esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overview of Multiobjective Path Problems and Algorithms
### Types of Multiobjective Path Problems  
We consider three groups of MOPPs. The first group (Section 2.2.1) is the most well known, the _multiobjective shortest path problem_ (MOSPP), where objective functions, to be minimized, are additive (minusum):  
\[f^{k}(p)=\sum_{(i,j)\in P}c_{ij}^{k},\ \ k=1,2,\ldots,r,\ \ p\in P_{st}. \tag{6}\]  
Since the MOSPP is the most used path problem in telecommunication network applications, we consider a large number of methods for its resolution and their classification as follows:  
* APO--methods using an a posteriori aggregation of preferences, that is, methods that generate the whole set of efficient paths, so that the articulation of preferences is made by the decision maker;
* APR--methods using an a priori aggregation of preferences methods, that is, the problem is a priori transformed into a single objective problem, for instance, by using a utility function. We must refer that, as the different objective functions are modeled prior to the reduction to a single objective problem, the problem remains intrinsically multiobjective. Note that strict-sense lexicographic approaches should be included in this class;
* INT--_Interactive methods_, that is, methods where the articulation of preferences is progressive, including two successive phases: calculation and dialogue phases. So, a cycle of proposals and reactions continues till a so-called satisfactory compromise is obtained, i.e., some stopping condition is reached.  
In the second group (Section 2.2.2), we include, besides minsum functions (for instance, those associated with routing load costs, expressed in terms of the inverse of the available bandwidth in each arc/link of the path), other types of objective functions, for instance,capacity functions such as maxmin or minmax corresponding typically, in routing models, to the optimization of path bottleneck bandwidths.  
The third group (Section 2.2.3) refers to specific path combinatorial problems, and we focus on methods dedicated to their resolution based on specific biobjective shortest path techniques.  
In Figure 2, we summarize the classification of the above referred to problems, considering deterministic formulations only.  
In the next subsections, we will be referring to the main results regarding the considered methods.  
In Tables 1-3, we summarize some essential features of the mentioned resolution methods as referred to in this paper.
## 4 Results and Discussion  
### Artificial Neural Networks Modelling Results  
The experimental data set, used for the training and testing of ANNs, included 39 specimens for the HMA mixtures with 50/70 penetration bitumen and limestone aggregate, 30 for ones with modified bitumen (25-55/75 penetration) and limestone aggregate, and 30 for each of the mixtures  
Figure 4: Grading curves of HMA20-50/70-Lm and HMA20-SBS-Lm.  
Figure 5: Grading curves of HMA20-50/70-Db and HMA20-SBS-Db.  
with diabase aggregate and a different type of bitumen (i.e., 129 specimens overall). According to the relevant literature [78; 81], for such a data set size, k-fold cross validation is recommended.  
The data of all the mixtures were fitted together to obtain a unique predictive model for each considered mechanical parameter; such models can take into account the different composition of the HMAs in terms of the bitumen content and type, aggregate type, filler to bitumen ratio, and volumetric properties.
# Modeling Implicit Trust in Matrix Factorization-Based Collaborative Filtering
## 4 Experiment Details
### Final Latent Features  
The accuracy of predictions depends on how accurately we calculate the final latent features. These final features are derived from Equations (12)-(17). The mean values are calculated using both item and user features. For the three data sets, we have graphed the mean values of the final latent matrices, after combining them with estimated trusts, for M-Trust, S-Trust, and W-Trust. For I-Trust, we only use the initial trust.  
In Figure 8, the mean values of the latent features in M-Trust are significantly greater than those of other methods; for all data sets, the values are around 0.045. This is due to the stacking of the mean trust value to the initial latent features. For the three data sets, M-Trust produces better accuracy than all the other methods. Consequently, stacking the mean trust values acts as adding preference biases to SVD or SVD++, resulting in better accuracy. Similarly, for I-Trust, when trust is combined with latent matrices, the mean values are increased. The I-Trust method has much higher values than that of the S-Trust and W-Trust methods, for all the data sets. This explains why the prediction accuracy of the I-Trust method is higher than that of both the S-Trust and W-Trust methods. The values in the latent matrices of both S-Trust and M-Trust are very small after combining trust. This is reflected in the results in all the prediction accuracy graphs (Figures 2, 4 and 6). These two methods' prediction accuracies do not demonstrate significant improvements compared to those of SVD and SVD++. However, they are still better than those of SocialMF and NGCF.  
systems, as the use of a full data set is not practical for some recommendation techniques [28]. Since the implicit trust calculations in our model are purely based on users' preference, we expect the model to perform similarly on other domains such as music or product recommendations, as long as preference data is available. However, if the data sets have an extremely large number of items or users, the trust values may not have a significant effect on prediction values.  
Here, we focused on the prediction accuracy of recommendation systems using implicit trust. We found that in terms of the prediction accuracy, the deep learning-based baseline method exhibits a considerably worse performance than the other three baselines. Similar to most modern recommendation techniques, NCGF is not designed to perform optimization for prediction accuracy, but instead for better prediction ranking. Therefore, as a future work, we plan to modify the proposed method to accommodate prediction ranking.
## 3 Test Results and Discussion
### Freeze-Thaw Cycles Tests
#### 3.4.1 Results at \(-10\)\({}^{\circ}\)C  
Figure 6 clarifies the impact of F-T cycles at \(-10\)\({}^{\circ}\)C on the stress-strain characteristics of both untreated and treated samples. Observations indicate a significant reduction in strength for untreated samples during F-T cycles (Figure 6a). However, Figure 6c,d demonstrate that the application of 9% and 14% MgCl\({}_{2}\) solutions results in a notable antifreeze effect, preserving the samples' stress-strain behaviors. A lower concentration of  
Figure 5: Influence of MgCl\({}_{2}\) solution on: (**a**) Unconfined compressive strength (UCS) values and (**b**) strain at failure.  
4% MgCl\({}_{2}\) solution exhibits a minor yet observable impact, slightly reducing the extent of strength loss and displaying a reduced decrement in strength after 7 cycles, compared to untreated samples (Figure 6b).  
The change in the unconfined compressive strength (UCS) values with the addition of a MgCl\({}_{2}\) solution suggests that, for a given F-T cycle, the stabilizer enhances the soil's strength (Figure 7a). For example, after the third freeze-thaw cycle, the untreated soil exhibits a strength of 145.84 kPa, while soil samples treated with 4%, 9%, and 14% MgCl\({}_{2}\) solutions show increased strengths of 220.35 kPa, 289.62 kPa, and 331.24 kPa, respectively. The effect of a 4% MgCl\({}_{2}\) solution on enhancing soil strength diminishes with the progression of cycles. Nevertheless, the strength improvements provided by 9% and 14% solutions endure even after the cycles progress.  
Figure 6: Stress-strain relations for treated and untreated samples at \(-10\)\({}^{\circ}\)C with different MgCl\({}_{2}\) solution: (**a**) Untreated, (**b**) treated with 4% MgCl\({}_{2}\), (**c**) treated with 9% MgCl\({}_{2}\), and (**d**) treated with 14% MgCl\({}_{2}\).  
The fluctuation in the durability index (DI), represented in Figure 7b, shows an apparent decrease in DI values for both untreated and 4% treated samples, with untreated samples exhibiting a notably sharp decline during the first F-T cycle. This highlights the critical role of the first F-T cycle in affecting the durability of soil samples. Notably, after 7 F-T cycles, the DI decreases to 0.41 for untreated samples and to 0.59 for samples treated with a 4% MgCl\({}_{2}\) solution, underscoring the severe impact of F-T cycles on soil durability. In contrast, DI values for samples treated with 9% and 14% MgCl\({}_{2}\) solutions exhibit stability, remaining consistent before and after 7 F-T cycles.
## 2 Methodology
### Increasing of Pointing Accuracy
#### 2.8.1 Conical Scanning  
Conical scanning is obtained by adding a helicoidal shift to the Pointer.  
Considering P = (Px,Py) as the designated point achieved during the pointing phase, the resulting Pointer motion with helicoidal scanning can be determined as follows [29]:  
\[\text{Hx}=\text{Px}+\text{r}_{\text{s}}\cdot\text{cos}(\upomega\text{t}) \tag{11}\]  
\[\text{Hy}=\text{Py}+\text{r}_{\text{s}}\cdot\text{sin}(\upomega\text{t}) \tag{12}\]  
in which \(\text{r}_{\text{s}}\) and \(\upomega\) are, respectively, the conical scanning amplitude and frequency.  
Figure 14 depicts the pointing accuracy graph in the case of helicoidal scanning.  
The locking area evaluation led us to identify an increase in pointing accuracy (Figure 15) with an increment of the number of times in which the system reached the requested pointing accuracy.
## 4 Experiments and Results
### Results for Redundancy, Overhead, and Delay  
Figure 3 and Table 3 present detailed performance comparisons in Redundancy, Overhead, and end-to-end delay across different broadcasting schemes. This subsection provides an analysis emphasizing the underlying causes.
# Metabolic Rate Measuring with Indirect Calorimetry for Thermal Comfort Evaluation
## 2 Methodologies
### Experimental Procedure  
In order to simulate four various thermal environments, this study set four ambient temperatures, which were 30 \({}^{\circ}\)C (hot), 24 \({}^{\circ}\)C (warm), 18 \({}^{\circ}\)C (cool), and 12 \({}^{\circ}\)C (cold), respectively. The atmospheric pressure was 100.48 kPa under each condition. Since the outdoor air in Tianjin was relatively dry during the experiment, the relative humidity was 30-40%, and it was easier to control and maintain stability by reducing the relative humidity with the dehumidification wheel than by increasing the relative humidity. Therefore, the relative humidity was constantly set at 30%. All subjects were requested to wear appropriate clothing under these four conditions. Table 2 gives the pertinent details about the clothing. Except for the down jacket, all clothing was made of cotton. Among them, men's briefs and long johns were provided by the subjects themselves, and the rest of the clothes were provided by this experiment to ensure that the clothing insulation was the same.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensors** & **Type** & **Range** & **Accuracy** \\ \hline Flow & Digital turbine & \(\leq\)20.1 L/s & \(\pm\)2\% \\ O\({}_{2}\) & Electrochemistry & 0–100\% & \textless{}0.1 vol \% \\ CO\({}_{2}\) & Nondispersive infrared & 0–13\% & \textless{}0.1 vol \% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of internal sensors.  
Figure 4: The thermal sensation vote.  
Figure 3: (**a**) MetaMax 3B, (**b**) the specialized mask (**c**) Polar H10, (**d**) DS1992L.  
Subjects are instructed to participate in the experiment at least three hours after the meal since eating increases MET significantly [16]. They are prohibited from drinking alcohol from the day before the start of the experiment until the end of the experiment. In addition, subjects receive at least one day of relaxation after participating in an experiment to prevent fatigue.  
Figure 5 shows the experimental process. The subjects wear the clothing and equipment before entering the climate chamber. After 25 min of sedentary activity to acclimate to the environment, their Tsk, MET, and HR are measured. The participants experience a 5 min sitting period and three 10 min exercise periods with varying walking speeds (2, 4, and 6 km/h, respectively). Similar to [PERSON]'s study, activity intensity runs from low to high [16]. Moreover, before raising activity intensities each time, subjects are required to achieve subjective questionnaires verbally.
## 3 Results
### Studies of the Influence of Technological Parameters on Fermentation Stems and Kinetic Modelling
#### 3.1.2 Studies on the Effect of the Initial Amount of Sugars  
Studies on the effect of the initial amount of sugars on changes in antioxidant activity (AA), total polyphenol content (TPC), and lactic acid efficiency (LAe) are presented in Table 3.  
Table 3 shows that the highest value of antioxidant activity (AA = 4.29 mmol Tx/L \(\pm\) 0.22) and the highest content of phenolic compounds (2.84 \(\pm\) 0.09) were obtained by conducting fermentation for 9 days using an initial sugar content of 4.60%. After lowering the initial sugar content to 3.20% on the ninth day of conducting the process, lactic acid formation was observed with the highest yield (LAe = 89% \(\pm\) 1). For this reason, an initial sugar content of 3.20% and a fermentation time of 9 days were selected for further studies on the effect of plant raw material content on stem fermentation. Under these conditions, AA and TPC values were, respectively, 4.23 mmol Tx/L \(\pm\) 0.13 and 2.82 mmol GA/L \(\pm\) 0.18 (Table 3).  
Figure 4 shows the course of changes in lactic acid yield during the fermentation of clove tree stems in the presence of _L. rhamnus_ MI-0272 using an initial sugar content of 1.60-4.60%, where the line corresponds to the values defined in Equation (3).  
Stem fermentation tests in the presence of _L. rhamnus_ MI-0272 and using an initial sugar content in the range of 1.60-4.60% showed that the best ratio of lactic acid gain constant to LA decay constant was observed for the process carried out at an initial sugar content of 3.20% (FSE-8) according to the determined reaction rate constants k\({}_{1}\) and k\({}_{2}\) (Table S1); moreover, kinetic curves show changes in lactic acid yield during fermentation (Figure 4).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Fermented Stems**} & \multicolumn{2}{c}{**Fermentation**} & \multirow{2}{*}{**AA**} & \multirow{2}{*}{**TPC**} & \multirow{2}{*}{**LAe**} \\  & & **Time** & & \\ \cline{2-4} \cline{6-6}  & **Days** & & & \\ \hline \multirow{2}{*}{FSE-7} & 1 & 4.09 \(\pm\) 0.21 & 1.90 \(\pm\) 0.11 & 44 \(\pm\) 1 \\  & 5 & 4.17 \(\pm\) 0.20 & 2.50 \(\pm\) 0.17 & 78 \(\pm\) 1 \\  & 9 & 4.21 \(\pm\) 0.18 & 2.80 \(\pm\) 0.18 & 83 \(\pm\) 1 \\ \hline \multirow{2}{*}{FSE-8} & 1 & 4.11 \(\pm\) 0.20 & 1.92 \(\pm\) 0.21 & 50 \(\pm\) 1 \\  & 5 & 4.19 \(\pm\) 0.17 & 2.52 \(\pm\) 0.21 & 85 \(\pm\) 1 \\  & 9 & 4.23 \(\pm\) 0.13 & 2.82 \(\pm\) 0.18 & 89 \(\pm\) 1 \\ \hline \multirow{2}{*}{FSE-9} & 1 & 4.24 \(\pm\) 0.17 & 1.93 \(\pm\) 0.19 & 43 \(\pm\) 1 \\  & 5 & 4.25 \(\pm\) 0.21 & 2.53 \(\pm\) 0.19 & 76 \(\pm\) 1 \\ \multicolumn{2}{c}{4.60\%} & 9 & 4.29 \(\pm\) 0.22 & 2.84 \(\pm\) 0.09 & 81 \(\pm\) 1 \\ \hline \hline \end{tabular} Mean \(\pm\) SD (n = 3), process parameters: _L. rhamnus_ MI-0272, inoculum content 3.20%; plant material content 1.60%, fermentation time 9 days.  
\end{table}
Table 3: Studies on the effect of the initial amount of sugars on changes in antioxidant activity (AA), total polyphenol content (TPC), and lactic acid efficiency (LAe).
## 1 Introduction
ML is playing a key role in detecting defects in flat polyethylene and polyamide panels by analyzing airborne ultrasonic signals. By training ML models on ultrasonic data, algorithms can distinguish subtle differences between signals reflected from normal and defective areas of the material. Types of defects such as voids, cracks, or weak bonds often produce distinct acoustic signatures that ML models can learn to recognize with high accuracy. Using techniques such as feature extraction, ML can process these high-dimensional ultrasonic data sets, identifying patterns that human inspectors might miss. Additionally, ML models are adaptable, allowing the same trained algorithms to work on different batches of materials or panel types with minimal retraining. This combination of automation and precision significantly increases the reliability and efficiency of quality control in industrial production settings. Combining traditional and machine learning approaches to detect defects in polyethylene and polyamide flat panels using airborne ultrasound introduces a novel hybrid solution in the context of Industry 4.0 and Industry 5.0 [25; 26; 27]. Traditional ultrasound techniques have long been reliable in detecting material defects but machine learning improves the process by analyzing large volumes of ultrasound data, learning from patterns and improving the accuracy of defect identification. The novelty lies in the system's ability to self-optimize over time, detecting even subtle anomalies that may be missed by manual or traditional methods [28; 29; 30]. This hybrid method not only provides precise, real-time quality control but also integrates human expertise into decision-making, highlighting Industry 5.0's human-centric focus. Ultimately, the combination of these technologies allows for more sustainable, efficient and flexible production processes [34; 35; 36; 37].
###### Abstract  
The growing amount of space debris in the low Earth orbit poses a danger to manned as well as uncrewed missions. Additionally, the new business model of providing internet from space is emerging among new space players, making low Earth orbit more crowded. These factors have encouraged the space community to focus on sustainability in space. Satellite manufacturers typically have the capability to perform complete life cycle analysis (LCA) on their own products based on the manufacturing data. However, there is a lack of a method for non-manufacturers such as environmentalists and the general public to predict the carbon footprint of satellite manufacturing using a subsystem-level mass budget. Hence, this paper presents a method to quantify environmental pollution caused by the production of satellite constellations. Starlink is taken as a case study in this paper, and mass budget is predicted based on space systems engineering budget estimation techniques, the parametric method, and Federal Communication Commission orbital data. With the budget table used as an input, space-specific life cycle assessment is performed based on European Space Agency's life cycle inventory database. Finally, the single score for Starlink constellation version 1 was found to be 76 kilo points. This signifies the annual environmental load. These results could be helpful in obtaining an overview of the environmental effects of the production phase of satellite constellations. Further, the results could act as a foundation for further research on implementing more circular approach practices on Earth as well as in space.  
c 2024 16, 9431. [[https://doi.org/10.3390/sul6219431](https://doi.org/10.3390/sul6219431)]([https://doi.org/10.3390/sul6219431](https://doi.org/10.3390/sul6219431))  
1  
Citation: [PERSON]; [PERSON]; [PERSON] Quantifying the Environmental Impacts of Manufacturing Low Earth Orbit (LEO) Satellite Constellations. Sustainability 2024 16, 9431. [[https://doi.org/10.3390/sul6219431](https://doi.org/10.3390/sul6219431)]([https://doi.org/10.3390/sul6219431](https://doi.org/10.3390/sul6219431))  
1
## 3 Results
The resulting measurement data are shown in Figure 10. In Figure 10, the voltage at the PCC with nominal (left) and reduced (right) amplitude, without compensator, is shown. Figure 10 shows the result of voltage regulation by the conditioner using reactive power only. Using 4.5 kVA reactive power, the voltage amplitude is compensated to match the nominal amplitude. Figure 10 illustrates the same scenario of voltage regulation, though with the use of apparent power. The active and reactive components are set with the same properties for grid resistance and reactance. It can be observed that the apparent power used for compensation is significantly reduced from 4.5 kVA to 2.5 kVA. Figure 8: Simulation results of the voltage controller with current minimization method: (**a**) test voltage waveform, (**b**) reactive power compensation and (**c**) proposed apparent power compensation. Figure 9: Scheme of laboratory setup. In Figure 11, the steady-state performance of the voltage balancing is presented. To balance the voltages, the conditioner injects a negative sequence current with active and reactive components set according to the grid complex impedance. The voltage drop on the grid impedance results in voltage balancing at the PCC. As seen in the graphs, the voltage unbalance can be eliminated. The integral path of the voltage controllers eliminates the steady-state error. Figure 11: _Cont._  
Figure 10: Voltage regulation in steady state: (**a**) nominal and reduced voltage without compensation, (**b**) reactive power compensation and (**c**) apparent power compensation. Figure 12 shows the compensation of an unbalanced voltage dip: 70% single phase dip with 100 ms duration. The conditioner operates in minimum apparent power control mode and with a three-phase voltage-balancing controller. The grid impedance is increased in the experiment to \(X=0.65\)\(\Omega\), \(R=1.7\)\(\Omega\) and \(\mid Z\mid=1.8\)\(\Omega\) in order to extend the conditioner control range and improve the visibility of the results. The compensating action brings the voltage amplitude to the nominal level in less than one cycle. In this case, the conditioner current is unbalanced, as a response to the negative sequence in the grid voltage. The characteristic feature of the PI-based voltage control is a transient overvoltage at the end of the voltage dip. This is caused by the integral action of the controller, which maintains the voltage-boost action for an instant after the voltage returns to a nominal level.
## 3 Experimental Results and Analysis
### Evaluation Metrics
#### 3.2.2 Diversity Metric  
The diversity metric evaluates the equality of the distribution within the solution set. Initially, boundary solutions are recognized in multi-objective functions. Then, the Euclidean distance between each solution and its neighboring solutions is calculated, yielding a set of distance values (d\({}_{i}\)). Average (\(\overline{\text{d}}\)) and difference (d\({}_{i}-\overline{\text{d}}\)) values are derived from this set. Lastly, Equation (19) is utilized to compute the diversity index of the solution set.  
\[\text{diversity metric}=\frac{\text{d}_{f}+\text{d}_{1}+\sum_{i=1}^{\text{N}-1 }\left|\text{d}_{i}-\overline{\text{d}}\right|}{\text{d}_{f}+\text{d}_{1}+( \text{N}-1)\overline{\text{d}}}, \tag{19}\]  
Figure 8: Calculation of hypervolume.