esa-sceva/satcom-chunk-collection huggingface dataset:
# Cephalometrics in Obstructive Sleep Apnea Patients with Mixed Dentition
## 3 Results
### Polysomnographic Characteristics  
After an initial quality check of the data, the analysis showed that the common and routinely used polysomnographic measures covered a large data set that could be stored and thus allow us to proceed with the subsequent unbiased analysis. The mean AHI and ODI values were 4.6/h and 3.9/h (_p_ < 0.001), respectively.  
In the sample, 46.2% had mild OSA, 25% had moderate OSA, and 10.3% had severe OSA. The supine AHI was 5.67/h \(\pm\) 8.8, while the non-supine AHI was 3.695/h \(\pm\) 5.77; the central apnea index was 0.287/h \(\pm\) 0.3071 (_p_ < 0.001). The index of obstructive sleep apnea was 1.4/h \(\pm\) 4.45 (_p_ = 0.043). The saturation averaged at 96.12% \(\pm\) 1.5 (_p_ < 0.05). Two children (5.13%) had average saturation below 92%.  
When analyzing the questionnaire parameters, a significant positive correlation was revealed between the weight of the examined patients and the BMI (r = 0.844 (_p_ < 0.01)). The average weight was 36.03 kg \(\pm\) 13.2. The average height was 1.31 m \(\pm\) 0.15. According to the survey, 53.8% were mouth breathers; the remaining 46.2% had a mixed type of breathing. The pulse averaged at 78.8 bpm 9.58 (_p_ < 0.001).  
Among patients with OSA, 66.7% had in-hospital full polysomnographic studies. The data obtained revealed the following parameters: the average sleep time of patients was 443.65 \(\pm\) 59.98 min and the sleep efficiency averaged 90.87% \(\pm\) 8.8% (_p_ < 0.001).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Parameters**} & **Mean \(\pm\) Standard** & \multicolumn{2}{c}{**One-Sample _t_-Test**} \\ \cline{3-4}  & **Deviation** & **_t_** & **Significance (2-Sided)** \\ \hline PNEb & 48.6 \(\pm\) 5.9 & 51.280 & 0.000 \\ \hline EbTt & 55.9 \(\pm\) 6.4 & 54.853 & 0.000 \\ \hline MPH & 11.4 \(\pm\) 4.3 & 16.753 & 0.000 \\ \hline NSH & 89.1 \(\pm\) 5.8 & 95.515 & 0.000 \\ \hline AHC3 & 29.7 \(\pm\) 3.6 & 50.769 & 0.000 \\ \hline C2 SpC4 SpSN & 98.9 \(\pm\) 8.9 & 69.044 & 0.000 \\ \hline OPTSN & 96.3 \(\pm\) 9.7 & 62.254 & 0.000 \\ \hline Ad & 11.6 \(\pm\) 2.6 & 27.437 & 0.000 \\ \hline ad1 & 12.2 \(\pm\) 3.9 & 19.437 & 0.000 \\ \hline ad2 & 13.8 \(\pm\) 3.7 & 23.371 & 0.000 \\ \hline PAS1 & 12.2 \(\pm\) 3.7 & 20.570 & 0.000 \\ \hline PAS2 & 6.8 \(\pm\) 2.6 & 16.173 & 0.000 \\ \hline PAS3 & 4.1 \(\pm\) 2.01 & 12.871 & 0.000 \\ \hline PASmin & 3.5 \(\pm\) 1.03 & 21.538 & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cont.  
Of the polysomnographically examined children, 30.8% had sleep bruxism. REM sleep averaged at 19.23% \(\pm\) 6.3, NREM1 was 4.01% \(\pm\) 4.64, NREM2 sleep phase was 35.5% \(\pm\) 11.1, and NREM3 was 41.25% \(\pm\) 12.27 (Table 3).
# A Brief Roadmap into Uncertain Knowledge Representation via Probabilistic Description Logics
## 3 Representing Uncertain Knowledge
### Other Approaches  
As previously mentioned, [PERSON]'s classification does not cover the whole spectrum of uncertainty which can arise in practical applications. Indeed, one of the best known and most commonly used cases for uncertainty is not covered by this classification. In many situations with unknown relationships between properties, we gather partial information about the world through different statistical models. It is important not to confuse statistical models with statistical probabilities--the latter is the unfortunate name given to the case considered earlier in this section. One of the most common statistical models is the use of _sampling_ to approximate the incidence of a property. In a nutshell, one takes a small part of the population (a sample) and queries for the property of interest. Under some reasonable assumptions (about the quality and covering of the sampling method) the proportion of the sample that satisfies the property is approximately the proportion of the full population with the same property. The quality of the approximation improves as the sample size grows, but obtaining a larger sample may be expensive and in many cases (such as in the medical domain) even impossible.  
Importantly, as part of the population has not been observed, the actual incidence of the property studied is itself uncertain: it can still move in any direction, although with decreasing probability as it moves farther away from the computed estimate. Dealing with the uncertainty of these approximations alongside logical properties is not an easy task. Some approaches have tried to handle it through uncertain [38; 39] or imprecise probabilities [40; 41]. The issue is that these approaches (despite their names) still require precise knowledge of the probabilistic bounds, in opposition to the knowledge provided by the statistical analysis, which can provide different bounds with different degrees of certainty, even propagate the uncertainty through different reasoning steps. A preliminary approach trying to handle this information formally was presented in [42], where so-called confidence intervals appear as first-class citizens to be manipulated. However, as it is clear from that preliminary study, there remain many open gaps before these ideas can be developed into a fully fledged uncertain knowledge representation language.  
A final approach that is worth mentioning is based on the principle of _maximum entropy_. In this approach, the probabilities of the axioms define a unique probability distribution which is considered the least informative, thus preserving the idea of open world assumption but simplifying the reasoning process once this so-called maximum entropy distribution has been computed. For details on how this principle is applied in description logics, see [43; 44].
## 4 Motion Planning Application and Analysis  
This section constructs motion planning as a closed-loop NMPC problem based on several constraints. Motion planning in emergency conditions is an important problem, and some authors have proposed several effective methods to realize collision avoidance [16]. In this section, we describe the motion planner designed based on SDM to validate the improvement of control performances by considering the dynamical coupling through several comparative HIL tests.  
Figure 6: Comparative results. (**a**) Longitudinal speed. (**b**) Lateral speed. (**c**) Yaw rate. (**d**) Slip angle of front tire. (**e**) Lateral coupling force in longitudinal direction.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 7 Multi-User Multiple-Input Multiple-Output (MU-MIMO)
### Related Works
#### 7.2.2 Channel Estimation  
Estimating the wireless channel is especially important in MIMO. An access point should minimize interference level among contending stations.  
[PERSON] [164] mitigated the carrier frequency offset (CFO) in an uplink MU MIMO that causes inter-carrier interference and rotation of the constellation of the received symbols. He first modeled the received signal for the OFDM uplink MU MIMO channel. Then, he derived a minimum mean squared error for MU MIMO receivers to estimate the channel. The results revealed that the frequency domain CFO estimation reimburses the relative CFO effect in the uplink MU MIMO channels.  
The same author [165] highlighted that the phase noise hardware impairment does not degrade performance even when the received powers from different MIMO transmitters differ. The in-phase and quadrature (IQ) imbalances have a much stronger impact. Due to non-ideal hardware, modulators and demodulators may cause errors, possibly increasing the Packet Error Rate (PER). In particular, the carrier frequency offset may have a non-negligible impact for uplink MIMO [166]. Thus, the channel needs to be finely estimated to mitigate both the amplitude and the phase imbalance.  
In MIMO, beamforming has a strong impact on the PER; the transmitter must select which signal to transmit for each of its antennas and for each subcarrier. [PERSON] et al. [167] proposed adaptive feedback from the receiver to adjust the beamforming matrix. [PERSON] et al. [168] proposed a new channel sounding scheme, i.e., a technique to measure the properties of the radio channel, particularly for the multipath effect, to improve the throughput in downlink MU MIMO and reduce the overhead. Stations feed back the compressed beamforming frames concurrently using uplink multiplexing. The access point reduces the latency to collect the CSI and thus decreases the bandwidth consumption.  
The authors of [169] proposed a deep learning model for a joint downlink MU MIMO and MU OFDMA to improve throughput and reduce the channel-sounding overhead. A deep neural network leverages uplink Channel State Informations (CSIs) to train for the downlink transmission. They also considered resource allocation as a mixed-integer nonlinear programming optimization problem. Then, they employed a deep neural network to this problem for near-optimal resource allocation.  
[PERSON] et al. [170] introduced a newPHY design for an asynchronous uplink MU MIMO. The access point receives concurrent signals asynchronously from multiple stations. While it is decoding a signal, it considers the other incoming signals as interference. It then applies a spatial filter to recover this signal. This mechanism works properly for up to four users.
## Appendix B Pseudocodes  
### Pseudocode for Training the Neural Network  
In Algorithm 1, we present the detailed pseudocode for training a neural network for selected offer or outcome predictions. For both options, the steps are the same, with a minor difference in preparing the output classes. The selected offer prediction requires converting the y-values to one-hot encoded vectors, as the original class options are three, four, and five chasers.  
```
Input: Dataset \(D\), model \(M\), learning rate \(\alpha\), number of epochs \(N\) Output: Trained model \(M^{*}\)
1 Drop the redundant columns from \(D\), depending on the scenario;
2 Define \(y\), depending on the scenario (selected number of chasers or outcome);
3 Convert \(y\) to categorical in the case of the selected number of chasers prediction;
4 Split \(D\) into \(D_{train}\) and \(D_{test}\) in a 70:30 ratio;
5 Split the \(D_{test}\) into \(D_{test}\) and \(D_{validation}\) in a 50:50 ratio;
6 Optional: Oversample the \(D_{train}\) using SMOTE;
7 Initialize model \(M\) parameters \(\theta\) randomly;
8 forepoch = 1 to Ndo
9 for each batch \((x,y)\) in \(D_{train}\)do
10 Obtain model \(M\) prediction \(y_{pred}\gets M(x;\theta)\);
11 Compute the training loss, \(L(y_{pred},y)\);
12 Update model \(M\) parameters \(\theta\) using \(L\) and \(\alpha\);
13 end for
14 Compute the validation loss, \(L(y_{validation},y)\);
15 Save the model if \(L(y_{validation},y)\) improves;
16 end for
17 Load the saved model \(M^{*}\);
18 Evaluate \(M^{*}\) on \(D_{test}\);
19 return\(M^{*}\)
```  
**Algorithm 1**Training a neural network for the selected offer or output prediction.
# A Survey of Blockchain Applications for Management in Agriculture and Livestock Internet of Things
## 5 Future Research Directions
### Improving the Scalability and Performance of Blockchain  
Enhancing blockchain's capability to efficiently handle voluminous agricultural IoT data hinges on implementing a multi-layered architectural design. This design stratifies the system into distinct segments: smart agriculture, smart contracts, data storage, and user access. Such segmentation facilitates more efficient operation and scalability of the overall system. Integrating IoT with blockchain technology promotes superior data collection and management, strengthening both data security and transparency.  
The use of smart contracts to automate data processing workflows significantly reduces the reliance on manual intervention, thereby enhancing the system's operational efficiency and responsiveness. Moreover, adopting strategic approaches to data handling, such as data compression and selective storage, alleviates the load on the blockchain. These optimizations enable the system to manage large datasets effectively, tailoring blockchainfunctionality to better address the unique demands of agricultural IoT while maintaining robust scalability.  
While blockchain technology presents promising solutions for enhancing transparency and traceability in agricultural and livestock supply chains, its practical implementation faces several challenges. Scalability remains a key concern, as current blockchain architectures often struggle with the high transaction volumes required in large-scale supply chain operations. Additionally, the energy-intensive nature of consensus mechanisms, particularly proof-of-work, raises significant environmental and economic issues.  
Effective implementation also demands extensive collaboration among stakeholders, including farmers, distributors, and regulators. Achieving consensus and fostering trust among these diverse groups can be particularly challenging in fragmented industries like agriculture. Addressing these complexities requires innovative solutions, such as transitioning to energy-efficient consensus mechanisms and developing incentive structures to promote stakeholder engagement.  
By tackling these trade-offs, blockchain technology can better meet the demands of real-world applications, providing a more balanced perspective on its limitations and potential areas for improvement. This approach underscores the practicality of blockchain adoption while identifying avenues for further refinement and development.
# Sentinel-1 and -2 Based near Real Time Inland Excess Water Mapping for Optimized Water Management
## 2 Materials and Methods
### Data
#### 2.2.2 Validation Data  
In the validation process, independent remote sensing data, acquired by satellite and aerial sensors, were used. In 2016, reference data of inland excess water patches was generated from a SPOT-7 image. In 2018, an orthophoto was used to create the reference data set.  
The acquisition date of the pan-sharpened SPOT-7 image was 14 March 2016. The spatial resolution of the orthorectified data was 1.5 m due to pan sharpening of the original 6 m multispectral bands. The image covered an area of 400 km\({}^{2}\) in the northwest part of the study area (Figure 3).  
Aerial photographs were taken on 28 March 2018 with a 60 MP RGB camera deployed on a Cessna 172 airplane. The generated orthophoto mosaic had 10 cm spatial resolution and covered an area of approximately 20 km\({}^{2}\) in the northwest of the study area.
## 3 Results and Discussion
### Experimental Study with Simulated and Direct Solar Irradiance on IWWTP Samples
Figure 10: Effect of distance (d) on reaction time (t) for the \textgreater{}99% photocatalytic degradation of Acetamiprid using P3 and P4 (sample 5 of wastewater, pH = 10). The results obtained under Murcia's real sunlight, in the best-case scenario, with a degradation time of 585 min, correspond to an equivalent distance of 10.5 cm under simulated sunlight. Moreover, the solar irradiation experimental values are consistent with the historical average values of Murcia's solar irradiation, with an annual average of 5 kWh/m\({}^{2}\)/day [73]. During months with higher solar irradiation and extended daylight hours, such as from June to October, the daily sunlight duration is sufficient to achieve pesticide removal using the direct irradiation method, making the approach particularly effective during the summer. On the other hand, it is important to note that while these distances are equivalent in terms of the kinetic values obtained in laboratory experiments under artificial sunlight and real sunlight, no direct equivalence can be established. It should be considered that the irradiation is different in each scenario due to the irradiated area on the test tube (illuminating only one side under artificial sunlight compared to all sides under direct sunlight). Additionally, for the 125 W mercury vapor lamp, only a small fraction of its power is emitted as usable light within the absorption range of RB. Furthermore, in both cases, the FeCl\({}_{3}\) filter is applied, blocking wavelengths below 450 nm, which further reduces the effective power, albeit with different components in each scenario. Considering that in previous studies [31; 59], these polymers were already optimized in terms of the ratio of RB incorporation into the polymer matrix and the amount introduced per reactor volume, the further improvement of the kinetic parameters under real sunlight conditions is not possible. This limitation stems from the inability to increase the amount of anchored RB photosensitizer to extend the kinetics. As a conclusion for real-world applications and as a continuation of previous studies, it can be determined that the removal of Acetamiprid from wastewater in industrial WWTPs is feasible, particularly during the summer and in months when the effective daylight hours with solar radiation intensity exceed Acetamiprid's degradation time, which occurs for approximately 7-8 months per year. Given that this method works, albeit with the limitation of the amount of photosensitizer per polymer weight, future work should focus on innovating the polymer matrix to achieve faster and more efficient kinetics under simulated and real sunlight conditions, while also ensuring compatibility with the pH range of wastewater plants. Additionally, further studies will explore the degradation of other pesticides compounds such as EPs.
## 5 Additively Manufactured Flexible Sensors (3D Printing)
Additive manufacturing (AM) has gained the attention of the flexible sensor industry, owing to its main advantages of flexible design, repeatability, wide range of printable materials (as we have seen in this report), ability to print multi-material items, reduced cost and customised micro/nano structures. AM has been used to fabricate various tactile sensors that can detect stimuli such as shear, bending, torsion, vibration frequencies reaching 400 Hz and pressures in the range of 5 Pa-100 Kpa [85]. In one report, a flexible strain sensor was fabricated by embedding silver nanowires between two PDMS layers [58]. The sensor showed great piezoresistivity with variable gauge factor and high stretchability reaching 70%. Piezoelectric effect involves the conversion of mechanical force to electrical signals and has been widely used in the fabrication of flexible sensors. Traditionally, ceramic-based piezoelectric materials have been used for sensing technologies, however these are limited in flexibility and cannot be used were flexibility and bendability are required, such as wearable and implantable medical devices. Hence, polymer-based piezoelectric materials such as poly(vinylidene fluoride) and copolymerized poly(vinylidene fluoride-trifluoroethylene) have gained attention in the fabrication of piezoelectric sensors [86]. A piezoresistive tactile sensor was fabricated via a Polylet-based 3D printing machine (Connex 500, Stratasys Objet Co., Ltd., Rehovot, Isreal) [87]. A commercially available flexible material called TangoPlus was used in the fabrication process. The sensor consists of two layers of the TangoPlus sandwiching MWCNTs piezoresistive lines. Another research team used a photopolymerisation-based 3D printer to print a flexible material with micro-channels (diameter of 500 microns) into which conductive nano silver was placed [88]. Initially the channels were filled with wax, which was then melted away to place the conductive silver nanomaterials, which acted as a piezoresistive sensing part. Laser beam techniques, such as SLM, have also been used in fabricating flexible sensors as in the case in one report whereby metal powder was used as the 3D printing material to fabricate helical-shaped electrochemical electrodes of various sizes [89]. These sensors exhibit pH sensing, oxygen catalytic properties and good capacitive properties. Three-dimensional printed sensors and their characteristics are summarised in Table 2. Some general advantages and disadvantages of commonly used 3D printing methods in flexible sensor fabrication are shown in Table 3.
## 4 Conclusions  
Much evidence has revealed that collaborative intelligence has potential application in forecasting. On the other hand, agent based collaboration has become an important field of research, and new applications of agent collaboration are expected to appear. In order to effectively predict the price of a DRAM product, an agent-based fuzzy collaborative intelligence approach is proposed in this study. In the agent-based fuzzy collaborative intelligence approach, each agent predicts the DRAM price using the FBPN approach based on its view. The agent then communicates its view and forecasting results to other agents with the aid of an automatic collaboration mechanism. After receiving that information, the agent modifies its setting and increases the overall accuracy. The forecasts by different agents are aggregated using an RBF.  
After applying the agent-based fuzzy collaborative intelligence approach to a real case, the following experimental results were obtained:  
Figure 3: The accuracy of the agent-based fuzzy collaborative intelligence approach.  
Figure 2: The required collaboration time.  
1. The aggregate results were considerably improved through the agents' collaboration. Especially, the forecasting accuracy, measured in terms of MAE, MAPE, and RMSE, of the agent-based fuzzy collaborative intelligence approach was much better than those of some existing methods.
2. It is therefore possible to predict the DRAM price level very precisely and accurately using a group of agents governed by an automatic collaboration mechanism.  
More sophisticated collaboration mechanisms can be developed in similar ways in future studies. In addition, some advanced artificial neural networks have been proposed to improve the accuracy of time series prediction. These methods can be fuzzified to replace the FBPN used in this study. The proposed methodology can also be applied to predict other types of time series, such as stock price, production costs, product yield, _etc._ However, the computation becomes very complicated if many agents are involved. For this reason, future studies may restrict the size of the agent coalition.
# Surface Functionalization of Polyethylene by Silicon Nitride Laser Cladding
## 2 Materials and Methods  
### Samples Production  
Low-density polyethylene pellets (Sigma-Aldrich, Darmstadt, Germany) were used to prepare substrates by melting at 125 \({}^{\circ}\)C under a low vacuum (10 Pa), in order to remove residual bubbles. A rectangular silicone rubber molding (80 \(\times\) 55 mm) was used to produce homogeneous samples that were then cut into smaller pieces (10 \(\times\) 10 mm) for various experimental purposes.  
SINTX Technologies Corporation, Salt Lake City, USA provided both the Si\({}_{3}\)N\({}_{4}\) bulk samples and the Si\({}_{3}\)N\({}_{4}\) powder used in this investigation. The bulk material, which was used as a positive control in biological testing, was prepared according to conventional ceramic fabrication techniques [19].  
A Vision LWI VERGO-Workstation Nd:YAG laser with a wavelength of 1064 nm (max pulse energy: 70 joule, peak power 17 kW, voltage range 160-500 V, pulse time 1-20 ms, spot size 250-2000 \(\upmu\)m) was used to clad the polyethylene with a silicon nitride powder coating.  
The parameters used for laser coating were: voltage 225 V, pulse time 4 ms, and a spot size of 2000 \(\upmu\)m. The laser coating apparatus was operated under a constant flux of nitrogen gas in order to prevent PE oxidation. The Si\({}_{3}\)N\({}_{4}\) powder was spread on the PE surface and then treated with the laser beam to melt the material surface.  
Silicon nitride powder (diameter between 5 \(\upmu\)m and 25 \(\upmu\)m) was deposited on the polyethylene surface and heated using the laser beam for it to be embedded into the polymeric matrix.