esa-sceva/satcom-chunk-collection huggingface dataset:
# The Oral Microbiome in Children with Black Stained Tooth
## 1 Introduction  
Black stain (BS) is a characteristic, extrinsic dental discoloration that occurs on the cervical third of the tooth and follows the contour of the gingival margin, particularly in primary dentition [1]. BS is thought to be a special form of dental plaque related to calcification because it contains an insoluble ferric salt, which is likely ferric sulfide, and has high calcium and phosphate contents [2]. The ferric sulfide may be formed through a reaction between hydrogen sulfide produced by bacterial action and iron in saliva or gingival exudates.  
BS occurs at any age, but there is a higher prevalence in childhood that decreases in adolescence and adulthood [3]. Studies have shown no difference in occurrence between the sexes [1]. Researchers have been interested in BS because of its correlation with the low caries experience [2, 4]. However, a recent study of more than 3000 people found no association between dental caries and BS [5].  
Many studies have examined BS-related bacteria, but the causative bacteria is still not clear [1; 3]. Early ultrastructural examinations of BS demonstrated that the black material is a ferric salt, probably ferric sulfide, formed by the reaction between sulfide produced by bacterial action and iron in the saliva or gingival exudate [2; 3]. The most commonly cultivated bacteria from BS teeth were _Actinomycetes_ with the detection of rarely pigmented Gram-negative rods [6]. Black-pigmented bacteria were found primarily in extrinsic tooth stains [6; 7]. Among black-pigmented bacteria, _Prevotella intermediia_ and _Prevotella nigrescens_ are the ones most frequently found in the oral cavity and associated with BS [3]. [PERSON] et al. [6] reported that _Porphyromonas gigivalis_, _P. intermedia_ and _P. nigrescens_ were related to BS using DNA-DNA hybridization for 40 taxa, whereas [PERSON] et al. [7] suggested that _P. gingivalis_ and _Prevotella melaninogenica_ were absent and _Actinomycetes_ sp. and _Aggregatibacter actinomycetemcomitants_ were present in BS by means of polymerase chain reaction (PCR) and electrophoresis gel on the agarose medium for 200 people. This conflict was partly caused by limitations in the analysis methods. As a result, more advanced analysis methods are needed.  
Recent advances in sequencing technologies, such as 454 pyrosequencing, have provided a deep understanding of the metagenomic diversity of the human oral microbiome [8]. Pyrosequencing can identify bacterial sequences and their amounts to give both qualitative and quantitative information of the oral microbiome, including uncultivable microorganisms [8]. For example, high-throughput pyrosequencing has revealed that dental plaques pooled from adults contain approximately 10,000 microbial phylotypes, with the ultimate diversity of oral microbiomes being estimated to contain approximately 25,000 phylotypes [9].  
The aim of this study was to identify the oral microbiome in BS using pyrosequencing and to test our hypothesis that the oral microbiome of BS in children might be related to black-pigmented bacteria.
## Materials and Methods
### RNA Isolation and Real-Time Reverse Transcription-Polymerase Chain Reaction (RT-PCR)  
Total RNA was isolated using the easy-spin(tm) total RNA extraction kit (iNtRON Biotechnology, Gyeonggi-do, Korea), and cDNA was synthesized using the GoScript(tm) Reverse Transcriptase (Promega, Madison, WI, USA). Real-time PCR was performed on the synthesized cDNA using the TB Green(tm) Premix Ex TaqMan(tm) II (TaKaRa Bio, Otsu, Japan) on the ABI QuantStudio 3 (Applied Biosystems, Foster City, CA, USA). The primer sequences (5'-3') used in the experiments were shown in Table 1. All gene expression values were normalized to _Actb_ (\(\upbeta\)-actin).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Genes** & **Forward Sequence** & **Reverse Sequence** & **Ref.** \\ \hline _Nos2_ & \(\text{GCGAAAGGTCATGGCTTCAC}\) & \(\text{CTGCTCCATGCAGACAACCT}\) & This study \\ _Plgs2_ & \(\text{CATCCCTTCCGCGAAGTT}\) & \(\text{GGCCCTGGTGTAGAGAG}\) & This study \\ _Tnf-a_ & \(\text{TGTCCCTTTCACTACTGGC}\) & \(\text{CATCTTTTGGGGAGTGCCT}\) & [13] \\ _IL-1b_ & \(\text{AACTGTGTCCTGAACTTG}\) & \(\text{GAGATTTGAAGCTGGAGTCTCT}\) & [14] \\ _IL-6_ & \(\text{GGAC
## 3 Results and Discussion
### Thermal Properties
#### 3.2.1 Thermogravimetric Analysis (TGA)
Figure 2: Thermogravimetric analysis (TGA) thermograms of PLA and nanocomposites at various nanosilica content (2–24 vol%).
# Directional Core Drilling as an Approach to Reduce Uncertainty in Tunneling Construction
## 3 Methodology for the Uncertainty Economic Valuation
### Costs Related to the Drainage of the Tunnel  
The total volume of water extracted from a tunnel is the sum of the different stretches (V\({}_{\rm T}=\sum\)V\({}_{\rm i}\)). Further, additives, such as flocculants or coagulants, are required for its management, as expressed by Equation (18), with an average cost of EUR 0.28 per m\({}^{3}\) for the water treatment.  
\[\rm C_{T}=c_{A}\cdot V_{T} \tag{18}\]  
Here, \(\rm C_{T}\) is the total cost of the water treatment (EUR), \(\rm c_{A}\) is the cost for each cubic meter of water (EUR/m\({}^{3}\)), and \(\rm V_{T}\) is the volume of water extracted (m\({}^{3}\)).  
The average water flow in a tunnel is defined by Equation (19), and the electric power of the water treatment system is calculated by Equation (20), assuming that the power consumption is directly proportional to the amount of water treated.  
\[\rm Q_{m}=\frac{V_{T}}{86400\cdot T_{f}} \tag{19}\]  
\[\rm P_{T}=c_{T}\cdot Q_{m} \tag{20}\]  
Here, \(\rm Q_{m}\) is the mean water flow (m\({}^{3}\)/s), \(\rm T_{f}\) is the time required to excavate the tunnel (days), \(\rm c_{T}\) is the ratio between the power and the water flow (kW/m\({}^{3}\)/s) (with a common value of 1000 kW/m\({}^{3}\)/s), and \(\rm P_{T}\) is the total electrical power of the wastewater treatment plant (kW).  
In addition, water must be pumped in a tunnel when there is a negative slope in the excavation direction, which requires electrical power that is proportionate to the tunnel length [44]. The mean power required in a stretch from Lo to Lf is defined by Equation (21).  
\[\rm P_{w}=c_{L}\cdot\frac{L_{o}+L_{f}}{2} \tag{21}\]  
Here, \(\rm P_{w}\) is the pumping power (kW), and \(\rm c_{L}\) is the proportion factor. In a tunnel with a negative slope of between 1% and 5%, the value of \(\rm C_{L}\) is approximately 0.25 kW/m, and it increases to 0.50 kW/m for negative slopes between 5% and 15%.  
Thus, the total energy required for water management is the sum of the water pumping and treatment, assuming that the whole system works 24 h a day (Equation (22)). Equation (23) defines the total energy cost.  
\[\rm E=24\cdot T_{f}\cdot(P_{T}+P_{W}) \tag{22}\]  
\[\rm C_{E}=c_{E}\cdot E \tag{23}\]  
Here, \(\rm E\) is the overall energy consumption (kWh), \(\rm T_{f}\) is the time required to excavate the tunnel (days), and \(\rm c_{E}\) is the energy cost (EUR/kWh), which depends on the type of energy generation. We assumed an energy cost of EUR 0.08 per kWh in this study.
## 3 Experimental Results and Discussion
### Stage 2--Production
#### 3.1.1 EDM Fluid--Gravimetric Analysis  
The uptake values for the bulk plate specimens immersed in dielectric fluid are shown in Figure 6.  
When compared with the behaviour of other fluids, the dielectric fluid shows an unusual evolution, the most notable characteristic being a negative fluid uptake; however, this variation is slight and in the order of 0.1%.  
Because the uptake was unstable and there was weight loss in the bulk plates, no diffusion law could be approximated to the experimental results.  
Fourier Transform Infrared Spectroscopy (FTIR)  
In order to understand the evolution of the adhesive bulk plate samples on the dielectric fluid, an FTIR analysis was carried out on four samples: two unaged bulk plate  
Figure 5: Specimen shear testing setup.  
samples and two bulk plate samples aged in dielectric fluid. The FTIR spectra obtained for the bulk plate samples can be seen in Figure 7.  
When comparing both obtained spectra, it is possible to see that the chemical profiles of both samples are very similar, with the only noticeable variation occurring in the region of 2840\(-\)3000 cm\({}^{-1}\). From the infrared spectroscopy tables, an increase in this frequency region corresponds with an absorption of alkanes, which is a common chemical compound found in machining and lubricating oils. From this analysis, one can conclude that there could have been a slight variation in the composition of the polymer most likely in the outer layer of the specimen, caused by the gravimetric ageing.  
Nonetheless, the reasoning behind this behaviour is still highly speculative. In general, when looking at the order of magnitude of the values of uptake, the high standard deviation,  
Figure 6: Experimental results of the gravimetric analysis in dielectric fluid.  
Figure 7: FTIR spectra obtained for bulk plate specimens, showing chemical changes in the alkane region between the control/unaged (green and pink lines) and aged samples (black and yellow lines).  
the nearly unseen change in the FTIR spectra from unaged to aged samples and the possible experimental variability, the main conclusion is that the EDM fluid does not affect the adhesive by itself.
## 4 Classification Results and Discussion
### Differences between Upscaling and Downscaling  
In Table 5, comparing R10 to G10, N10, P10, W10, and H10, the higher spatial resolution does not always lead to higher classification accuracy because of the within-class spectral variability increases at the same time [66]. However, in general, downscaling plays a more important role than upscaling in LULC classification based on Sentinel-2 imagery. Figures 3 and 4 clearly show that the upscaled image with a coarser spatial resolution achieved the lowest accuracy in all feature sets. This is because the nearest neighbor resampling not only loses the spatial details but also increases the proportion of mixed pixels by artificially combining four adjacent pixels into one, both of which have a detrimental effect on classification accuracy [67].  
Figures 3 and 4 show clearly that based solely on spectral bands, the OA increased by 1.84% to 3.85% by different pansharpening algorithms compared to that of upscaling. In addition to OA,  
Figure 5: Feature importance of ten spectral bands using data sets obtained by six downscaling methods.  
Figure 6: Class accuracy using data sets obtained by different downscaling methods.  
its effects on specific classes, e.g., shrubs, agricultural land, built-up area and roads, are more remarkable. Particularly, the OA of agricultural land finally increased to 72.5% from 58.43% by nearest neighbor resampling, making the LULC maps valuable for practical applications by satisfying the lowest precision requirement.
## 4 Numerical Example  
### Data Information  
Two datasets are used to evaluate the effectiveness of the proposed model. The first dataset was the failure data from the PIG project, which is a platform for analyzing large datasets. The PIG project was used to display the parallelized data analysis programs. The PIG project has had five releases, with versions 0.2.0 through 0.5.0 defined as Release 1, versions 0.6.0 through 0.8.0 defined as Release 2, versions 0.8.1 through 0.9.2 defined as Release 3, version 0.10.0 defined as Release 4, and versions 0.10.1 through 0.12.0 defined as Release 5. A total of 1814 failures were observed monthly between April 2009 and October 2013 [47]. The second dataset comprises the failure data of a tandem computer with multiple central processing units inside a single computer. The system was tested for 10,000 h, and 100 failures occurred during testing. The data exhibited a concave shape with many failures at the beginning, followed by a gradual decrease [48].
## 2 Materials and Methods
### Waves  
Wave data was collected from three moored buoys (Figure 1) managed under protocols between the IH and the \"Administracao do Porto do Douro e de Leixoes\" (APDL) and \"Administracao dos Portos de Sines e do Algarve\" S. A. (APS) between the time periods of 1996 and 2019 for Leixoes; 1988 and 2019 for Sines and 1986 and 2019 for Faro. The analysis of the Leixoes buoy is used to characterize the waves in central and north-western Areas A, B, and C, the Sines buoy to characterize the waves in Area D and the Faro buoy to characterize the waves in Area E.
## 4 Results  
### Questionnaires Results  
The SSQ questionnaire was filled at four different moments during the experimental sessions, as described in the previous section. The initial response (TS1) was subtracted from each of the three later responses to obtain the change in total sickness measure (ATS2, \(\Delta\)TS3, and \(\Delta\)TS4) as metrics to determine discomfort on the HMD (Figure 11).  
This figure shows the distribution for each of the three metrics considered. The results show a high dispersion. In 45% of the users, there was no change in the Total Score during the performance of the trials (\(\Delta\)TS2 to \(\Delta\)TS4 are equal to zero). The percentage of \(\Delta\)TS scores greater than 10 increases from stage 1 (\(\Delta\)TS2) to stage 3 (\(\Delta\)TS4), indicating users experienced a greater increase in discomfort during this stage (\(\Delta\)TS4).  
The SUS provides a broad global view of subjective assessments of usability. The SUS was administered to 29 participants. The results show a high mean SUS score across all participants at 82.9 (SD 11.6). Eighty-three percent of the participants assess the VR system as \"Acceptable\" and seventeen percent as \"Marginal\" (Figure 12), according to the acceptability scores shown in Figure 4. There is no user who rated the system as \"Not acceptable\" (SUS less than 50).  
Figure 10: Grouped Scatter of “Event” by “Percentage of attention time”.  
Figure 9: Boxplot chart of “Average error of distances calculated in VR” filtered by Event.  
The PQ was administered to 29 participants and the results show a high mean PQ score across all participants at 5.3 (SD 0.3).
## 3 Proposed Method
### Player and Ball Tracking  
In this work, we proposed a tracking algorithm for reliable tracking. In the proposed tracking algorithm, the get_player_positions function processes data obtained from the YOLOv8 object detection model to extract player and basketball center coordinates, bounding boxes, and attributes. To improve the accuracy of detecting players and the ball, a total of 14,425 image data, consisting of 53,320 player data, 13,527 referee data, 5591 net data, and 4938 ball data are used for training in fine-tuning, with 300 epochs and 16 batch sizes for the model update from the athlete recognition dataset [21]. Only objects with a confidence score of 0.5 or higher are considered. The basketball is linked to the nearest player by calculating the Euclidean distance between the basketball's center coordinates and the center coordinates of all detected players. The compute_iou function calculates the Intersection over Union (IoU) between bounding boxes to serve as the basis for matching objects. Objects with an IoU of 0.3 or higher are considered the same entity, while objects with an IoU below this threshold are treated as unmatched. The match_players function generates a cost matrix based on IoU values and employs the Hungarian algorithm to perform optimal matching between bounding boxes from consecutive frames. The track_players function updates the state of tracked objects based on matching results, incrementing a missed frames counter for unmatched objects. Objects that remain unmatched for more than five frames are removed from tracking. Finally, the player_tracking function performs object tracking across all frames, exporting results in the JSON format. The saved data include each object's ID, position information, bounding boxes, and relationships with the basketball. Through these functions, the algorithm maintains consistent player tracking and object association throughout the video, ensuring robust detection and tracking of players and the basketball.  
Figure 2: Court segmentation in local view.
# BVLOS Unmanned Aircraft Operations in Forest Environments
## 4 The International Regulatory Environment
Many of these issues will be covered in Section 5.3, which details other considerations such as training and maintenance, which are organisation-specific due to differences in the nature of operations and types of aircraft being operated. Parachute systems, combined with minimum safe altitudes, the wearing of personal protective equipment, and ensuring staff are trained on first aid and emergency responses, are effective ways of mitigating ground risks [21] and are suitable for the forestry environment. One that is particularly relevant is considering how any fire risk might be managed, which is particularly important when using a petrol-powered unmanned aircraft. Typically, there will be fuel shut-off valves that activate upon a heavy impact to try and contain fuel, combined with the ability to quickly respond in the event of an emergency (anecdotal examples include having a manned aircraft operator on standby or a second unmanned aircraft that can be used to see if the wreckage has ignited in the event of an accident). Many forest owners also maintain their own firefighting infrastructure; therefore, close coordination of BVLOS operations, with the relevant firefighting resources notified of the operation and placed on standby, can further mitigate risks. In terms of formalising the issues discussed in this section, one method that is being increasingly applied around the world is the Joint Authorities for Rulemaking on Unmanned Systems' (JARUS) Specific Operations Risk Assessment (SORA) process [37]. This involves classifying the airborne and ground risks, as well as applying mitigations to reduce those to an acceptable level. Its annexes provide details around how this process works. At the time of writing this article, there are 63 regulatory authorities from around the world that are part of the JARUS community [38], meaning that this approach is arguably the most widely accepted internationally. Nonetheless, the way in which the approach is used does vary internationally, and countries will typically use the process as part of an application, rather than in lieu of one. _The International Regulatory Environment: Executive Summary_  
* There are currently no standards and recommended practices (SARPS) that are internationally applied. * ICAO has released model regulations, which are analogous to regulations in many major jurisdictions. * Operators either need to use special use airspace to be separated from manned aircraft or must fulfil the see, detect, and avoid principle, through detect and avoid (DAA) systems, to allow for BVLOS flight
## 2 Related Work
### Deep-Reinforcement-Learning-Based Approaches
ALVS [32] introduced a deep reinforcement learning framework for video live streaming, where adaptive playback speed and video quality level were selected for the next segment to enhance QoE. Additionally, GreenABR [33] proposed a deep-reinforcement-learning-based ABR scheme with the objective of optimizing energy consumption during streaming sessions without compromising QoE. The authors in [34] proposed a deep reinforcement learning approach aimed at enhancing the QoE in video applications. This was achieved by dynamically adjusting IEEE 802.11 parameters to improve network conditions and maintain a higher QoE for users. However, it is important to note that this approach employed traditional ABR with fixed rules in a dynamic environment. Addressing the need for effective bitrate adaptation in mobile video streaming, [35] introduced DQNReg, an advanced deep reinforcement learning approach that optimizes QoE. DQNReg outperforms other methods, displaying faster convergence, higher QoE scores, reduced rebuffering, and superior video quality maintenance. In [36], a proximal-policy-optimization-based DRL (PPO-ABR) approach was presented for adaptive bit rate streaming. This leverages DRL to maximize sample efficiency and elevate video QoE. The authors tackle the challenge of ensuring high QoE in video streaming within dynamic 5G and beyond 5G (B5G) networks, underscoring the limitations of existing ABR algorithms. Several DRL-based approaches and frameworks have been presented in the literature, some of which employ intricate neural network architectures, often combining multiple neural networks. In contrast to the cited works, we proposed a novel DRL-based approach utilizing an LSTM neural network that takes into account the holistic environment when determining the next bitrate. In our DRL approach, a single deep neural network approximates the policy. We have incorporated a novel factor known as \"distance\" to aid in bitrate selection, all in the pursuit of enhancing the QoE. Our primary objective is to deliver optimal dynamic adaptation in highly complex and dynamic environments.
# Automated Multiple Concrete Damage Detection Using Instance Segmentation Deep Learning Model
## 2 Mask and Region-Based Convolutional Neural Network (Mask R-CNN)
### Feature Extraction by Convolutional Neural Network and Feature Pyramid Network  
For feature extraction, Mask R-CNN takes an input image through convolutional neural network (CNN), which is depicted as five sequential gray boxes with varying width and height in Figure 1. CNN is an aggregation of filters with trainable weights and bias. The weights and bias decides features to be emphasized or dismissed in an input image. While training, the weights and bias are optimized to carry out a given task on a given dataset. In Mask R-CNN, ResNet-101 [35] is used for major feature extractor. The gray boxes from C1 to C5 in Figure 1 shows the 5 stages of ResNet-101. Their increasing width and decreasing height shows the increasing depth and the decreasing resolution of convolutional layers in ResNet-101. The feature maps created by CNN are fed to feature pyramid network (FPN) [36] in the next step. Since most of CNN downsizes an input image during feature extraction, FPN restore spatial information that are potentially lost while downsizing. The feature maps extracted by FPN is restored to five different sizes in order to detect objects of different sizes. To express this conceptually in Figure 1, the size of the blue feature map from P5 to P1 is represented with increasing height.