esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Worst-Best Model Comparisons  
The best-performing model based on the fine-tuned RoBERTa large feature extractor achieved an MCC score of 0.6337, whereas the worst term frequency method provided features that allowed for an MCC score of only 0.4049.  
A detailed view of how the worst- and best-performing models performed the given prediction task is displayed in the confusion matrices in Table 4. The main difference in model quality lies in model error regarding minority class 2 (very respectful); that is, the best-performing model did not confuse a very respectful tweet with a disrespectful tweet even once. However, interestingly, the best model more frequently (167 errors) incorrectly predicted disrespectful tweets as being respectful than did the worst model (87 errors).  
In addition, a critic might note that the worst model, based on features extracted by Term Frequency method, was nonetheless able to correctly predict 79.9% of data instances, whereas the best model achieved 86.0%. From this perspective, the difference between the compared models seems minimal and thus might incorrectly suggest that model choice is not significant. This line of thinking can be readily dismissed because significant class imbalance was present in the data set; as a result, naively assigning the majority class for each data instance would allow for 75.1% accuracy. Thus, an appropriate quality metric such as MCC should be utilized; moreover, the actual model use-case matters most. In the given example, not mistaking disrespectful tweets for very respectful tweets is probably more important than not confusing disrespectful tweets with respectful tweets. Of course, in another use-case, it could be appropriate for the models to optimize a different type of errors. Fortunately, in this case, the MCC score was decided to serve as a decisive quality metric, and the result of the comparison is clear.
## 3 Methodology: Extending the Processing Toolbox
### Network Length Completeness Model  
As discussed in Section 2, the completeness of the data is vital to justify the fitness of data for various GI application domains. The network length assessment is an important intrinsic indicator to assess the completeness of road networks [31]. The processing model developed (Figure 2) requires two types of input: (1) the network graph; (2) the polygon layer of smaller regions with the region ID field. This model was used to calculate the length of roads class-wise as suggested by [PERSON] et al. [31]. Firstly, the input graph was re-projected to a suitable coordinate system. Thereafter, the class-wise road network was extracted and presented to the \"sum line lengths\" processing algorithm to find road network statistics. OSM data contain many classes for \"highway\" tags ([[http://wiki.openstreetmap.org/wiki/India:Tags/Highway](http://wiki.openstreetmap.org/wiki/India:Tags/Highway)]([http://wiki.openstreetmap.org/wiki/India:Tags/Highway](http://wiki.openstreetmap.org/wiki/India:Tags/Highway)), accessed on 2 March 2017) with few classes representing links for connecting a higher order road from/to a lower order road. For example, trunk_link connects the trunk to/from the primary road. In the current study, for calculating the network length class-wise, the \"links\" were considered part of the same class of which they are represented, i.e., trunk_link would be considered as the same as trunk class [31].
## 4 Analysis Results
### Measurement Model
#### 4.1.3. Target Coefficient  
The target coefficient is calculated by comparing the Chi-square of the fully-correlated model (structural saturated model) with the Chi-square of the second-order model. For the construct CQ, the Chi-square of the structural saturated model is 353.771. The Chi-square of the second-order model is 362.002. The target coefficient equals 353.771/362.002 = 0.977. For the construct ESE, the Chi-square of the structural saturated model is 161.225. The Chi-square of the second-order model is 178.127. The target coefficient equals to 161.225/178.127 = 0.905. The target coefficient 0.977 provides the evidence of the second-order factor existence. Specifically, 97.7% variance of the fully correlated model can be explained by the second-order factor. The target coefficient 0.905 provides the evidence of second-order factor existence. Namely, 90.5% variance of the fully correlated model can be explained by the second-order factor. The results are shown in Table 4.
## 3 Results
### Validation of AOD Estimation Methods
#### 3.2.1 Springtime
These results are consistent with those shown in Figure 4. It is noteworthy that there are some underestimations within the M3 results, particularly for high-AOD cases. Statistical analysis for the Seoul metropolitan area during springtime is shown in Figure 6 using the data from Figure 5. Statistical scores with correlation (R), root mean square error (RMSE), and mean bias (MB) are displayed in each plot. The R-values are all between 0.24 and 0.3, which is significantly lower than those reported in previous studies (e.g., [45; 46]) from WRF-CMAQ around the Korean Peninsula. The lower performance of the model can be attributed to two possible factors: (1) the small number of observations and (2) local features of observational points that are not included in the model. Because of error terms or extreme values from observation, local statistics can show bad performance. As indicated in Koo [47], the impact of hygroscopic growth factors on AOD can differ among observational locations. Further studies are required to address this issue. At 27 km resolution, the M1 method exhibited positive biases, with a value of 0.16 (0.42) for a maximum RH of 93% (96%). However, the MB of the M2 was half that of the M1 method, while the M3 only showed a negative bias of \(-0.08\), which is consistent with its  
Figure 5: Time series of daily aerosol optical depth (AOD) from the Yonsei-YSU Aerosol Robotic Network (AERONET) station (black) and estimated AOD from the forecast model during March–May 2016 (springtime). Estimated AODs in (**a**,**b**) represent the 27 and 9 km resolution model results, respectively. Yellow, green, blue, and red lines indicate M1 with maxRH = 93%, M1 with maxRH = 96%, M2, and M3, respectively. underestimation tendency. Visual differences between the coarse and fine resolutions are not significant, but the absolute value of MB decreased in the 9 km grid for both the M1 and M2 methods. The M3 estimation was not sensitive to the model grid size in terms of MB. The other statistical values saw no significant improvement when using the higher resolution model with any of the methods.
Antibiotics_**2015**, \(4\), 627-642; doi:10.3390/antibiotics4040627  
_Article_  
Genetic Screen Reveals the Role of Purine Metabolism in _Staphylococcus aureus_ Persistence to Rifampicin  
**[PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] ***  
Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health,  
Johns Hopkins University, Baltimore, MD 21205, USA; E-Mails: [EMAIL_ADDRESS] (R.Y.);  
[EMAIL_ADDRESS] (P.C.); [EMAIL_ADDRESS] (W.S.); [EMAIL_ADDRESS] (J.F.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +1-410-614-2975.  
Academic Editor: [PERSON]  
_Received: 29 September 2015 / Accepted: 27 November 2015 / Published: 7 December 2015_  
**Abstract:** Chronic infections with _Staphylococcus aureus_ such as septicemia, osteomyelitisitis, endocarditis, and biofilm infections are difficult to treat because of persisters. Despite many efforts in understanding bacterial persistence, the mechanisms of persister formation in _S. aureus_ remain elusive. Here, we performed a genome-wide screen of a transposon mutant library to study the molecular mechanisms involved in persistence of community-acquired _S. aureus_. Screening of the library for mutants defective in persistence or tolerance to rifampicin revealed many genes involved in metabolic pathways that are important for antibiotic persistence. In particular, the identified mutants belonged to metabolic pathways involved in carbohydrate, amino acid, lipid, vitamin and purine biosynthesis. Five mutants played a role in purine biosynthesis and two mutants, _purB_, an adenylosuccinate lyase, and _purM_, a phosphoribosylaminoimidazole synthetase, were selected for further confirmation. Mutants _purB_ and _purM_ showed defective persistence compared to the parental strain USA300 in multiple stress conditions including various antibiotics, low pH, and heat stress. The defect in persistence was restored by complementation with the wildtype _purB_ and _purM_ gene in the respective mutants. These findings provide new insights into the mechanisms of persistence in _S. aureus_ and provide novel therapeutic targets for developing more effective treatment for persistent infections due to _S. aureus_.  
**Keywords:**_Staphylococcus aureus_; persisters; purines; rifampicin
## 1 Introduction
### Dwell Time Delays  
Dwell times refer to the time a passenger train is stationary at a station, and the duration is based on the difference between the arrival and departure times [9]. Although dwell time delays are relatively small in isolation, these delays are relevant to this study since they can accumulate to a large portion of delay times for an entire journey [4; 10] and can have a similar impact on rail capacity as the maximum running speeds of trains [11]. Interms of quantifying dwell time delays, several indicators have been proposed in the past. The authors of [5], for example, mention the probability of a train arriving with a delay of more than three or nine minutes at the final station. Other studies mention the use of the average size of delays and the standard deviation of arrival times, thus highlighting the central tendency and variance. With regard to the variance in arrival times, the difference between the 90 th or 80 th percentile and the 50 th percentile of the measured arrival times is mentioned in the literature as well [6; 12]. Examples specific to dwell times are the use of the mean and standard deviation [13] and the ratio between the mean and 95 th percentile of the observed dwell times [10]. Focussing on the duration of dwell times alone is, however, not sufficient to understand the dynamics which lay ground to the rise of dwell time delays, as the dwell process is complicated, and multiple factors interact. In broad terms, dwell times consist of a fixed and a dynamic time element [14]. The fixed time element is defined by the technical elements, such as the time it takes to open and close the doors, and the dynamic element is defined by the time it takes for the passenger exchange to be completed [15]. For an extensive review on how passengers influence dwell times, we refer to [16].
## 3 Face Recognition System
### Face Detection  
Nowadays not all face-recognition applications need a face-detection module. In some cases, the input face images are already normalized. Therefore, there is no need for face detection. An example  
Figure 4: Face-recognition system schema.  
of such a face-recognition system is the criminal database. Law enforcement agencies save the models of the faces of individuals with criminal reports in the database. In order to uncover the identity and criminal records of an individual, all that is needed as input is the person's passport photo, which is compared to all the face models in the database. However, input images for a general face recognition system are not suitable for analysis, since they are likely to contain other objects besides faces. This is why there is a need for a face-detection module to be present. There are numerous challenges that face detection must overcome in order to accurately detect faces in an image. These challenges appear due to the uncontrolled environment where images are taken. These challenges can be attributed to factors such as:  
* Pose variation: The ideal situation for face detection would be in cases where all the input images contain faces in frontal pose. However, this is hardly realizable in practice. Furthermore, pose variation affects face detection algorithms' performance.
* Obscuring face features: The presence of elements such as glasses, a hat or a beard is another challenge for face detection.
* Face gestures: Face features change by a considerable amount as a result of different face gestures.
* Factors of the environment where the image is acquired: Different cameras and different environmental conditions can greatly affect the image quality, thus also affecting the visibility of the face in the image.
## 3 Network Architecture  
As shown in Figure 1, the layered quantum satellite network architecture is divided into the physical layer, quantum key pool layer, control layer, and application layer.  
Physical layer: It consists of the satellites and the ground stations. The satellites and ground stations are linked with each other to form the classical and quantum channels over the free space. The practical linking motion is conducted in this layer to transmit the classical signal and the quantum signal.  
Quantum key pool layer: It comprises the quantum key pools (QKPs). The QKP is an effective technique to save the secret keys for timely provisioning [17], which can store the secret keys in the storage medium for reducing the key wastage. The secret keys are obtained from the physical layer to this layer for storage. This layer will be detailed in Section 4.  
Control layer: It is made up with the software-defined networking (SDN) controller for the efficient resource allocation control [29; 30]. The SDN controller has two control modules, namely QKP layer control module and physical layer control module. The QKP layer control module is used to route the requests over the QKP layer and manage the rest of the keys for provisioning. The physical layer control module is used to control the establishment and removal of the link between the satellite and the associated satellite/ground station.  
Application layer: It consists of several applications for secret keys. This layer generates the requests for secret keys and sends the requirements to the control layer. This layer will finally obtain the secret keys from the quantum key pool layer via the key delivery application programming interface (API).
# Data Conversion Strategies for Effective Aviation Technical Support as a Service
## 2 Alternative ATSaaS Platform Architectures
### Data Conversion in the Context of ATSaaS
#### 2.3.2 Practical Challenges and Implementation Strategies for Data Standardization in ATSaaS
The establishment of ATSaaS standardization working groups--comprising representatives from airlines, MRO providers, regulatory agencies, and software vendors--can drive the development of practical, consensus-based standardization policies. These groups can play a key role in defining best practices, resolving integration challenges, and ensuring continuous alignment with evolving industry standards. Furthermore, automated data validation and compliance monitoring tools can be integrated into ATSaaS to enhance standardization efforts. These tools can assess incoming data against predefined standard formats, flag inconsistencies, and suggest corrective actions. By embedding automation into the data exchange process, ATSaaS can minimize errors, improve data integrity, and reduce the burden of manual verification for participants.
# Substrate-Driven Atomic Layer Deposition of High-\(\kappa\) Dielectrics on 2D Materials
## 2 Atomic Layer Deposition on 2D Materials  
The ALD is the most suitable deposition technique for the growth of ultra-thin and conformal dielectric layers on a substrate. The key strength of ALD is related to its peculiar deposition mechanism. It is based on sequential and self-limited chemical reactions between the properly selected chemical precursors and the active sites of the substrate surface, which results in the so-called layer-by-layer growth mode (see Figure 1a). This characteristic principle of growth allows for obtaining uniform, conformal, and pinhole-free depositions with angstrom-level control of the thickness in a large area [31]. However, ALD implementation on a pristine surface of graphene and other 2D materials is a difficult task due to the lack of dangling bonds working as active sites or functional groups for the adsorption of ALD precursors (as depicted in Figure 1b). The main consequence of this feature is a lack of deposition or the formation of inhomogeneous films (e.g., see the image of ALD-Al\({}_{2}\)O\({}_{3}\) on the pristine MoS\({}_{2}\) surface in Figure 1c).
## 3 Results and Discussions
### Application to Real Field Samples  
Samples collected in France and in Lebanon between July and September 2019 were analyzed according to the method developed. Samples were analyzed in triplicate and results with standard deviation are reported Table 4.  
Azithromycin was found in the whole samples with levels varying between 10.8 and 377.7 ng L\({}^{-1}\) in Lebanon, and between 3.75 and 17 ng L\({}^{-1}\) in France. Erythromycin and fluoxetine were only quantified in Lebanese freshwaters at levels between 121 and 268 ng L\({}^{-1}\), and 12.6 and 28.4 ng L\({}^{-1}\), respectively. Sotalol was found in the whole samples in France, with a concentration gradient; levels increased from upstream to downstream of the Arc river, from 2.1 to 5.0 ng L\({}^{-1}\). In Lebanon, this compound was only detected in the Litani river (44 ng L\({}^{-1}\)).  
Highest levels of targeted molecules in Lebanese freshwater may be explained by the low volumes of wastewater that are treated before discharge in the environment (below 10%) [19; 37], or simply lack of wastewater infrastructure with used waters only relying on open sewers, septic tanks, or simply discharge wastewater directly into the environment, as evidenced by very high counts of fecal and total coliforms, inorganic and organic pollutants recorded in rivers, watersheds and groundwater [19]. However, it should also be noticed that these molecules are known to be poorly eliminated in wastewater treatment plants, with removal rates between 1.9% (sotalol) and 31% (azithromycin) (Table 1). This explains that azithromycin and sotalol are detectable in freshwaters in France, especially at the outlets of wastewater treatment plants. Moreover, consumption of pharmaceuticals is particularly high in France with a per capita consumption of pharmaceuticals of 150 g vs. 49 g per capita in Lebanon [19]. This study is the first one to reveal the presence of sotalol, fluoxetine and erythromycin in Lebanese freshwaters. Overall, data on the occurrence of pharmaceuticals in Lebanese water are scarce. Only one paper reported, until now, the occurrence of pharmaceuticals in Lebanon [38] and has shown the presence of erythromycincin at levels up to 98 ng L\({}^{-1}\) in the Ibrahim river.  
These new data emphasize the needs to implement a large survey of pharmaceuticals at the country level in Lebanon.
## 4 Simulation-Based Performance Evaluation
### Comparison and Discussion
So, we can conclude that energy consumption is lower if the node density is high for all algorithms (except NONE). On the other hand, from Figure 7, it can be seen that in terms of energy consumption, NONE is the worst, and LTRT is the second worst. FTC, FLSS and k-Neighbors performance is very close to each other, but it is better than NONE and LTRT. Finally, Figure 8 demonstrates the average maximum communication range. FTC maintains the lowest maximum communication range comparing it with other algorithms, which means that the most energy-consuming node in the network consumes the lowest power. Furthermore, Figure 9 evaluates the Energy Expended Ratio (EER) (define \(EER=100\times\frac{CR_{aya}}{CR_{max}}\%\)) [28]. EER is a parameter wanted to be as small as possible. NONE is the highest; FTC is very similar to LTRT; while k-Neighbor and FLSS are relatively lower. FLSS claims that it has achieved near optimal performance. Figure 6: Average node degree (k = 3).
###### Abstract  
This paper uses a combination of approaches to evaluate the viscoelastic phenomenon in high-molecular-weight polymers (24-28 M Daltons) used for enhanced oil recovery (EOR) applications. Rheological data were cross-analyzed with single- and two-phase polymer flooding experiments in outcrop cores and micromodels, respectively. First, the impact of semi-harsh conditions (salinity, hardness, and temperature) was evaluated. Second, the impact of polymer degradation (sand face flow), focusing on the viscoelastic properties, was investigated. Finally, polymer viscoelastic properties were characterized, proposing a threefold rheological approach of rotational, oscillatory, and elongational behavior. Data from the rheological approaches were cross-analyzed with core flooding experiments and performed at a room temperature of 22 \({}^{\circ}\)C and at a higher temperature of 55 \({}^{\circ}\)C. The change in polymer viscoelastic properties were analyzed by investigating the effluents from core flooding experiments. Oil recovery experiments in micromodel helped our understanding of whether salinity or hardness has a dominating impact on in situ viscoelastic polymer response. These approaches were used to study the impact of mechanical degradation on polymer viscoelasticity. The brines showed notable loss in polymer viscoelastic properties, specifically with the hard brine and at higher temperature. However, the same polymer solution diluted in deionized water exhibited stronger viscoelastic properties. Multiple flow-behaviors, such as Newtonian, shear thinning, and thickening dominated flow, were confirmed through pressure drop analysis against interstitial velocity as already reported by other peer researchers. Turbulence-dominated excessive pressure drop in porous media was calculated by comparing core flood pressure drop data against pressure data in extensional viscometer-rheometer on a chip (eVROC(r)). In addition, a significant reduction in elastic-dominated flow was confirmed through the mechanical degradation that happened during core flood experiments, using various approaches. Finally, reservoir harsh conditions (high temperature, hardness, and salinity) resulted in a significant reduction in polymer viscoelastic behavior for all approaches.  
**Keywords:** viscoelasticity; degradation; brine-hardness; rheology; elongational viscosity; enhanced oil recovery (EOR)