esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In summary, we have investigated a MQW-SESAM structure by femtosecond time-resolved spectroscopy at a wavelength of \(\sim\)1050 nm, and found a wide variety of coherent acoustic features. In particular, we observed two distinct frequency regimes: one with its main frequency components appearing around 365 GHz, and the other with frequencies ranging from \(\sim\)15 GHz to \(\sim\)100 GHz.  
The high frequency components can be attributed to the coherent acoustic phonons generated and detected in the In\({}_{0.27}\)Ga\({}_{0.73}\)As QWs of the sample, which also give rise to an acoustic frequency comb with a spacing of \(\sim\)33 GHz. The lower frequency components show the signature of SL eigenmodes and a comparison with the calculated acoustic dispersion of the DBR superlattice yields good agreement. This is surprising, because the DBR materials are transparent at the used pump wavelength, and thus, no acoustic response is expected. In addition, several unexpected modes are present in the acoustic spectrum, in particular, MBZ edge-modes which are usually not observable due to optical detection selection rules.  
We propose an explanation based on the excitation of CLAPs in the DBR by spatial electrostriction coefficient modulation and the spatially modulated pump electric field. A subsequent stimulated subharmonic decay of the MBZ-center modes is then able to excite the MBZ-edge modes including the 15 GHz mode. The dynamical folding of the zone-edge mode onto the zone center, together with Raman-Nath-type probe light scattering, can account for the appearance of the MBZ-edge modes in the measured acoustic spectra. The observation of simultaneous CLAPs in MQW region and the weak MBZ modes in the DBR is enabled by the high-detection-sensitivity in our system (\(\Delta R/R{\sim}10^{-7}\)).  
Our results not only provide a way to non-destructively characterize SESAM structures which are extensively employed in the realization of mode-locked lasers, but also provide new insights into the light-matter interaction in non-doped long-period superlattices formed by optically non-absorbing materials.  
Investigation, C.L.; Methodology, V.G.; Resources, E.D.; Software, C.L. and M.H.; Supervision, T.D.; Visualization, C.L.; Writing original draft, C.L. and V.G.; Writing review & editing, V.G., T.D. and M.H.  
**Funding:** This research is financially supported by the Center for Applied Photonics at the University of Konstanz (CAP-02). It is partially supported by the Deutsche Forschungsgemeinschaft (DFG) through SFB 767. It is also supported by Chinese Scholarship Council (CSC).  
**Acknowledgments:** [PERSON] is grateful to versatile experimental supports from [PERSON] and [PERSON], and helpful discussions with [PERSON] and [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels
## 1 Introduction
To the best of our knowledge, this is the first effort in predicting the path loss at frequency bands higher than 6 GHz for typical indoor corridor environments based on several ML methods. Path loss prediction models with the highest possible accuracy are vital for such environments since the trend is to rely on indoor channels for future wireless networks. Therefore, the primary motivation of this work is to evaluate the feasibility and prediction accuracy of various machine-learning-based models for predicting path loss in indoor corridor environments. Eight machine learning methods are presented and evaluated in this work. The methods include multiple linear regression (MLR), polynomial regression (PR), decision tree (DT), random forest (RF), support vector regression (SVR), K-nearest neighbor (KNN), artificial neural network (ANN), and long short-term memory (LSTM) artificial recurrent neural network (RNN), RNN-LSTM. The study is based on actual measurement data collected in a typical indoor corridor environment at three frequency bands from the SHF band, namely 14, 18, and 22 GHz. Our previously published work sufficiently describes the measurement campaigns and the channel sounder setup [10]. The rest of this paper is structured as follows. First, a review of the existing related works aimed at predicting the path loss based on ML algorithms is presented in Section 2. Then, Section 3 discusses the preparation of the raw data, hyperparameters tuning, justification of the models' stability and the evaluation metrics. Section 4 describes the methods adopted for this study. The main results of this research work are discussed in Section 5. Finally, Section 6 concludes the research findings of this paper.
## 3 Results and Discussion
### Hydraulic Losses with Different Downstream Structures  
In order to better understand the internal flow of the draft tube is divided into three parts along the flow direction: the front straight section (FSS), the middle elbow section (MES), and the rear straight section (RSS), as shown in Figure 7. After the water flows out of the runner, it first enters the FSS, then enters the MES, changes the flow direction in this section, and then flows into the RSS.  
Figure 5: Water turbine performance test bench.  
Figure 6: Comparison of numerical predictions of water turbine performance with test results.  
Figure 7: Section division of the draft tube.  
The head loss of the draft tube is shown in Figure 8. The head loss in MES accounts for most of the head loss of the entire draft tube, more than 40%, and the head losses in FSS and RSS are small and almost equal. However, due to the short length of FSS, it is considered that the hydraulic loss of the draft tube is mainly concentrated in FSS and MES. The head loss of the draft tube is highest for ELN, followed by NLN, and lowest for SLN in different downstream structures. For NLN, the head loss of the FSS accounts for 27% of the total head loss of the draft tube, MES accounts for 46%, and RSS accounts for 26%. For SLN, the head loss of FSS and RSS is significantly smaller than that of NLN, and the head loss of MES is slightly smaller than that of NLN; however, the proportion of head loss increases to 50%. The locking nut improves the flow pattern of the draft tube and reduces the hydraulic loss, especially in FSS and RSS. For ELN, FSS, and RSS, the head loss is significantly greater than that of NLN and SLN. The head loss of MES is similar to that of NLN and slightly greater than that of SLN; however, the proportion of head loss decreases to 41%. Extended locking nuts may cause deterioration in the flow pattern, especially in FSS and RSS.  
Further analysis of MES, which accounts for the majority of hydraulic losses in the draft tube, shows that the head loss in the first half is significantly greater than that in the second half, as shown in Figure 9. For NLN, the head loss in the first half of MES is less than twice that in the second half. For SLN, the head loss in the first and second half of MES is slightly smaller than that of NLN. The locking nut improves the MES flow state. For ELN, the head loss in the first half of MES is greater than that in NLN and SLN, but the head loss in the second half of MES is smaller than that in NLN and SLN, and the head loss in the first half of MES is twice that of the second half. The extended nut does not improve the flow state in the first half of MES but improves the flow state in the second half.
## 4 Discussion
### Limitations of the Review  
This review adhered to the PRISMA 2020 guidelines but was limited by the significant heterogeneity among the included studies and the lack of standardization in data collection. It is important to note that these limitations are not attributable to the researchers of this study but to the deficiencies in the existing scientific literature on the treatment of PIPJ stiffness. The inability to conduct a robust quantitative meta-analysis, including the use of the I\({}^{2}\) statistic, was due to the lack of detailed information, such as SD for changes in ROM, reflecting inconsistencies in the reporting methods across the studies analyzed. The inclusion of studies with diverse methodologies and varying sample sizes also limited the applicability of the findings to clinical practice, highlighting the need for stricter inclusion criteria and more methodological rigor in future research. The studies included in this review showed considerable heterogeneity in terms of the therapeutic approaches applied, the methodology used, and the characteristics of the patient populations. This variability, inherent in the existing studies, complicated the synthesis of results and reduced the ability to conduct robust statistical analyses. The lack of adequate control over co-interventions and the absence of complete quantitative data, such as baseline, intermediate, and final ROM values along with their standard deviations, reflected the methodological limitations in the literature, undermining the reliability of the findings and complicating the comparison between studies. Furthermore, inconsistencies in follow-up criteria and outcome measures, such as quality of life or patient satisfaction, made it difficult to comprehensively assess the treatment effects.
## 3 Data Collection and Methods
### Morphometric Parameters
#### 3.1.1 Catchment Geometry Indexes  
Geometries were calculated based on four parameters, including catchment area index (A), catchment length index (L), catchment width index (W), and catchment perimeter (P) [17; 19].  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Data** & **Sources** & **Date** & **Resolution** \\ \hline Shuttle Radar Topography & & \\ Mission (STRM) digital & [[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)) & 23 September 2014 & \\ elevation model data & & 00:00:00-05 & 30-m resolution \\ \hline Geological maps of Egypt & EGPC and CONOCO, \"Egyptian & \\  & General Petroleum Corporation and & 1987 & 1:500,000 scale \\  & CONOCO”, A geological map of Egypt & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Available data that were processed in the study area.  
Figure 3: Annual rainfall of the study zone from 2005 to 2020.  
Figure 4: Flowchart showing the processing steps of the morphometric indexes.  
Figure 5: Surface raster data analysis of the study catchments and sub-catchments. (**a**) Elevation; (**b**) contour map; (**c**) slope map; and (**d**) aspect map. Yellow stars indicate the main cities of the study area.
## 2 Fault Data Acquisition System  
An experimental system of bearing fault simulation in machinery is illustrated in Figure 1a. This experimental model for bearing condition acquisition has been widely used in previous research and has supplied satisfactory performance in bearing fault detection and diagnosis. Basically, the system setup consists of two driven and nondriven end shafts, which are linked together via a gearbox on the inside and fastened on the framework using NJ206-E-TVP2 bearings. The nondriven end shaft is loaded by two adjustable blades connected using a pulley and belt.  
An acoustic emission (AE) sensor with a wideband operation is attached on top of the nondriven end bearing housing at a 21.48-mm distance to capture the continuous information of bearing conditions in the time domain. This AE sensor is directly connected to a PCI-2 based data acquisition system to digitize the signals for further processing. In this study, AE signals of bearing conditions were acquired for a duration of 5 s and a sampling frequency of 250 KHz. The bearings used for condition monitoring were seeded with three primary defect types generated at various positionson the bearing's surface, consisting of a bearing crack on the outer surface (BCO), a bearing crack on the inner surface (BCI), and a bearing crack on the roller (BCR), as shown in Figure 1b. The bearing characteristic defect frequencies were computed and are described in Table 1. They included a ball pass frequency of the outer surface (BPFO), a ball pass frequency of the inner surface (BPFI), and two times of ball spin frequency (2 \(\times\) BSF (ball spin frequency)). Mathematical formulas for calculating these characteristic frequency components are given particularly in [22]. In this work, the AE data of bearing defects were obtained under different crack size conditions (i.e., 3, 6, and 12 mm) for experimental verification.
# Low-Complexity Microclimate Classification in Smart Greenhouses: A Fuzzy-Neural Approach
## 4 Methodology
### Network Architecture
Each of the aforementioned sensors and actuators is connected to the gateway and directly to others through an STM32 microcontroller and an SX1280 module. Through this network architecture, three types of control loops can be developed: extreme edge control loops, edge control loops, and cloud control loops. Typically, in other architectures, only edge and cloud loops are utilized, transferring sensor data from the GH to external computing units. This enhances control accuracy and decision-making through more complex algorithms, benefiting from higher computational power. However, it also increases the latency between sensing and actuation (such as cooling system activation, water pump control and illumination adjustment). In case of stringent latency requirements, the optimal approach is to employ extreme edge control loops with direct communication between sensors and actuators. In this case, computing and decision-making must be performed immediately on the microcontroller, which is the primary focus of this paper. Indeed, our work reduces the complexity of the NNs in terms of memory-allocated variables and the number of FLOPs required for microclimate prediction and classification.
# A Multipath Data-Scheduling Strategy Based on Path Correlation for Information-Centric Networking
## 3 System Design  
In this section, we introduce a multipath transmission mechanism applied to ICN to improve transmission efficiency and stability by exploiting the characteristics and functions of ICN.
## 1 Introduction
Another EVI compatibility analysis using Aqua MODIS (L2G daily 500 m, Collection 5) and VIIRS (L2G daily 500 m) showed that an average of MODIS EVI minus VIIRS EVI was \(-0.022\) for North America for August 2013 [17], indicating that VIIRS EVI was generally higher than MODIS EVI. The positive errors in VIIRS EVI over MODIS EVI were consistently observed in both Hyperion-simulated and actual sensor data. It should be noted that the errors might contain not only the spectral effects but the effects of other factors including differences in the product generation algorithm such as atmospheric correction and quality flags, geolocation errors, spatial resolution differences, and radiometric calibration uncertainties, to name a few. Numerous cross-sensor VI translation equations, especially for the NDVI, have been proposed and the techniques can basically be applied to the EVI. These techniques for cross-sensor VI translations can be categorized into three approaches as summarized in [17]: (1) polynomial based approach [18; 19; 20; 21; 22; 23];(2) band-averaging approach [24; 25; 26]; and (3) vegetation isoline-based approach [27; 28]. In our previous study, the vegetation isoline-based approach, that translates a reflectance of an arbitrary wavelength to that of another wavelength in the solar-reflective region based on the radiative transfer theory [29], was employed and a MODIS-compatible EVI with VIIRS spectral bandpasses was derived [17]. The derived equation had four coefficients that were a function of soil, canopy, and atmosphere, e.g., soil line slope, leaf area index (LAI), and aerosol optical thickness (AOT). The MODIS-compatible EVI resulted in a reasonable level of accuracy when the coefficients were fixed at values found via optimization for model-simulated and actual sensor data (the North American continent in August 2013), demonstrating the potential practical utility of the derived equation [17]. The primary objective of this study was to calibrate the MODIS-compatible VIIRS EVI equation for global conditions. \"Optimized\" coefficients were sought using a year-long, global VIIRS-MODIS dataset at the climate modeling grid (CMG) resolution of 0.05\({}^{\circ}\)-by-0.05\({}^{\circ}\). A secondary objective of this study was to develop a cross-calibration protocol that can be used to revise the optimum coefficients when a new dataset becomes available. We evaluated the extent to which errors decreased by applying the obtained VIIRS EVI equation with the optimized coefficients on global data and the degree to which errors varied as a function of sun-target-sensor viewing geometry, EVI values, and land cover type.
## 2 Materials and Methods
### Detection of Apnea Events based on Relative Changes in Respiratory Baseline
#### 2.3.1 Rule-Based Threshold Based Classification  
The rule-based classifier detects apnea in detection windows (\(W_{m}\)) when the input features \(I_{c}\) and \(A_{c}\) both activate the classification rules \(\hat{I}_{lb}\leq I\leq\hat{I}_{ub}\) and \(A_{c}>\hat{A}\) where \(\hat{I}_{lb}\), \(\hat{I}_{ub}\) are the classification thresholds for \(I_{c}\) and \(\hat{A}\) is the classification threshold for \(A_{c}\). An exhaustive search approach is applied for each of these thresholds in order to learn their optimal values. A novel approach was used to fit the rule-based classifier and learn the classification rules using a two step optimization method. The classification thresholds for \(I_{c}\) are optimized first to obtain the receiver operating characteristics curve (_ROC_) with the maximum area under _ROC_ (_AUC_) over our training data. Once the \(I_{c}\) classification rule is learned, the optimal classification threshold for \(A_{c}\) is learned by searching along the selected receiver operating curve (with maximum _AUC_) for the threshold that provides the maximum sensitivity (\(TPR\)) that constrains (\(FPR\)) not to exceed the maximum acceptable limit of 20% (\(FPR\leq 20\%\)). More details about the derivation and tuning of the rule-based classifier can be found in our recent study [49].
## 2 Materials and Methods
### Pharyngeal Pressure Measurements  
An essential requirement to evaluate the effect of nebulized surfactant delivered during HFNC in surfactant-depleted rabbits was to ensure the compatibility of the neonatal NCs with the rabbit's upper airway anatomy. Therefore, the fit and insertion stability in the rabbits' nose and the pharyngeal pressure generated by the different NCs was assessed in surfactant-depleted adult rabbits. The pharyngeal pressure generated during nasal CPAP at a level of 5 cmH2O with nasal prongs customized to the rabbits' nose was used as a reference value. Such nasal prongs have been previously used to deliver nasal CPAP, nasal intermittent positive pressure ventilation (NIPPV), and synchronized NIPPV to surfactant-depleted rabbits [29].  
The pharyngeal pressure measurements were carried out in 6- to 7-week-old surfactant-depleted male rabbits (Charles River Laboratories, Calco, Italy). All procedures conducted on animals were approved by the intra-mural Welfare Body and the Italian Ministry of Health (Prot.n\({}^{\circ}\) 1300-2015-PR). Animal handling and the surfactant depletion procedure by Broncho-alveolar lavages (BAL) have been described elsewhere [29; 30]. Briefly, animals were stabilized in mechanical ventilation (Acturonic Fabian HFO ventilator) with the following settings: fraction of inspired oxygen (FiO\({}_{2}\)) 100%, inspiratory flow = 10 L/min, respiratory rate (RR) = 40 cycles/min, positive-end expiratory pressure = 3 cmH2O, tidal volume (\(V_{\rm T}\)) targeted to 7 mL/kg (with a maximum peak inspiratory pressure not higher than 23 cmH2O). While intubated, the animals underwent repeated BALs by flushing 20 mL/kg of pre-warmed 0.9% NaCl solution until the partial pressure of arterial oxygen (PaO\({}_{2}\)) dropped below 150 mmHg (with a FiO\({}_{2}\) of 100%). If the PaO\({}_{2}\) remained below 150 mmHg in a subsequent blood gas analysis performed 15 min later, the respiratory distress was confirmed, and the animal was included in the study. The rabbits were then extubated and managed with HFNC support with a FiO\({}_{2}\) of 100% and a gas flow of 8 L/min.  
The pharyngeal pressure generated by the different NCs was determined by placing a customized flow sensor in the pharynx of the rabbits. The pressure signal was amplified, recorded, and analyzed with data acquisition software (PowerLab, ADI Instruments,Colorado Springs, CO, USA). Surfactant-depleted adult rabbits were managed for 5 min with HFNC, and the pharyngeal pressure was annotated. For each NC type, three independent pharyngeal pressure measurements were performed. During pharyngeal pressure measurements, the animal caregivers evaluated the insertion stability and the animal comfort by observational quality criteria.
## 6 Open Issues and Future Research Directions
### Coordination and Collaboration between UAVs  
Collaboration and coordination among UAVs are necessary for preventing collisions between many UAVs. Cooperation and coordination amongst UAVs are critical for enhancing routing efficiency in large-scale UAV networks and multi-UAV missions. For improved UAV communication, dynamic route planning is necessary. In dense deployment, decreasing the end-to-end latency between UAVs is still a key research area. Furthermore, a swarm of UAVs and satellites can generate a satellite-UAV network (CSUN) to provide wide area coverage for 6G and IoT applications [119]. Recently, 6G mobile communication technology has been proposed to provide communication for a swarm of UAVs to perform a specific mission [120].
# Neural Graph Similarity Computation with Contrastive Learning
## 4 Experiments and Results
### Experimental Settings
#### 4.1.1 Datasets  
Following the state-of-the-art works [27], we adopt three real-world datasets for evaluation, including:AIDS [49], a dataset consisting of molecular compounds from the antiviral screen database of active compounds. The molecular compounds are converted to graphs, where atoms are represented as nodes and covalent bonds are regarded as edges. It comprises 42,687 chemical compound structures, where 700 graphs were selected and used for evaluating graph similarity search;
* LINUX [50], a dataset consisting of program dependence graphs in the Linux kernel. Each graph represents a function, where statements are represented as nodes and the dependence between two statements are represented as edges. A total of 1000 graphs were selected and used for evaluation;
* IMDB [51], a dataset consisting of ego-networks of film stars, where nodes are people and edges represent that two people appear in the same movie. It consists of 1500 networks.  
More statistics of the dataset can be found in Table 2 (The datasets can be obtained from [[https://github.com/yunshengb/SimGNN](https://github.com/yunshengb/SimGNN)]([https://github.com/yunshengb/SimGNN](https://github.com/yunshengb/SimGNN)), (accessed on 1 September 2021).) For each dataset, we create the training, validation and testing sets by following the 60%:20%:20% ratio. For AIDS and LINUX, the ground-truth GED distance is obtained by using the A\({}^{*}\) algorithm [8]. Since the IMDB dataset contains larger graphs and the exact algorithms fail to work, approximate algorithms, i.e., HED [10], Hungarian [21] and VJ [22] are used to calculate the GEDs, where the minimum value is regarded as the ground-truth label [7].