esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Investigated Classification Techniques  
Six well-known classification techniques were considered, including \(k\)NN, C4.5, SVM, RF, ANN and LGR. Table 2 lists the primary applications of these techniques in previous studies in medical domain [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]. \(k\)-nearest neighbors (\(k\)NN) is a well-known case learning method that classifies new cases based on distance functions. For a test case, \(k\)NN first finds its top \(k\)-nearest neighbors in the vector space; the class label of the test case is assigned to the majority class among its neighbors [32]. The Euclidean distance and the Hamming distance are usually used for numeric and categorical variables, respectively. Decision tree (C4.5) is a commonly used classification technique. The growth of the decision tree is a recursive process. First, an attribute with the best gain ratio is selected as the split node of the tree, and the corresponding branches are generated according to its attribute values. Then, the dataset can be partitioned into subsets, and each subsets can be used for next round of tree partition. The decision tree grows until the termination condition is met. The rules generated by the decision tree are easy to understand and can be applied in practice [33]. Because gain ratio is proposed to measure the improvement of data purity in C4.5, it is also used to measure the importance of IVs [34]. Support vector machine (SVM) is a supervised learning method that maps IVs and a DV to a high-dimensional vector space by using structural risk minimization. Then, it attempts to find a hyperplane to separate the data into two categories. Therefore, a new test case can be mapped into one subspace based on the determined hyperplane [35]. Random forest (RF) is a tree-based ensemble learning algorithm, which adopts bagging and random feature selection techniques [36]. Each decision tree is constructed from bootstrap samples of training data, and the final predicted result is determined by a majority vote. When building multiple trees, a random sample with replacement and the random selection of feature of the training set are used, which reduce the possibility of overfitting in tree growing process. Artificial neural network (ANN) consists of an input- and an output- layer with a number of hidden layers. Artificial neurons between two adjacent layers are connected; the neurons receive the inputs from the outputs of the front adjacent neurons and convert them into an output value through the transfer functions. The key component in the learning procedure is the connection weights between two neurons. In training phase, it iteratively tunes the connection weights between neurons for minimizing the difference between the estimated output value and the actual output. The learning process repeatedly executes the feedforward and backward phases until any stop criteria is reached [37].
# A Roadmap for Gigabit to Terabit Optical Wireless Communications Receivers
###### Abstract  
Silicon photomultipliers' relatively large areas and ability to detect single photons make them attractive as receivers for optical wireless communications. In this paper, the relative importance of the non-linearity and width of SiPMs' fast output in their performance in receivers is investigated using Monte Carlo simulations. Using these results, the performances of receivers containing different SiPMs are estimated. This is followed by a discussion of the potential performances of arrays of existing SiPMs. Finally, the possible dramatic improvements in performance that could be achieved by using two stacked integrated circuits are highlighted.  
visible light communications; optical wireless communications; SiPM; Monte Carlo simulation 2023
## 2 Materials and Methods
### Data Sources, Search Strategy, and Article Selection  
Single investigator performed an independent electronic search of the English literature utilizing Google Scholar, Elsevier's Scopus, Clarivate Analytics' Web of Science, MEDLINE (OVID), EMBASE, and PubMed (National Library of Medicine). The search period ranged from inception up to April 2021. The exact search words were \"mini dental implant\" OR \"mini implant\" OR \"mini dental implant\" AND \"overdenture\" OR \"mini dental implant\" AND \"prosthodonotic\" OR \"mini implant\" AND \"overdenture\" OR \"mini implant\" AND \"prosthodonotic\"\".  
Manual searching for the studies was conducted by the same investigator until April 2012 in the following journals: Journal of Prosthetic Dentistry, International Journal of Prosthodontics, Journal of Oral Rehabilitation, Journal of Dentistry, Journal of Dental Research, International Journal of Prosthodontics, International Journal of Oral and Maxillofacial Implants, Clinical Oral Implants Research, and Clinical Implant Dentistry and Related Research.  
The screening of the abstracts and titles of the articles identified using the abovementioned strategy was conducted by single investigator. A full-text reading of the relevant articles was performed. Manual searching of the bibliography of the pertinent literature reviews and research articles was also carried out for identifying articles that may have been missed in the previous step. Discrepancies were solved via discussion.
## 4 Results and Discussion
### Crack Signal Initiation Mechanism
In a single cycle of signals in Figure 14b,c, the manifestation of crack signals occurs primarily in the form of impact signals corresponding to the rod load commutation points R1 and R2, along with a series of consecutive, relatively low-amplitude impact signals, denoted as T. Conversely, in the absence of a crack signal, as shown in Figure 14a, the AE amplitude remains at only less than 0.15, which is more than ten times smaller than that observed in the presence of a crack signal. Consequently, the sub-band signals serve as effective representations of the crack propagation components. Following the aforementioned analysis, a value of 0.15 is employed as the threshold for the AE counting in the subsequent computation of the eigenvalues. Figure 13: The load reversal induced the commutation of the contact between the pin and bearing. Schematic diagram of the pin and bearing contact position reversal for lubrication (**a**); variation in the contact position with the rod load curve (**b**).
# Self-Organized Proactive Routing Protocol for Non-Uniformly Deployed Underwater Networks
###### Abstract  
Electromagnetic (EM) waves cannot propagate more than few meters in sea water due to the high absorption rate. Acoustic waves are more suitable for underwater communication, but they travel very slowly compared to EM waves. The typical speed of acoustic waves in water is 1500 m/s, whereas speed of EM waves in air is approximately \(3\times 10^{8}\) m/s. Therefore, the terrestrial wireless sensor network (WSN) protocols assume that the propagation delay is negligible. Hence, reactive protocols are deemed acceptable for WSNs. Other important issues related to underwater wireless sensor networks (UWSNs) are determining the position of the underwater nodes and keeping a time synchronization among the nodes. Underwater nodes can neither determine their position nor synchronize using Global Navigation Satellite Systems (GNSS) because of the short penetration of EM waves in sea water. The limited mobility of UWSN nodes and variation in the propagation speed of acoustic waves make time synchronization a challenging task for underwater acoustic networks (UASNs). For all these reasons, WSN protocols cannot be readily used in UASNs. In this work, a protocol named SPRINT is designed to achieve high data throughput and low energy operation in the nodes. There is a tradeoff between the throughput and the energy consumption in the wireless networks. Longer links mean higher energy consumption. On the other hand, the number of relay nodes or hops between the source node and the final destination node is a key factor which affects the throughput. Each hop increases the delay in the packet forwarding and, as a result, decreases the throughput. Hence, energy consumption requires the nearest nodes to be chosen as forwarding nodes, whereas the throughput requires the farthest node to be selected to minimize the number of hops. SPRINT is a cross-layer, self-organized, proactive protocol which does not require positioning equipment to determine the location of the node. The routing path from the node to the gateway is formed based on the distance. The data sending node prefers to choose the neighbor node which is closest to it. The distance is measured by the signal strength between the two nodes.  
underwater; UWSN; location free; routing protocol; self-organized; self-configud; wireless networks; proactive +
Footnote †: journal: Sensors
## 5 Conclusions  
In this paper, enhancements are made to the communication aspect of the integrated waveform design framework. To reduce out-of-band radiation between different subcarriers, this paper chooses to add a VC and a CP to the waveform instead of adding ZP before symbols. Moreover, the use of STO estimation and CFO estimation at the receiver ensures temporal and spectral synchronization of the received signal. Consequently, it effectively improves the communication BER of the integrated signal. For radar detection, this paper uses PSLR and ISLR in the frequency domain to characterize their radar detection performance. Chirp signals sampled in the frequency domain are emitted as wide pulses to ensure an ample range of action. A corresponding pulse compression algorithm is used at the time of reception to obtain a narrow pulse to ensure its resolution. MATLAB 2018a is used as the software platform for simulation experiments to verify that the integrated system proposed in this paper can accurately transmit and receive communication data, reach a BER of \(7\times 10^{-5}\) and an EVM of 0.2087 at an SNR of 10 dB, and perform speed and distance measurements on radar targets with a PSLR of \(-13.81\) dB.  
Data curation, C.Z.; funding acquisition, P.H. and S.W.; investigation, G.W.; methodology, C.Z., P.H. and G.W.; project administration, P.H. and S.W.; resources, P.H.; software, C.Z.; validation, C.Z. and G.W.; writing--original draft, C.Z.; writing--review and editing, P.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Yantai City 2021 School-Land Integration Development Project Fund No. 1521001-WL21 JY01, Chinese National Nature Science Foundation No. 62201491, Natural Science Foundation of Shandong Province No. ZR2021 QF097, and Science and Technology-based Small and Medium-sized Enterprise Innovation Capacity Enhancement Project of Shandong Province No. 2023 TSGC0823 under Grant.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 3 Results
### Validation of CAD-Based Patient-Specific Nuss Bar Design Technology
Furthermore, the improved HI values were near the target HI value of a normal chest wall (HI < 2.5). The data analysis results revealed that the minimum AP distance and HI improved the most at point B that had the largest HI value before operation. Therefore, the optimal insertion point was confirmed to be B which had a corrected HI value lower than 2.0 and showed the greatest change. Using the Nuss bar design technology which is described in Section 2.3, we derived the Nuss bar design for asymmetric PE patients (Figure 12). Patients Nos. 2 and3 represent the eccentric and unbalanced PE patients, respectively. The insertion position and shape of the patient-specific Nuss bar are derived using corrected HI values. Additionally, the angle and height on the left and right side of chest have similar values after the Nuss procedure, which is for the cosmetic aspect. Table 6 shows the corrected HI values after Nuss bar insertion. After Nuss bar insertion, the HI values of the eccentric case decreased by 31.9% from 3.26 to 2.22 in the insertion position A. In the unbalanced case, the HI values after Nuss bar insertion decreased by \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type** & **A (mm):** & **A\({}^{\prime}\) (mm):** & **C (mm):** & **C (mm):** & **HI:** & **ICI:** \\  & **Pre-Op.** & **Post-Op.** & **Pre-Op.** & **Post-Op.** & **Pre-Op.** & **Post-Op.** \\ \hline \hline \multicolumn{1}{c}{Eccentric (Patient No. 2)} & 197.2 & 187.34 & 60.4 & 84.4 & 3.26 & 2.22 \\ Unbalanced (Patient No. 3) & 246.0 & 233.7 & 72.6 & 98.6 & 3.39 & 2.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Haller index before and after the Nuss procedure in Patients Nos. 2 and 3. Figure 12: CT images of asymmetric patients. (**a**) and (**b**) are CT images taken before the Nuss operation; (**a\({}^{\prime}\)**) and (**b\({}^{\prime}\)**) are images taken after the Nuss operation. Yellow lines in (**a\({}^{\prime}\)**) and (**b\({}^{\prime}\)**) show the patient-specific Nuss bar. 30% from 3.39 to 2.37 in the insertion position C. The corrected HI value of both patients (eccentric and unbalanced) are below 2.5 (normal range of HI).
# Comparing Winds near Tropical Oceanic Precipitation Systems with and without Lightning  
[PERSON]  
1 NASA Marshall Space Flight Center, Huntsville, AL 35812, USA; [EMAIL_ADDRESS]  
1222  
to expect that thunderstorms should feature stronger gust fronts compared to storms without lightning. In addition, as lightning flash rate increases, this tends to signal intensification of the convection, to the point that very high flash rate storms are likely to be associated with severe weather, such as damaging winds from intense gust fronts [14; 15]. However, due to the relative sparseness of global wind observations, it is difficult to demonstrate this hypothesis conclusively, let alone quantify the actual differences observed between gust fronts associated with thunderstorms and gust fronts associated with storms without lightning.  
Over oceans, scatterometers such as the Advanced Scatterometer (ASCAT) are capable of retrieving near-surface vector winds, but these measurements are more uncertain in precipitation [16; 17; 18]. The Cyclone Global Navigation Satellite System (CYGNSS) uses Global Positioning System (GPS) reflectometry to retrieve wind speed over the oceans [19]. Because it is L-band, CYGNSS is expected to be less impacted by precipitation, and its ability to retrieve accurate winds in rain has been demonstrated [20]. CYGNSS provides coverage of oceanic winds in the global tropics.  
Due to its ability to accurately retrieve winds even in the presence of heavy rain [20], which is common near tropical convection, CYGNSS was chosen as the basis for this study. By combining CYGNSS observations with global precipitation measurements, as well as global/regional lightning datasets, this study will quantify differences in winds near lightning-producing oceanic convection versus convection without lightning. In turn, this study will attempt to determine (in a categorical sense) the impact of relatively stronger updrafts and ice microphysical processes on wind speeds near precipitation systems, as thus by proxy infer potential impacts on cold pool and gust front strength.
# Adaptive Energy Management in 5G Network Slicing: Requirements, Architecture, and Strategies
## 3 Energy Management Proposal for 5G Network Slicing
### Energy Management Strategies for Adaptive Consumption
#### 3.2.5 Service Rejection  
If the energy supply is not sufficient to guarantee the execution of a service, either due to an energy shortage or because the energy has already been allocated to another service, and also the application of the strategies described above (i.e., _time shifting_ or _quality degradation_) does not allow its processing, the service in the admission stage is not processed and is categorized as _rejected_, as shown in Figure 1b for service \(S_{5}\). The criteria for service rejection may be established by the MNO and in agreement with the customer, and may depend on a number of factors such as those described in Section 3.2.2.  
In the example in Figure 1, each energy management strategy acts on a specific service. However, in a real implementation, the MNO with information of the services (required execution time, duration, and power demanded) and considering the use of the different management strategies described above, must be able to apply the best strategy for each service. This process is performed with the aim that the total or aggregate power of all services optimizes the use of the available supply. To this end, the MNO executes service scheduling algorithms such as the one described in Section 5.2, which allows achieving the energy efficiency required by 5G networks in a network slicing context.  
The adaptive behavior of the proposed energy management solution through the management strategies (i.e., the degradation in service quality and time shifting capability) enable to adapt the consumption to generation constantly. This feature allows dealing with the intermittent and dynamic behavior of renewable energy sources and leverage at the maximum their energy production capacity. In the event that green energy is not sufficient to meet all demand, the proposed management solution considers the possible rejection of services. Furthermore, although our approach prioritizes the use of renewable energy, the partial or total energy provisioning from non-renewable sources is an alternative, in which case the management strategies described above can also be employed.
## 3 Results
### Development of the Extraction Method  
#### 3.1.1 Preliminary Analyses of the Products Extracted  
To preliminarily investigate the effect of enzymatic treatment, extracts obtained by hydroalcoholic extraction alone and those obtained by enzymatic treatment followed by hydroalcoholic extraction were analyzed by HPLC. Chromatograms comparison showed that the extract obtained by enzymatic treatment appeared richer in specific phenolic compounds compared to the extract obtained from hydroalcoholic extraction alone (Figure S1).  
Such observation may be explained by the accelerated release of phenolic compounds thanks to the enzymatic action. Indeed, when the enzymatic treatment was applied, the yield of TPC reached its maximum value at 2 h after the initiation of the hydroalcoholic extraction (Figure 2A,B). Conversely, when the hydroalcoholic extraction alone was applied, the same yield was not achieved even after 24 h of extraction. Therefore, a 2 h hydroalcoholic extraction after enzymatic treatment was sufficient to maximize the extraction yield of the active compounds. A similar behavior was found for flavonoid extraction: the TFC yield was improved when the hydroalcoholic extraction was coupled with enzymatic treatment, for all the hydroalcoholic extraction durations tested.  
Figure 2: A preliminary analysis of the _V. nigrum_ extracts, in terms of TPC (**A**) and TPC (**B**), was assessed by colorimetric assays to compare the yield obtained with different extraction conditions. HA: hydroalcoholic  
extraction; EH 15% + HA: enzymatic treatment followed by hydroalcoholic extraction (enzyme concentration: 15% by weight of dry biomass); EH 30% + HA: enzymatic treatment followed by hydroalcoholic extraction (enzyme concentration: 30% by weight of dry biomass). Data were analyzed by 2-way ANOVA followed by Tukey multiple comparisons test (** \(p<0.01\); *** \(p<0.0001\)).  
Moreover, by assaying two different enzymatic concentrations (15 and 30 wt% relative to the weight of dry mass), no remarkable differences were observed, indicating that the lower concentration is sufficient for an effective enzymatic treatment.
## 4 Results and Discussion
### Effect of Inter-Electrode Separating  
The distance between electrodes is analyzed in this study by variations in current versus voltage curve for different gaps and emitter electrode radii. As can be seen in Figure 9, as we increase the separation between electrodes, the spatial distribution in the drift zone is smaller, reducing the corona  
Figure 8: **(A)** Heat-transfer coefficient for different electrode diameter as a function of device power and (B) operating voltage with an inter-electrode separating 2 mm.  
Figure 7: Wire radius consequence on the current–voltage curves for positive polarity.  
discharge current. For each increase of half millimeter gaps, it is necessary to increase approximately 200 V to 500 V to produce the dielectric breakdown of the air and generate the corona discharge. The behavior, as can be seen in Figure 10, is applicable for the different radius with the inter-electrode separating studied.  
Figure 9: Representation of leakage C–V curve for different electrode separation with radius of (**A**) 12.5 \(\mu\)m, (**B**) 25 \(\mu\)m and (**C**) 50 \(\mu\)m.  
Considering the electrostatic behavior of the results described above, the coefficient of convention is analyzed for these small separations between electrodes. A longer gap seems to be more efficient in power consumption, but it requires higher voltages (Figure 10A,B). This behavior can be explained by the fact that a longer gap will increase ion wind volume, so acceleration of particles will be stronger. This increasement in air speed is traduced in an enhanced heat transfer. In conclusion, a bigger gap seems to be more efficient in power consumption, but operating voltage is a critical parameter design in order to achieve reliability, and a more stable discharge.
## 1 Introduction
In summary, the current fatigue analysis studies of carbon-fiber structures in wind turbine blades typically only consider parameter uncertainty, without accounting for the uncertainty in the distribution parameters. The global sensitivity analysis of multi-input variable distribution parameters faces the problem of excessive computational demand, significantly reducing its computational and analytical efficiency. This makes it difficult to quickly identify the critical distribution parameters affecting the analysis's reliability and accurately predict the fatigue life of carbon-fiber structures in wind turbine blades. To overcome these challenges, this study introduces a novel sensitivity index inspired by variance-based methods. This index specifically evaluates the influence of the distribution parameters on the fatigue life of carbon-fiber structures, offering a more comprehensive understanding of their impact. Furthermore, we propose an efficient computational approach that leverages the Kriging model to directly establish the relationship between the distribution parameters and output statistics. This method incorporates a surrogate sampling probability density function, significantly improving the efficiency of sensitivity analysis under both subjective and objective uncertainty factors. The subsequent sections of this paper are organized as follows: Section 2 presents the theoretical foundations of this study, including the Sobol sensitivity analysis and the active learning Kriging model. Section 3 details the fatigue experiments conducted on carbon-fiber structures and the validation of the finite element simulation model. Section 4 introduces the construction and validation of the surrogate model using the ALK-SS approach. Section 5 applies the proposed method to perform a Sobol sensitivity analysis, highlighting the impact of distribution parameter uncertainty on fatigue performance. Section 6 summarizes the main findings and conclusions of this study. Fatigue Life Prediction and Reliability of Cracked Carbon-Fiber Structures under Uncertain Parameters