esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Study Area and Spatial Datasets
### Performance Assessment of the Final BE-LMTree Model  
Because the landslide modeling in this research is considered to be a binary form of pattern recognition, therefore the performance of the final BE-LMTree model could be assessed using confusion matrix (Figure 6) [40], both on the training dataset and the validation dataset. Based on the matrix, several model measures are further derived i.e., sensitivity (SEN), specificity (SPE), positive predictive power (PP2), and negative predictive power (NP2), Kappa statistics, and classification accuracy (CLA) for the assessment, as suggested in [50]. It should be noted that a perfect landslide model will have 100% for SEN, SPE, PP2, NP2, and CLA.  
For the case of CLA, although CLA provides the overall performance of the landslide model, however, a landslide model with a high CLA value may not classify the landslide pixels well. Therefore, the likelihood ratio (LLR) is additionally used [84]. LLR is a metric that assesses the trade-off of both SEN and SPE of landslide models. The higher the LLR value, the better the landslide model.  
Global performance of the BE-LMTree model is summarized and assessed using the Receiver Operating Characteristic (ROC) Curve and Area Under the curve (AUC) [40; 41; 85]. In general, the closer the curve to the upper left corner, the better performance of the landslide model. Once the ROC curve is constructed, AUC for the model is computed and used to quantify the quality of the model. Accordingly, the performance of the model is excellent (AUC belong to 0.9-1), good (AUC belong to 0.8-0.9), fair (AUC belong to 0.7-0.8), and poor (AUC is less than 0.7) [86].  
Figure 6: Confusion matrix and model measures used in this research.
## 3 Results and Discussion
### Chemical and Physical-Chemical Parameters
#### 3.1.2 Effect of the Harvesting Method  
The harvesting method affected all the parameters included in Table 1. Not only did the Ground oils (from ground-picked fruits) reach the highest and therefore worst values in all the quality parameters, but also the lowest in the total phenol content and oxidative stability (Figure 1). All the Ground oils, taken over the six-week period, exceeded considerably the 0.8% limit for free acidity for the extra virgin olive oil stated in European Commission Regulation (61/2011) [25], values being in the 2.4-5.3 range, and moreover all except the oil produced in week 2 exceeded the maximum allowance at least in one more quality parameter. Conversely, the two types of oils from tree-collected fruits (Conventional and Organic oils) were within the allowance limits for all the quality parameters. These results match those reported in Picaul samples [26], with all the oils from ground-picked fruits exceeding the limit for free acidity and all the ones from tree-picked olives being below it.  
With respect to the total phenol content, ground-picked olives, which are more exposed to microbiological infection than tree-picked olives, might have yielded lower phenol concentrations due to the phenol decline that the microbiological activity causes, in particular in oleupropein derivatives [27]. Furthermore, since phenols protect oil from oxidation [28], a decrease in them would facilitate a decline in the oxidative stability of oil. These results show a considerable repercussion of the harvesting method on the Verdial de Badajoz oil quality over, regardless of the harvesting time, and confirm the fact that fruits collected from the ground result in poor quality olive oils [10; 26]. Thus, clear differences in the volatile compound profile between the Ground oils and the others might be expected.
###### Abstract  
Extending the shelf life of perishable food, such as apples, and storing them in cold conditions and/or controlled atmospheres have been of great interest in the last decades. Apples are very valuable fruits with many health benefits, but during storage at ambient conditions, they ripen quickly and lose moisture, causing lower crispness or other negative effects, resulting in waste problems. There has been growing attention to protective edible coatings or active packaging films based on biopolymers and natural bioactive substances. Edible coatings and films allow for combination with functional ingredients or compounds, affecting the maintenance of the postharvest quality of fruits and vegetables. They also ensure the preservation of the sensory characteristics of food, and they can have antimicrobial or antioxidant properties. All these aspects play a significant role in the storage of apples, which can also help prevent waste, which is in line with the circular economy approach. The functionality of coatings and films is closely related to the type, content, and composition of active compounds, as well as their interaction with biopolymers. Active coatings with the addition of different functional compounds, such as plant extracts, phenolic acids, and nanoparticles, can be an alternative solution affecting the postharvest quality of apples during storage, maintaining the fruit's stability, and thus minimising their waste. The most important issues related to the latest reports on improving the postharvest quality of apples using edible coatings incorporated with various active substances were evaluated. Agricultural conditions and factors that affect the postharvest quality of apples were described. The requirements for protective coatings for apples should be focused on low-cost materials, including waste-based resources, good miscibility, and compatibility of components. Those factors combined with the storage conditions may result in shelf life extension or retention of the postharvest quality of apples, regardless of the variety or cultivation techniques.  
apples; _Malus_ spp.; edible coatings; edible films; active packaging; postharvest quality
# Correlation of Pavement Distress and Roughness Measurement
###### Abstract
Due to this, roughness measurements were acquired in a reasonably short period of time and at a cheap cost. The major goal of this study is to determine the relationship between pavement distress assessment and roughness measures in the Riyadh roadway network. The pavement distresses were ride quality-related distresses such as cracking, patching, potholes, depression, rutting, and raveling. Roughness measurements are indicated by the International Roughness Index (IRI). The analysis included establishing correlation factors between the distress types of riding quality and IRI and developing models to forecast the pavement distress type values based on IRI and highway class.
## 5 Discussion
### Complexity Analysis
#### 5.6.3 Trainable Parameters  
After examining memory usage and prediction speed, we now turn to the third metric of model complexity: the number of trainable parameters. In deep learning models, each adjustable weight during training counts as a trainable parameter. Generally, a higher number of parameters allows a model to capture more intricate data patterns but also increases the risk of overfitting on limited training data.  
Figure 12 displays the number of trainable parameters for the various deep learning models used in this study (CNN, LSTM, BiLSTM, GRU, BiGRU, and our proposed Att-ResBiGRU). These values were obtained from model summaries across the benchmark HAR datasets (Opportunity, PAMAP2, and REALWORLD16). As expected, based on their complexity, the baseline models showed varying parameter counts. The GRU emerged as the simplest model, with around 130,000 parameters for each dataset. Conversely, the  
Figure 11: Mean prediction time in milliseconds of deep learning models used in this work.  
Figure 10: Memory consumption in megabytes of deep learning models used in this work.  
CNN boasted the highest parameter count, signifying its greater complexity. Our proposed Att-ResBiGRU fell between these extremes, exhibiting a moderate parameter count of approximately 250,000 across all datasets. Att-ResBiGRU maintained a lower parameter count compared to CNN, BiLSTM, and BiGRU.
## 4 Discussion
Second, we used only three predictors for the PCO classifier application: (1) age at diagnosis, (2) clinical-radiological staging, and (3) the modified risk level, which is combination of pretreatment prostate specific antigen (PSA) and the Gleason score. We chose to use only these three predictors because the PCO classifier application aimed to calculate the risk using minimal, necessary information to support prostate cancer patients' decisions on treatment. In addition, classifiers, such as the CAPRA score and the Candiolo classifier, require positive cores at biopsy as a predictor of BCR. We did not use biopsies as a predictor for the PCO classifier application. Our model can be extended to use other predictors if they are identified as necessary or potentially advantageous to the model's accuracy. Future research should be conducted to include other predictors. Third, 809 patients treated with SBRT and followed for 5 years at a single institution were included in this study. Future research should compare the results using data from other hospitals or a longitudinal observational database, such as the Cancer of the Prostate Strategic Urological Research Endeavor (CaPSURE) database, with various stages and treatments of the prostate [29]. Fourth, we used the ASTRO definition for BCR, even though other research has emphasized that the Phoenix definition is a superior predictor of patient outcomes, such as distant metastasis, cause-specific mortality, and overall mortality [30]. We stand by our decision to use the ASTRO definition, because it is a predictor of distant metastasis and cause-specific mortality, just not overall mortality [30]. The ASTRO definition has resulted in substantially higher estimates of BCR at 5 years than has the Phoenix definition [22]. Because our study was interested in BCR, the ASTRO definition seemed like the correct choice, even if it is controversial. Future research should substitute the Phoenix definition for the ASTRO definition and compare the results. Finally, there are 18 prostate cancer patients with BCR in our study. We developed the PCO classifier application based on 18 patients among 809 patients. The BCR rate in this study is an un-meditated rate. It is a real BCR rate. However, a future study could collect big data from multiple hospitals to use more patients with BCR.
## 5 Results  
In this section, we evaluate the proposed PS-AUPF in the high-maneuvering target tracking problem through simulations and compare the performance of PS-AUPF to that of PF, PF with partitioned sampling (PS-PF), UKF, UPF and UPF with partitioned sampling (PS-UPF). In addition, a modern tracking method of artificial intelligence, the multiple-model neural filter (MMNF) [24], is also selected for comparison. All of the algorithms are implemented using MATLAB R2015a on computer with the following specification: CPU, Intel Core i5-3230M 2.6 GHz; Memory, 8 GB. The target motion model and measurement model given in Section 2 of this paper are used in our experiments.
## 4 Results and Discussion
### GMM Clustering Based on Stay Time  
#### 4.2.1 Clustering Results Based on Stay Time  
The stay time of intercity ridership is often related to their travel purpose, and different travel purposes will show different stay times. In this regard, there is obviously a big difference in the main travel purposes of different intercity ridership groups based on stay times, so the cluster analysis of intercity ridership is conducted. The boarding time and alighting time difference in the round trip is calculated to calculate the ridership's stay time in Changsha. That is, for each passenger, the stay time is defined as the difference between the second boarding time and the first alighting time, which represents how long an individual stays in Changsha and is a comprehensive expression of travel patterns as well. To examine whether there is any difference in intercity stay time between weekend and weekday ridership, \(T\)-test results indicate significant differences between weekends and weekdays, and the 95% confidence interval is (2.869, 3.904). Figure 10 shows the stay-time distribution of intercity ridership. It can be seen that there are two peaks, which can be roughly divided into two categories. Since the stay time of the intercity metro ridership shows significant heterogeneities with multiple peaks, the GMM is adopted to identify the travel patterns of the different passenger groups.  
There are two Gaussian distributions identified. Table 5 shows their weights, means, and standard deviations. The weight of Gaussian distribution 1 is 79.1%, and the mean value is 4.3 h. The weight of Gaussian distribution 2 is 20.9%, and the mean is 23.5 h. That is, Gaussian distribution 1 and Gaussian distribution 2 correspond to the short-stay-time and long-stay-time groups, respectively. Obviously, the short-stay-time group accounts for the highest proportion and the long-stay-time group has the lowest proportion. According to the length of stay time, these two groups correspond to two different stay patterns: a round trip on the same day and overnight stay. The differences between these two groups will be further explored below from a spatiotemporal perspective.
## 2 Different Methods of Hydrogen Generation
### Latest Hydrogen Production Process Advances
#### 2.3.7 SRBW Technology  
RMIT University engineers have devised a technique to improve the efficiency of the electrolysis process used to create hydrogen from water. The engineers were able to dramatically enhance the output of hydrogen generated during the electrolysis process by using sound waves, which might have significant implications for the renewable energy sector. Typically, the electrolysis process necessitates the use of corrosive electrolytes and costly electrodes such as platinum or iridium, both of which may be expensive and ecologically damaging to produce. The RMIT University engineers created a process that includes exposing the water to high-frequency sound waves that cause a vibration, which helps in the breakdown of water molecules into their component atoms. This technology has been demonstrated to be considerably more efficient than typical electrolysis methods, with a net positive energy saving of more than 27%. In general, employing sound waves to produce hydrogen is a viable method that might help in solving the problems posed by current electrolysis methods. This technology may lead to more environmentally friendly and cost-effective ways by reducing the need for pricey and environmentally destructive materials [58].
###### Abstract  
This paper addresses the design, analysis, and validation of safe close-proximity operations around uncooperative targets, with an application to the ClearSpace-1 (CS-1) mission. It is focused on the areas of Guidance, Navigation, and Control (GNC), and Mission Analysis, due to their criticality for the success and safety of this kind of operation. The relevance of the concepts, of the GNC solutions, and their validation is demonstrated for the case study of CS-1, a reference mission for the rendezvous, capture, and de-orbiting of an uncooperative target (i.e., the VESPA payload adapter). It is shown how the design approach can be adopted for the Concept of Operations of CS-1, covering the definition of keep-out zones, corridors, and GO/NO GO criteria, for assessing the passive safety of trajectories, and for the incorporation of active safety strategies. The analysis is adopted for functional chains such as the Navigation and Control, and the combination of a prototyping and a high-fidelity simulator is adopted for directed Model-in-the-Loop Monte-Carlo campaigns. The outcomes are intended to support the industry in the development of Close-Proximity Operations similar to that of CS-1. These can be adopted in a wide variety of missions, including Active Debris Removal and In-Orbit Servicing. In particular, the adopted concepts are a key contribution to the standardization of Close-Proximity Operations for non-cooperative rendezvous missions, and act towards a sustainable and safe commercial application.  
GNC; Active Debris Removal; close-proximity operations; safety; verification and validation 2025  
1267
# Steric, Activation Method and Solvent Effects on the Structure of Paddlewheel Diruthenium Complexes
## 2 Materials and Methods
#### Synthesis of [Ru2 Cl(\(\mu\)-O2 CC6H4-\(m\)-OMe)4]n (**2**)  
This compound has been obtained by means of the three synthetic methods described for compounds **1a** and **1b**, using \(m\)-methoxybenzoic acid (0.23 g, 1.50 mmol). Similar conditions were used in each case, unless otherwise stated.  
_Method a:_ Yield: 77%. Anal. Calcd.(%) for [[Ru2 Cl(\(\mu\)-O2 CC6H4-\(m\)-OMe)4]-H2O]n (**2**: H2O): C, 44.68; H, 3.52. Found (%): C, 44.52; H, 3.32.  
_Method b_ Yield: 76%. Anal. Calcd.(%) for [[Ru2 Cl(\(\mu\)-O2 CC6H4-\(m\)-OMe)4]-0.5H2O]n (**2**: 0.5H2O): C, 45.16; H, 3.43. Found (%): C, 45.13; H, 3.34.  
_Method c:_ MeOH as solvent and 16h of cooling ramp down to room temperature were used in this synthesis. Yield: 47%. Anal. Calcd.(%) for [[Ru2 Cl(\(\mu\)-O2 CC6H4-\(m\)-OMe)4]-3H2O]n (**2**: 3H2O): C, 42.89; H, 3.82. Found (%): C, 41.96; H, 3.38. No better elemental analysis has been obtained for the product obtained by this procedure.  
FT-IR (cm\({}^{-1}\)): 3018w, 2941w, 1736w, 1587w, 1490w, 1460m, 1446m, 1398s, 1286m, 1247m, 1114w, 1043s, 787m, 755s, 678m.  
UV-Vis-NIR (diffuse reflectance): [\(\lambda\), nm] 328 sh, 490, 1169.  
Mass spectrometry [\(m/z\) (fragment)]: 808 [Ru2(\(\mu\)-O2 CC6H4-\(m\)-OMe)4]+.  
\(\mu_{\text{eff}}\) (R.T.) = 4.14 \(\mu_{\text{B}}\).  
2.4.5. Synthesis of [Ru2 Cl(u-O2 CC6H4-_p_-OMe)4]n (**3**)  
This compound has been obtained by means of the three synthetic methods described for compounds **1a** and **1b** using _p_-methoxybenzoic acid (0.23 g, 1.50 mmol). Similar conditions were used in each case, unless otherwise stated.  
_Method a_: Yield: 48%. Anal. Calcd.(%) for [[Ru2 Cl(u-O2 CC6H4-_p_-OMe)4]-H2O]n (**3**-H2O): C, 44.68; H, 3.52. Found (%): C, 44.28; H, 3.39.  
_Method b_ Yield: 61%. Anal. Calcd.(%) for [[Ru2 Cl(u-O2 CC6H4-_p_-OMe)4]-H2O]n (**3**-H2O): C, 44.68; H, 3.52. Found (%): C, 44.32; H, 3.33.  
_Method c_: An isotherm at 90 \({}^{\circ}\)C and 24 h of cooling ramp down to room temperature were used in this synthesis. Yield: 79%. Anal. Calcd.(%) for [[Ru2 Cl(u-O2 CC6H4-_p_-OMe)4]-H2O]n (**3**-H2O): C, 43.67; H, 3.67. Found (%): C, 42.98; H, 3.31. No better elemental analysis has been obtained for the product obtained by this procedure.  
FT-IR (cm\({}^{-1}\)): 3078w, 2935w, 2837w, 1603s, 1584w, 1517w, 1443w, 1388s, 1314m, 1255s, 1168s, 1107w, 1019m, 847m, 784w, 771s, 699m.  
UV-Vis-NIR (diffuse reflectance): [x, nm] 352, 474, 1162.  
Mass spectrometry [_m/z_ (fragment)]: 808 [Ru2(u-O2 CC6H4-_p_-OMe)4]\({}^{+}\).  
\(\mu_{\text{eff}}\) (R.T.) = 4.07 \(\mu_{\text{B}}\).
## 3 Results
### GDPD Network Analysis  
To determine what, if any, relationship there was between the isolated GDPD protein and the apparent loss of motility of cells at the intercolony interface, we used the STRING Database v11.0 [53] to establish possible functional interactions among related proteins. STRING is a database of known and predicted protein-protein interactions. The interactions include direct (physical) and indirect (functional) associations; they stem from computational prediction, from knowledge transfer between organisms, and from interactions aggregated from other (primary) databases. In this analysis, the STRING Database predicted ten potentially interacting proteins (Table 1).  
As illustrated in Figure S3, GDPD could be interacting with a FAD dependent oxidoreductase, a GDPD with six transmembrane domains, a glycerol-3-phosphate dehydrogenase (GPDH), two glycerol kinases, a phosphodiesterase/alkaline phosphatase D, a flagellar secretion chaperone FliS, an extracellular nuclease, a phosphatase, and a chemotaxis protein histidine kinase. Importantly, the STRING Database revealed an interaction between  
Figure 4: Secretion of proteins by colonies of _M. algicola_ DG893. Upper left: Zones sampled (1a and 1b) for non-interacting colonies. Upper right: Zones sampled (2a and 2b) for interacting colonies. Lower: 12% SDS-PAGE separation of proteins from cell lysates or secreted proteins from indicated areas produced by _M. algicola._ Migration position of periplasmic glycerophosphorylester phosphodiesterase (RefSeq WP_007154889.1; GenBank EDM46611.1) is indicated with an arrow.  
periplasmic GDPD and a chemotaxis protein histidine kinase that enables bacteria to sense, respond, and adapt to a wide range of environments, stressors, and growth conditions ([[http://www.ebi.ac.uk/interpro/entry/IPR014310](http://www.ebi.ac.uk/interpro/entry/IPR014310)]([http://www.ebi.ac.uk/interpro/entry/IPR014310](http://www.ebi.ac.uk/interpro/entry/IPR014310))), as well as potentially controlling a flagellar secretion chaperone protein present in this network. On the other hand, the STRING Database established a potential interaction of GDPD with two glycerol kinases involved in glycerol degradation ([[https://www.uniprot.org/uniprot/A6F2B1](https://www.uniprot.org/uniprot/A6F2B1)]([https://www.uniprot.org/uniprot/A6F2B1](https://www.uniprot.org/uniprot/A6F2B1))). Another interesting potential interaction was with an extracellular nuclease that is involved with diverse processes of a typical biofilm architecture in _Vibrio cholerae_[54].  
Based on motility assays, however, it is clear that GDPD, in and of itself, is not the trigger for motility loss, as cells treated with partially purified protein continue to be motile. Taken together with some preliminary here, these data suggest that GDPD might exert an effect on interacting sibling colonies by triggering changes in triglyceride or glycerolphospholipid metabolism to have a role in biofilm formation. Nevertheless, detailed evaluation of any potential GDPD-related mechanism will have to await isolation and characterization of larger amounts of functional protein.