretical.  
This process resulted in 22 papers that were included in our survey. A supplementary search was conducted in Google Scholar using keywords like \"churn prediction SaaS\", \"user engagement SaaS\" and \"user lifetime value SaaS\" resulting in 6 more papers. Thus, a final list of 28 papers was formed that was analyzed further. Figure 1 illustrates the research process followed.  
At this stage, it is important to acknowledge the limitations of this research. The research was limited from 2014 to 2025, so as to cover a full range of 10 years, as well as due to the fact that SaaS adoption began to rise mainly after 2014, where the main volume of literature was located. However, the latter did not affect our research findings, since the main volume of the research was located after 2014 and especially from 2020 onwards. Moreover, the database coverage was limited to Scopus and Google Scholar. While other databases also exist, such as Web of Science or PubMed, the use of both Scopus and Google Scholar could improve coverage and reliability, as they both cover a wide range of peer-reviewed sources.
## 2 Experimental Section
### Methods
#### 2.2.9 Attenuated Total Reflectance Infrared (ATR-FTIR) Spectroscopy Studies  
The interaction between the sodium alginate based transdermal films of MTC and the skin were investigated using ATR-FTIR spectroscopy. ATR-FTIR spectroscopy has been widely employed to report on _stratum corneum_ intercellular lipid ordering through the C-H stretching absorbances from the methylene groups of the lipid acyl chains [33, 34]. The spectrographs of skin obtained from ATR-FTIR studies provide information about the influence of chemical penetration enhancers on reduced order of skin lipids and proteins [20]. For this purpose in our study the subcutaneous fat tissue of excised pig skin was removed with a scalpel and the hairs were clipped with hair clippers. The skin was stored frozen at \(-25\ ^{\circ}\mathrm{C}\) (wrapped in Parafilm\({}^{\circledR}\) and packed in ZipLock bags) for not longer than 2 months prior to use. Before collection of ATR-FTIR spectra, samples were defrosted 8 h at 4 \({}^{\circ}\mathrm{C}\). After defrosting, skin surface was wiped two times with wet cotton swab and excess water was gently dried with paper tissue. Transdermal films of MTC were applied immediately on the surface of the skin and kept for 3 h at 25 \({}^{\circ}\mathrm{C}\) under non-occlusive conditions. After the treatment period, excess of formulation was gently blotted away from the surface, then spectra were collected with a spectral resolution of 4 cm\({}^{-1}\) in the 4000-650 cm\({}^{-1}\) range using a Perkin Elmer Spectrum 100 FT-IR spectrometer (Cambridge, UK) equipped with a diamond ATR crystal. Treated skin, cut to dimensions 2 cm \(\times\) 2 cm, was placed _stratum corneum_ side down onto the ATR crystal. To ensure reproducible contact between the sample and the crystal, always the same pressure on top of samples was applied (force gauge 80 N). Attention was focused on detecting the shifts of the symmetric (SSV) C-H stretching bands near 2850 cm\({}^{-1}\). For each treatment, peak height and area was measured and a non-treated skin served as control. In order to evaluate the effects of penetration enhancers, a control film was also prepared following the same method and components without adding penetration enhancer (TF-CONT). Spectrum Version 6.0.2 software (Perkin Elmer, Cambridge, UK) was used for calculations with \(\pm 0.1\ \mathrm{cm}^{-1}\) accuracy.