esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 STATCOM Performance and Results
### Continuous Loads  
Figure 3 shows the performance results of the STATCOM at various dynamic conditions of continuous load. Initially, the reactive power variation was set to '0' for 0.2 s and the performance was observed. Then, the reference reactive power is varied from 0 to 100 MVAR and the time interval is between 0.2 s and 0.4 s. The STATCOM performance was observed during this time period and the reactive power flows to the STATCOM side, then the AC output voltage and DC-Link voltage were adopted to the voltage losses. After the reactive power is set to '0' during the time interval 0.4 s to 0.6 s, STATCOM performance results were analyzed during the time interval from 0.4 s to 06 s by presetting the reactive power to '0'. The reference reactive power was varied from 0 to \(-\)100 MVAR for the time interval between 0.6 s and 0.8 s. The STATCOM performance was observed during this time period and the reactive power flowed from the STATCOM side; then, the AC output voltage and DC-Link voltage are added to the voltage losses. After the reactive power was set to '0' during the time interval 0.8 s to 1.0 s, the STATCOM performance results were given, showing the improvement of the smooth steady-state system with low harmonic distortion.  
Figure 4 shows the seven level STATCOM harmonic currents at inductive and capacitive operations of continuous loads. The THD was 3.83%, observed at inductive operation of continuous load, (i.e., the reference reactive power was changed to 100 MVAR). In addition, the THD was 5.10%, observed at the capacitive operation of continuous load, (i.e., the reference reactive power was changed to \(-\)100 MVAR).  
Figure 3: Continuous load performance results of seven level VSC based STATCOM with reference reactive power changes.
###### Abstract  
The naval multi-function console provides various types of information to the operator. It is equipment that is key for submarine navigation, and fatal human errors can occur due to the mismatch between the console specifications and the operator's body size. This study proposes a method for deriving console specifications suitable for the body size of Korean users. The seat height, seat width, seat depth, upper edge of backrest, and worktable height were selected as the target design variables. Using six anthropometric dimensions, a mismatch equation for each target design variable was developed. Anthropometric measures of 2027 Korean males were obtained, and the optimal specifications of the console were derived via an algorithmic approach. As a result, the match rate, considering all the target design variables, was improved from 2.57% to 76.96%. In previous studies and standards, the optimal console specifications were suggested based on the anthropometric data of a specific percentile of users, and it was impossible to quantitatively confirm the suitability of the console design for the target users. However, the method used in this study calculated the match rate using the mismatch equation devised for comfortable use of the console and a large amount of anthropometric data that represented the user population, and therefore the improvement effect of the recommended specification can be directly identified when compared to the current specifications. Moreover, the methodology and results of this study could be used for deciding the specifications of multi-function consoles in several fields, including nuclear power plants or disaster situation rooms.  
multi-function console; data-driven design; mismatch equation; anthropometric measures; algorithmic approach; optimal design +
Footnote †: journal: Applied Sciences
## 5 Numerical Results
Based on the value of \(\eta\), the value of \(\tilde{\chi}_{q}^{2}\) can be calculated according to [20], with other simulation parameters set to \(\xi_{p}=4.5856\)  
Figure 4: ABER versus SJR (dB) curves for an FSO system with a varying \(\rho\) with considering additive Gaussian noise in Scenario 2. Figure 5: Comparison of ABER between the two scenarios under varying turbulence parameters. Figure 3: ABER versus SJR (dB) curves for an FSO system with a varying \(\rho\) with considering additive Gaussian noise in Scenario 1. \(\alpha_{p}=\alpha_{q}=5.4\), \(\beta_{p}=\beta_{q}=3.77\), and \(\rho=0.5\). As \(\eta\) decreases, it indicates that the RIS is relatively closer to the source node than the destination node. From Figures 6 and 7, it can be observed that a smaller value of \(\eta\) leads to better system ABER performance. Therefore, it should be positioned as close to the source node as possible. Furthermore, it is noted that under weak turbulence with jamming present, the adoption of SIMO technology does not effectively enhance the ABER in Scenario 1, as depicted in Figure 6. However, in Scenario 2, SIMO notably improves the ABER, as shown in Figure 7. This phenomenon is attributed to the minimal channel attenuation in weak turbulence conditions, where the introduction of multiple additive Gaussian noises in SIMO results in an increase in the ABER. Therefore, it is not recommended to employ SIMO technology to mitigate jamming in Scenario 1 under weak turbulence conditions. Computational complexity analysis: From (45), and (51), it can be concluded that the computational complexity of ABER using SIMO is \(O\big{(}N^{3}\big{)}\). When N is large, the computational complexity of (45), and (51) is high. When N is small, the complexity is relatively small. Since SIMO is typically employed in FSO with 2 receiving apertures [24], our proposed ABER calculation can be effectively applied to FSO systems. Figure 6: Analysis of ABER for different RIS positions in Scenario 1. Figure 7: Analysis of ABER for different RIS positions in Scenario 2.
# Sustainability in Bio-Based Edible Films, Coatings, and Packaging for Small Fruits
## 5 Edible Film and Coating Functionalities Important for the Small Fruit Industry
### Bioactivity of the Edible Films and Coatings
#### 5.1.2 Antioxidant Properties
In recent years, numerous researchers have explored specific food by-products and residues with potential applications as components in biodegradable and edible films for preserving fruits and vegetables [168]. Adding a natural antioxidant extract from bergamot pomace (_Citrus bergamia_ Risso) and bergamot essential oil in the coating obtained by [PERSON] et al. [137] contributed to achieving an antioxidant activity preserving strawberries' quality. Using the ABTS assay, the measured antioxidant capacity was higher than the DPPH assay. During fruit storage, the total antioxidant activity generally increased, with the most notable rises observed in samples containing 100 ppm BHT, both 1% and 5% bergamot pomace extract. In [PERSON] et al. [169] research, pomegranate peel extract of varying concentrations was incorporated into jackfruit seed starch-based edible films and coatings to evaluate their antioxidant activity. Moreover, the effect of the optimized edible coatings was investigated on the postharvest shelf life of white grapes. The results showed a significant increase in the prepared edible films' antioxidant activity (DPPH assay) but decreased water vapor permeability. Studying the antioxidant properties of edible coating and films is still a largely understudied field, and adding different compounds to these films (Figure 7) can significantly improve those properties.
## 2 Problem Description
### Constraints  
The following constraints are established based on the symbols and assumptions:  
Constraint (1) guarantees that each task is scheduled within its designated transmission window:  
\[\forall i:st_{t_{j}}\leq stw_{i}<etw_{i}\leq et_{t_{i}} \tag{1}\]  
Constraint (2) ensures that each task is scheduled within the visibility time windows between ground stations and satellites:  
\[\forall i,j,k,z:\left(\chi_{ikj}=1\right)\cap\left(\xi_{ikj}=1\right)\cap \left(sw_{jk}^{z}\leq stw_{i}<etw_{i}\leq ew_{jk}^{z}\right) \tag{2}\]  
Constraint (3) indicates that there should be a correspondence in the frequency bands between the satellite and ground station antenna:  
\[\forall i,j,k,d,z:\left(\chi_{ikj}=1\right)\cap\left(\xi_{ikj}=1\right)\cap \left(af_{sjm}=f_{t_{i}}\right)\cap\left(af_{gbd}=f_{t_{i}}\right) \tag{3}\]  
Constraint (4) indicates that a single ground station antenna cannot handle multiple tasks concurrently:  
\[\forall i,j,k,\exists i_{1},i_{2}(i_{1}\
eq i_{2}):\left(\chi_{i1 kj}\cdot \chi_{i2 kj}=0\right)\cup\left(TW_{i_{1}}\cap TW_{i_{2}}=\varnothing\right) \tag{4}\]  
Constraint (5) guarantees that each available task is allocated to no more than one ground station antenna:  
\[\forall i,j,k,d,z;\ \exists k_{1},k_{2},\ j_{1},j_{2}((k_{1}\
eq k_{2})\cup(d _{1}\
eq d_{2})):\chi_{ikj1}+\chi_{k2j2}\leq 1 \tag{5}\]Constraint (6) ensures that each task's allocated transmission time slot meets the minimum transmission duration criteria:  
\[\forall i:\mathit{d}ur_{t_{i}}\leq\mathit{etw}_{i}-\mathit{stw}_{i} \tag{6}\]  
Constraint (7) specifies that each task is scheduled at most once:  
\[\forall i,j,\ k:\sum_{i}^{t_{i}}\chi_{ijk}:\xi_{ijk}\ \leq 1 \tag{7}\]  
Constraint (8) guarantees that the interval between two tasks should be sufficient to accommodate the antenna transition time. \(C\) is a constant:  
\[\forall i_{1},\ i_{2},\ j_{1},j_{2},m_{1},m_{2},z_{1},z_{2},k,d:\left(\chi_{ i1 bj}\cdot\chi_{i2b2j}=1\right)\cap\left[max\big{(}stw_{i_{1}},stw_{i_{2}}\big{)}- min\big{(}etw_{i_{1}},etw_{i_{2}}\big{)}\geq C\right] \tag{8}\]  
Constraints (9) and (10) define the scope of values for the choice variables:  
\[\forall i,j,m,k,d,z:x_{ijmkd}^{2}\in\{0,1\} \tag{9}\]  
\[\forall i:stw_{i},etw_{i}\geq 0 \tag{10}\]
## 2 Related Research
### LEACH Protocol for WSN
#### 2.1.2 Steady-State Phase  
In the steady-state phases (or data transmission phase), each normal node collects environmental data and sends its collected data to its designated CH during its assigned TDMA slot schedule to conserve energy and reduce interference. This stage is composed of numerous frames, and it is much longer than the setup phase. The CH then aggregates the received data from all member nodes and transmits it to the BS (or sink node) using Code Division Multiple Access (CDMA) to avoid the collision.
# Creep Model of Weakly Cemented Soft Rock Considering Damage and Secondary Development in FLAC3D
## 1 Introduction
Furthermore, beyond finite volume-based models such as FLAC3D, the Lattice Element Method (LEM) and Finite Element Method (FEM) offer powerful alternatives for simulating microcrack evolution and time-dependent damage [27]. The LEM is particularly effective for modeling fracture processes at the mesoscale, while the FEM has been widely applied to simulate nonlinear creep and viscoelastic behavior in geomaterials [28; 29]. These methods complement damage-based continuum models by incorporating microstructural failure mechanisms. Recent studies (e.g., [12; 30; 31; 32]) have advanced creep modeling by integrating fractional calculus, energy-based damage theory, and advanced numerical simulations, offering valuable insights into deformation. In creep experiments, the mechanical properties of rock exhibit progressive degradation, with strength reduction resulting from damage, which manifests as parameter weakening in creep models [33; 34; 35; 36]. To characterize this degradation, researchers have introduced damage theory and quantified rock mass deterioration using multiple damage factors [37; 38]. [PERSON] et al. [39] incorporated time-dependent damage variables reflecting the degradation of mechanical properties during creep and developed a damage-based creep constitutive model capable of capturing the full creep process of hard rock. [PERSON] et al. [40] introduced an Abel dashpot and damage variables to establish a thermomechanical coupling-damaged Abel dashpot and proposed a three-dimensional fractional creep model for deep coal masses considering the effects of thermomechanical coupling damage. The validity and applicability of these models were successfully verified. Several researchers have also proposed models integrating nonlinear elements to better represent the accelerated creep phase [41; 42; 43]. To investigate the microscopic creep degradation mechanism of weakly cemented mudstone, this study first analyzed its microstructural deterioration using SEM and X-ray scanning. Building on previous research and triaxial creep test results under various confining pressures, we incorporated damage theory and nonlinear winding elements, identified damage thresholds, and developed an improved Nishihara creep model with damage considerations. The model was then extended to three-dimensional stress conditions and validated using the Levenberg-Marquardt (L-M) algorithm. Additionally, secondary development in FLAC3D was carried out to enhance its practical applicability. These efforts contribute to a deeper understanding of the creep behavior and degradation mechanisms of weakly cemented soft rock. The novelty of this study lies in proposing an improved Nishihara creep model that integrates a nonlinear winding element and a time-dependent damage function to characterize accelerated creep. Unlike existing models, the proposed model is fully extended to three dimensions and verified through secondary development and simulation in FLAC3D with multiple rock types, ensuring both theoretical rigor and practical applicability.
## 3 Materials and Methods  
### Previous Monitoring Information  
A first monitoring campaign of subsurface deformation was set up in 2006 and consisted of one vertical borehole drilled approximately in the middle portion of the landslide accumulation that was equipped with an inclinometric casing tube (Figure 3). During a biennium of measurements (i.e., from October 2006 to October 2008) executed through a removable inclinometric probe, any significant deformation was detected: the cumulative horizontal displacements measured at the tube head were lower than 1.5 mm.  
A second monitoring configuration was defined after significant acceleration of the movements occurred between January and February 2014, following a particularly wet period of several months [66; 67]. Due to the considerable displacements, some deep tension cracks developed through the landslide body (see Figure 3), while some buildings of the Fontane hamlet suffered major damage and had to be evacuated. Some months later,  
Figure 5: (**a**) S-wave seismic refraction tomography profile (the black dashed line indicates the depth of the rock basement); (**b**) ERT profile (the black dashed line indicates the qualitatively estimated depth of the rock basement interpreted from the 150 Ohm m iso-resistivity contour coupled with stratigraphic boreholes’ evidence); (**c**) interpretative transverse engineering–geological cross-section (see Figure 3 for location; redrawn and modified from [55]).  
in June 2014, subsurface displacement measurements started along flexible inclinometer casings that were installed into four boreholes (i.e., FON1, FON2, FON3, and FON4) drilled along the longitudinal direction of the landslide accumulation (see Figure 3). Their lengths below the ground level were 14, 19, 17, and 29 m, respectively. Two tubes, namely FON1 and FON4, were installed at the upper edge and foot of the active portion of the landslide, respectively. Instead, the FON2 and FON3 tubes were in the middle section of the landslide, approximately at the same elevation and 50 m apart from each other. After the sharp reactivation that took place in February 2014, the landslide remained active (Figure 6a), showing an extremely slow to very slow mobility according to [63]. In all inclinometric tubes, the shape of displacement profiles is characterized by pronounced and localized horizontal movements. Along the verticals FON3 and FON4, no significant profile perturbations were detected above the main levels of concentrated deformation. As far as the FON1 and FON2 tubes, slightly more irregular displacement profiles were obtained, probably because of minor internal deformations or flaws connected to the casing installation (e.g., grouting). As shown by the vectors of cumulative horizontal displacement measured at the tube guide heads (Figure 6b), the movements were consistent with both the dip direction of the slope and the pattern of tension cracks developed during the 2014 reactivation. However, both the displacement magnitude and average azimuth were slightly different at the section of the landslide foot (i.e., FON4 tube).  
Figure 6: _Cont_.
## 3 Experiments and Discussions
### FIL Simulation Experiments
#### 3.1.1 Simulate Normal Use  
The target temperature was set to 10 \({}^{\circ}\)C and the simulation time was 80 s to simulate a single RFA process to evaluate the performance difference between the ANFIS-PID controller and the PID controller for RFA temperature control. The experimental results are shown in Figure 4 and Table 1. In the figure, _step_ is the target temperature, _ANFIS-PID_ is the temperature controlled by the ANFIS-PID controller, and _PID_ is the temperature controlled by the PID controller (the above three items have the same meaning in the following figure).  
Figure 3: FIL simulation model.  
When simulating a single RFA process, the PID controller showed a faster response with a rise time and settling time of 4.18 s and 5.89 s, respectively, which were reduced compared to the ANFIS-PID controller. However, in terms of stability, the ANFIS-PID controller performed better, with an overshoot and undershoot of 6.43% and 3.69%, respectively, which were lower than the PID controller.
# An Explorative Model to Assess Individuals' Phubbing Risk
## 6 Results
### Result 1: Univariate Operative
#### 6.2.2 Phubbing Univariate Predictors: ICT and Social Media Effects  
Overall, the social media effects (i.e., social media and ICT usage and related addiction scales) have been previously assessed by research. Following the literature, we explored the general correlation structure emerging from those observables of our study that fit with the literature evidence (Table 4). In particular, in accordance with literature, the Mobile Phone Usage appears to be the best predictor, within the ICT related features, of all the phubbing factors' scores, as well as of the partner phubbing score. A strong relation is evident also, as predictable, with the other scales. The only exception is represented by the Games Usage Addiction Scale (GUAs). Nevertheless, the strength of relations with the partner phubbing appear in general to be weaker.  
Figure 1 is a recap of the results explained in details above. As we can see in this picture, there are strong positive correlations between both factors of phubbing and the addiction investigated in our study. The strongest one is with the mobile phone/smartphone usage addiction. A good relation is also present between phubbing and partner phubbing, highlighting how to do phubbing is correlated to our perception on how much one's partner does phubbing in his/her presence and thus on how much someone feels they are being phubbed. All correlations are reported in Table 4.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Observable** & **Phubbing** & **Phubbing** & **Total** & **Partner** \\  & **Factor 1** & **Factor 2** & **Phubbing** & **Phubbing** \\ \hline Mobile Phone Usage & 0.524 *** & 0.597 *** & 0.636 *** & 0.294 *** \\ SMS Usage/Addiction & 0.441 *** & 0.460 *** & 0.510 *** & 0.228 *** \\ Games Usage/Addiction & 0.162 *** & 0.130 *** & 0.164 *** & 0.154 *** \\ Social Media Usage/Addiction & 0.499 *** & 0.498 *** & 0.564 *** & 0.293 *** \\ Internet Usage/Addiction & 0.493 *** & 0.413 *** & 0.510 *** & 0.254 *** \\ \hline \hline \end{tabular}  
*** = \(p\) 0.001, *** = \(p\) 0.01, * = \(p\) 0.05.  
\end{table}
Table 4: Pearson _r._ correlations between personal and partner phubbing and the ICT usage addiction.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Observable** & **Social** & **STAI** & **General** & & \\  & **Anxiety** & **(Trait)** & **Self Efficacy** & & \\ \hline Phubbing Factor: Communication disturbance & 0.282 *** & 0.281 *** & \(-\)0.183 *** & 0.233 *** & \(-\)0.251 *** \\ Phubbing Factor: Phone obsession & 0.160 *** & 0.157 *** & \(-\)0.112 * & 0.214 *** & \(-\)0.195 *** \\ Personal Phubbing (Total) & 0.246 *** & 0.244 *** & \(-\)0.165 *** & 0.252 *** & \(-\)0.250 *** \\ Partner Phubbing & ns & 0.151 *** & ns & ns & ns \\ \hline \hline \end{tabular}  
*** = \(p\) 0.001, *** = \(p\) 0.01, * = \(p\) 0.05.  
\end{table}
Table 3: Pearson _r._ correlations between personal and partner phubbing and sociodemographic (age) and psychological variables, like anxiety and self-efficacy.
# Active RIS-Assisted Uplink NOMA with MADDPG for Remote State Estimation in Wireless Sensor Networks
## 4 Simulation Results
Figure 5: Convergence for different maximum power constraints. Figure 6: Convergence comparison under different sensor-to-channel configurations. Figure 7 evaluates the impact of different amplifier efficiency coefficients \(\xi\) on the convergence behavior of the proposed algorithm. The parameter \(\xi\) reflects the effective amplification capability of the active RIS, as constrained by the total power budget \(P_{\text{total}}\) and the internal power consumption \(M(P_{\text{DC}}+P_{c})\), as defined in constraint (20d). A larger value for \(\xi\) enables the active RIS to contribute more effectively to signal enhancement via stronger amplification, thereby improving the signal-to-noise ratio (SNR) at the receiver and reducing the RSE error. However, increasing \(\xi\) also implies that more power is allocated to RIS amplification, potentially limiting the power available for sensors. As shown in the figure, a moderate value for \(\xi\) achieves a good trade-off between RIS enhancement and the overall system power balance, whereas excessive amplification may lead to diminishing returns. This result highlights the importance of careful tuning of the RIS-related parameters to optimize the joint communication-sensing performance in energy-constrained WSN environments. To facilitate a comprehensive and intuitive comparison across different experimental settings, Table 2 presents a summary of the convergence time and the steady-state RSE error under various configurations. In this table, \"Conv. Time\" denotes the number of iterations (abbreviated as \"Iter.\") required for the algorithm to reach convergence, while \"Conv. Value\" represents the final RSE error after stabilization. The data in the table are systematically derived from key experimental results, primarily based on the quantitative trends observed in Figures 3-5. As shown in Table 2, the proposed MADDPG algorithm assisted by the active RIS consistently achieves a superior performance in terms of both the convergence speed and estimation accuracy, particularly when equipped with a larger number of reflecting elements. In contrast, reducing the number of RIS elements or the transmission power constraint leads to increased estimation errors, highlighting the importance of spatial diversity and power resources for accurate sensing. This table provides a clear and concise overview of the system behavior across algorithms and configurations, offering valuable insights for future research and parameter optimization in wireless sensor networks. Figure 7: Convergence performance under different RIS amplifier efficiency coefficients \(\xi\).