esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setup
### Measurement System  
The used infrared camera is an IR8800-type from the manufacturer InfraTec GmbH (Gostritzer Str. 61-63, 01217 Dresden, Germany), which is sensitive to wavelengths between 7.7 and 10.2 m and has a noise-equivalent temperature difference (NETD) of 25 mK. To achieve a sufficient image magnification, a 200 mm telephoto lens is used for the field measurement. For the measuring distance of approximately 190 m, an image field with 9.12 m \(\times\) 7.30 m, represented by 640 \(\times\) 512 px\({}^{2}\), is the result. The evaluated rotor section was between 8 m and 17 m radial distance from the nacelle and the minimal chord length in the evaluated part is 2.8 m. An in-process measurement is shown in Figure 1.  
The field of view was fixed to the horizontal rotor position and the aforementioned radial rotor position, respectively. It covers the complete blade profile from the leading edge to the trailing edge. Triggering with an external visual camera has been previously reported and is possible, but here, the image acquisition is thermally triggered each time one of the rotor blades passes the defined region of interest due to an increased detected mean temperature within that region.  
For comparable field measurements, the following requirements concerning the measurement and flow conditions are needed:  
* The wind speed must be permanently between the switch-on and switch-off speed of the turbine. In addition, operation in the part-load range is desirable, since in part-load operation there is a linear dependence between the wind speed and the rotor  
Figure 1: The measurement setup with the IRT camera that aims at the wind turbine from a measuring distance of 190 m.  
speed and thus the optical detection of the rotor speed allows an approximation of the incident flow speed [31].
* The solar heating of the rotor blades is constant in time.
* For a fixed geometric alignment between the camera and the observed rotor blade section, no yawing or pitching may occur during the measurement period.  
Therefore, the recorded image series are filtered with regard to these requirements, i.e., only time periods that meet the requirements are evaluated.
## 2 Materials and Methods
### Deltoid Segmentation Model
A hybrid parameter, which computes both Dice loss and cross-entropy loss, was utilized as the loss function. The Adam optimizer was used with a learning rate of 0.0001 and a weight decay coefficient of 0.00005. The SwinUnetR configurations were set for image dimensions of 64 \(\times\) 64 \(\times\) 64, a network feature size of 48, and a maximum number of iterations of 40,000. The model inference (i.e., ML mask generation) was followed by a pre-defined post-processing pipeline, in which we performed island removal and surface smoothing. Finally, a quantification pipeline measured numerous metrics, such as: fatty infiltration (FI--percentage of fat within the muscle based on Hounsfield unit values), deltoid volume, normalized deltoid volume (deltoid volume normalized across all patients of the same age and gender), and radiomic features [23; 24; 26]. Please refer to Figure 1 for additional information related to the model architecture. Figure 1: **Left**: Sample of training data utilized to tune the segmentation model. **Top Right**: SwinUnetR model architecture used for training the segmentation model. **Bottom Right**: Training and validation performance.
# A Comprehensive Review of Mechanisms, Predictive Techniques, and Control Strategies of Rockburst
## 5 Rockburst Prediction Methods
### Stress Methods  
The empirical stress estimate method is widely used for underground mining rockburst prediction and prevention. This method uses various indices and indicators to assess the rock mass, with geomechanical properties of undamaged rock and in-situ or produced stresses as crucial indicators of rockburst severity [146]. Observations such as drill core descriptions can provide valuable information on rock mass attributes and help to locate high-stress zones [147]. Brittleness, which refers to a decline in strength caused by the connection between grains, is used to assess rockburst intensity [32; 148]. The rock brittleness coefficient is defined as the ratio of UCS to the tensile strength of undamaged rock based on experimental findings and site assessment [149], as expressed in Equation (2):  
\[B_{i}=\frac{\sigma_{c}}{\sigma_{t}} \tag{2}\]  
where \(\sigma_{c}\) is the uniaxial compressive strength of the rock and \(\sigma_{t}\) is the tensile strength of the rock. Both values are in MPa (Table 6). The mean stress index and strength index, ratios of rock strength to stress, are empirical approaches to rockburst prediction.  
There is ongoing debate on how to accurately determine the in-situ rock strength, with different researchers proposing methods such as crack initiation stress or crack damage  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Rock Brittleness Coefficient** & **Risk of Violent Rupture** \\ \hline \(B_{i}>40\) & No rockburst \\ \(26.7<B_{i}<40\) & Weak rockburst \\ \(14.5<B_{i}<26.7\) & Moderate rockburst \\ \(B_{i}<14.5\) & Strong rockburst \\ \hline \hline \end{tabular}
\end{table}
Table 6: Rockburst intensity depending on the brittleness coefficient [149].  
Figure 10: Academic and scientific approaches of comprehensive rockburst prediction (modified after ref. [145]).  
stress [150; 151; 152]. However, in-situ strength and stress remain poorly understood, partly due to the influence of excavation shape and boundary roughness on redistributed stress near the excavation boundary (\(\sigma_{\theta}^{\text{max}}\)) [153; 154].
## 2 Methods
### Methodology for Validating the Performance of the Algorithmic Process  
To validate the performance of the developed algorithmic process, we quantified the sensitivity and precision in the detection of the total count of apneic or hypopneic episodes irrespectively of the patient they belong to. These two metrics were defined as:  
\[\text{Sensitivity}=\frac{TP}{TP+FN}=\frac{TP}{Total\ count\ of\ all\ reference\ events} \tag{1}\]  
\[\text{Precision}=\frac{TP}{TP+FP}=\frac{TP}{Total\ count\ of\ all\ detected\ events} \tag{2}\]  
where TP is the count of True Positive detections, FN is the count of False Negative detections, and FP is the count of False Positive detections. An episode was considered as TP if there was an overlap between the detected and annotated event. If an annotated episode did not present any overlap with the detected events, it was considered as non-detected, i.e., as FN. If a detected episode did not have any overlap with the annotated events, it was eventually counted as FP.  
For each detected episode, we also extracted the relationship between the detected event duration and the duration of the corresponding reference event as annotated by the clinical team. The determination of the algorithmic performance in the level of the events was the first purpose of this work, focusing on the possibility to accurately detect in time the apneic episodes and extract information relevant to each event.  
In addition to the above factors, we also examined the duration distribution of all detected events--both true and false positive--compared to the distribution of the reference annotated events and we compared the MAD index estimated for each patient with the reference MAD index, derived by the reference episodes.
# Friction and Wear Performances of Materials for Wind Turbine Sliding Bearing Bushes
## 4 Wind Turbine Sliding Bearing Bush Friction and Wear Properties Analysis
### Surface Wear Morphology Analysis of Two Groups of Bearing Bush Materials  
The evaluation of material friction and wear properties encompasses critical indices, with the friction and wear of coating surfaces playing a pivotal role [14; 38]. The three-dimensional wear morphology of these surfaces under varying loads was scrutinized using an ultra-depth microscope, as detailed in Figure 11. Scratches on the CoCrFeNiMo high-entropy alloy coating exhibited regular patterns, featuring a relatively smooth edge transition. Notably, the depth of the wear mark first increased and then decreased with the increase in applied load. By contrast, both the length of the wear mark and the wear surface area exhibited an initial decrease followed by an increase. Conversely, the scratches on the Ni-Cr-Mo-Si alloy coating exhibited relatively irregular profiles. Nevertheless, the overall wear marks were characterized by small and smooth patterns. When the applied load increased, the wear length exhibited an initial decrease followed by an increase, while both wear depth and surface area exhibited an increasing trend. The wear mechanisms are elucidated in Figure 12. Abrasive wear was the dominant mechanism in the CoCrFeNiMo high-entropy alloy coating. With the increase in the applied load, the coating experienced peeling, with partially detached wear chips adhering to the grinding pair. This resulted in more violent plastic deformation on the surface of the wear marks, generating furrows. By contrast, the wear mechanism of the Ni-Cr-Mo-Si alloy coating primarily involved both adhesive and abrasive wear. With an increase in the applied load, more abrasion marks and furrows appeared on the worn surface, intensifying the surface peeling phenomenon to varying degrees on both sides.  
Figure 10: Friction coefficient and wear quality of different materials: (**a**) 30 N, (**b**) 40 N, (**c**) 50 N, (**d**) average coefficient of friction and wear amount of comparison.
## 2 Achieving Error Resilience: Design Decisions
In a Raptor code for the inner LT code, even in an error-free channel, there is a small probability that the decoding will fail. However, that probability converges to zero in polynomial time in the number of input symbols [32] and a similar analysis applies to an outer LDPC [33]. In this paper, the probability of decoder failure is modeled statistically by the following equation from [34]:  
\[ \begin{split} P_{f}(m,k)&=1\hskip 113.811024 pt\text{if} \hskip 113.811024 ptm<k\\ &=0.85\hskip 2.845276 pt\times 0.567^{m-k}\hskip 5.690551 pt\text{if} \hskip 113.811024 ptm\geq k\end{split}  \tag{1}\]  
where \(P_{f}(m,k)\) is the decode failure probability of the code with \(k\) source symbols if \(m\) symbols have been successfully received (and 1-\(P_{f}\) is naturally the success probability). Notice that for \(k>200\)[34] the model of Equation (1) almost ideally models the performance of the code. This implies that, if block symbols are used, approximately 200 blocks should be received before reasonable behavior takes place. Therefore, we require packets of at least 200 bytes in our scheme. In summary, the resulting video communication system used by us over a WiMAX link has protection at several layers of the protocol stack. Data randomization is applied at the PHY layer to avoid runs of 1 s or Os. Subsequently, one of WiMAX's modulation and coding schemes is selected. Mobile WiMAX offers modulation by one of Binary Phase-Shift Keying (BPSK), Quadrature Phase-Shift Keying (QPSK), 16-Quadrature Amplitude Modulation (QAM), or 64-QAM in descending order of robustness. The mandatory convolutional coding rate is selected from 1/2, 2/3, 3/4, and 5/6 in ascending order of protection. There are other WiMAX PHY-layer protection options such as Turbo coding or PHY-layer H-ARQ which were not configured in the simulation experiments of Section 5. Thus, bit-level FEC protection is first applied at the PHY layer and additional FEC is applied in our scheme at the application layer by means of a rateless channel coder. However, unlike conventional block-based AL channel coding, a variant of packet-level channel coding, we used intra-packet byte-level rateless channel coding. By selecting this form of application-layer channel coding, we were able to use adaptive rateless coding enabled by means of a simple, low-latency H-ARQ mechanism.
# Non-Contact and Non-Intrusive Add-on IoT Device for Wireless Remote Elevator Control
## 2 Related Work
### Elevator Panel Buttons and COVID-19 Pandemic  
The coronavirus disease 2019 (COVID-19) pandemic highlighted the need to reevaluate the way people interact with shared objects such as door handles, button panels, and handrails in order to reduce the contagion risk of infectious diseases. During the outbreak of the pandemic, people were aware of the risks of coming into contact with shared objects, and the importance of maintaining adequate hand hygiene as prevention measures to avoid infection. This may be one of the causes of the low COVID-19 infection rates attributed to contact with fomites reported in the scientific literature [47; 48]. However, [PERSON] et al. [49] measured the environmental contamination of SARS-CoV-2 in a hospital in order to assess the role it may have in the transmission of the virus, reporting that the surface of the elevator buttons were positively contaminated with SARS-CoV-2 in 42.86% of the case samples analyzed. These findings confirm the need to consider the physical contact with shared button panels as a path for pathogen transmission.  
In the context of the COVID-19 pandemic, the concept of the Internet-of-Things has fostered the development of new multi-sensor devices [50; 51]. Some alternatives designed to specifically mitigate the risk of infection in elevators are the deployment of automatic disinfection systems using UV-C light [52] and the implementation of contact-free buttons [53]. Alternatively, [PERSON] et al. [4] designed an epidemic prevention and protection plan to be followed when using an elevator, along with the proposal of a contact-free elevator device that allows interacting with the elevator through hand gestures or by using a mobile phone app. Once the user has entered the elevator car, the destination can be selected using smart cards, QR codes, voice, infrared induction buttons, or face recognition. Similarly, [PERSON] et al. [5] explored the control mechanisms for developing a contact-free elevator-ride system based on a combination of speech recognition, contact-free perceptual buttons, gesture-recognition sensors, an app, and a web page browser.  
The use of a web application and QR codes proposed by the cited works were the inspiration for the implementation of the add-on IoT device proposed in this paper. In this case, the development of a non-intrusive add-on device is proposed as a quick response tool to fight against pathogen transmission in places were the modification of touch-based elevator-control panels has not been considered, either due to its cost or its practical availability.
## 4 Discussion  
Laser treatment was seen to convert silicon nitride powder into silicon phase and also into amorphous phase. Previous studies have demonstrated this laser cladding chemical structure as well as new surface properties [36; 37; 38; 39]. Laser cladding changes the morphology of the substrates to which it is applied, but the final roughness values seem more dependent on the physical properties of the material and applied voltage than on initial surface roughness. For example, two samples with a similar initial surface roughness, Ti6 Al4V and zirconia-toughened alumina (ZTA), showed completely different morphology after treatment. Initial surface roughness did, however, play a clear role in cladded layer adhesion, as can be observed by comparing the ZTA (rough) and pure zirconia (polished) images. Initially, smooth surfaces resulted in partial delamination of the coating [37]. The voltage applied could also be correlated with the composition of the cladded layer: higher values resulted in a lower nitrogen quantity.  
Raman spectroscopic images obtained for the different substrates showed the appearance of a strong signal at about 520 cm\({}^{-1}\) and related to the presence of Si-Si bonds formed due to the release of nitrogen from the surface. Depending on the position on the spectra, the Si-Si bonds could be associated with the presence of amorphous or nanocrystalline silicon, the second being more abundant at higher voltages.  
The antibacterial study was focused on the Gram-positive _S. epidermidis_ mainly because it is one of the first causes of infection in biomedical implants. Moreover, the role of cladded silicon nitride coatings on Gram-negative bacterium test was already presented in previous work [49].  
The in vitro bacteria counts summarized in Figure 7 confirm that all silicon nitride-based cladded layers have a time-dependent antibacterial effects. The trend observed in Y-TZP- and Ti6 Al4V-coated substrates showed a clear decrease over time. In the LDPE Si\({}_{3}\)N\({}_{4}\)-coated substrates, the opposite trend was observed, but the increase was less significant compared with that of uncoated LDPE. This is probably due to the presence of Si\({}_{3}\)N\({}_{4}\) which, when homogeneously distributed, hindered biofilm formation. The slight increase of OD at 48 h could be associated with adopted laser treatment because, although particles were fixed without physically modifying the bulk substrates of Ti6 Al4V and Y-TZP, the surface was melted by the laser. In LDPE, this resulted in Si\({}_{3}\)N\({}_{4}\) particle incorporation. This provided a non-homogeneously Si\({}_{3}\)N\({}_{4}\) covered surface, but areas with LDPE were still present as observed in Figure 2.  
More bone tissue was formed on substrates with higher fractions of surface silicon phase. In silicon-rich samples, cells produced bone tissue, but at a lower rate than that of other materials.  
Substrate roughness also contributed actively to bone formation. The ZTA and Ti6 Al4V samples achieved higher surface roughness after laser cladding when compared to other substrates. This influenced the biological response.
# Estimation of Stability Parameters for Wide Body Aircraft Using Computational Techniques
## 2 US DATCOM
### Geometric Parameters  
Geometric parameters required for analysis (estimation of aerodynamic coefficients) were obtained from the established literature and reliable online sources. Some of those parameters are given in Table 2.  
The scaled-down model drawings of the Boeing 747-200 airplane with dimensional parameters and horizontal/vertical clearances obtained are shown in Figure 4. Based upon geometric parameters and horizontal/vertical clearances, source code was generated in DATCOM by applying VLM theory.
## 3 Results
### Daylight Autonomy (DA)
#### 3.2.5 Interior Reflectance (Bright/Dark Coatings)  
Bright coatings consistently result in higher \(DA\) values compared to those for dark coatings. For instance, the \(DA_{500}\) values for bright interiors typically range from 80% to 87% for WWRs between 25% and 45%, maintaining consistency throughout the room's depth. In contrast, dark finishes lead to greater variability and lower values, particularly in north orientations and smaller WWRs. In scenarios with a 15% WWR and dark coatings, \(DA\)values often fall below 50%, especially for north orientations with upper window positions, highlighting the significant impact of interior reflectance on daylight performance.  
Overall, the \(DA_{300}\) values exceed 50% in most configurations, except for specific cases with a 15% WWR, dark coatings, and north orientations. \(DA_{500}\) values frequently fall below this threshold for dark coatings with a north orientation and upper windows, as well as for south orientations in upper positions with a 15% WWR. Additionally, some regions in rooms with centered windows, with a 15% WWR and dark coatings facing north, as well as those with bright coatings facing south, also demonstrate \(DA\) values below 50%.
## 3 Results and Discussion
### Color Parameters, Browning and Whitening Index
Figure 5: Electrical conductivity (_EC_) evaluation of fresh and dried apple chips during storage under ambient conditions (S1—apple chips ‘Starkrimson’, oven; S2—apple chips ‘Starkrimson’, dehydrator; G1—apple chips ‘Golden Delicious’, oven; G2—apple chips ‘Golden Delicious’, dehydrator; F1—apple chips ‘Florina’, oven; F2  
The color difference between fresh and dry samples is greater when the browning index values are higher [17]. Temperature and the duration of exposure influence the evolution of color parameters [67]. [PERSON] et al. [23] stated that the change in color during drying is associated with the development of browning reactions, attributed to the activity of the enzyme polyphenol oxidase. Generally, the browning increases with temperature [58] (observed for S1, S2, F1 and F2 samples); thus the decrease in the browning index was unexpected for samples G1 and G2. The browning index (_BI_) at 0 days of storage was between 44.05 \(\pm\) 0.84 (F1) and 58.42 \(\pm\) 1.54 (F2), while the whitening index (_WI_) varied between 51.76 \(\pm\) 2.28 and 63.44 \(\pm\) 0.36. _WI_ values of S1 and S2 and F1 and F2 are statistically significantly different. _WI_ values differ depending on the drying method for S and F cultivars, while for cultivar G, _WI_ values are not so influenced by the drying method. During storage, _BI_ values for S1 increased and then decreased and for S2 decreased and then increased, while for _WI_ values, the reverse was observed. In the case of G1 and G2 samples, _BI_ values fluctuated and finally increased on the 21 st day; for F1 and F2 samples, the _BI_ values also increased on the last day of the observations compared to the values on the first day. The differences between the color parameter values are statistically significantly different. It was observed that _p_-values are less than 0.05, the null hypothesis is rejected, and not all group means are equal [52]. In addition, according to results obtained by applying [PERSON]'s method, the means that do not share a letter are significantly different (Table 2). The model fits our data, since R-sq explains 90.74% of the variation in the response in the case of the _BI_ values of dried samples on the first day of drying, 89.48% on the 7 th day of storage, 99.52% on the 14 th day of storage and 96.75% on the 21 st day of storage. R-sq explains 96.84% of the variation in the response in the case of _WI_ values of dried samples on the first day of drying, 97.25% on the 7 th day of storage, 98.83% on the 14 th day of storage and 99.03% on the 21 st day of storage.