esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Material and Methods
### Application Case Study
#### 3.2.2 Training and Test Data  
Training and test samples are generated in preprocessing. With the test data, the user can evaluate the model before deploying it to the real-time system. For the results shown in the article the full window, from which training and test data are generated, spans from 1 May 2017 to 10 April 2020.  
For the test samples to be useful in the evaluation of the model, the test set must not overlap with training samples. Therefore, the total time series window is split, so that test samples are generated based on the last \(l_{c}+l_{o}+c_{test}\) days, where \(c_{test}\) is the number of test samples. Training samples are generated from a window starting at the beginning and ending at \(c_{test}+l_{o}\) days from the last day. Thereby, there is no overlap between the context windows of the training samples and test samples.  
\[D_{train}=\{(c_{i,j},o_{i,j})|\forall i,0<j<m-(c_{test}+l_{o})-(l_{c}+l_{o})\} \tag{5}\]  
\[D_{test}=\{(c_{i,j},o_{i,j})|\forall i,m-(l_{c}+l_{o}+c_{test})<j<m-(l_{c}+l_{ o})\} \tag{6}\]  
Weekly correlation is typically strong and therefore we want an equal number of samples starting on each weekday. Because there could also be differences between the first part of a month and the last part of a month, we also want samples spread over a month, and therefore we chose \(c_{test}=28\).  
Figure 1: Events are daily imported from music streaming services such as Spotify or YouTube. When an update of the prediction models is required, events are imported and converted to streaming time series. In pre-processing data are filtered (series below a threshold may be removed) and split into a training set and a test set. These are used for the visual evaluation of outliers and explanations. Finally, the model is uploaded to the real-time system where predictions, explanation, and outlier analysis can be accessed by DMGs web front end.  
Due to Challenge 1 we want to remove outlierish behavior from training data. From the perspective of DMG new songs in their catalog or songs removed from the catalogue are uninteresting outliers that should be removed to improve the model. Furthermore, DMG has less interest in songs with only very few streamings and the system will not be used to find outliers among these. We, therefore, filter songs with fewer streamings in the two periods \(c\) and \(o\) together than a threshold \(t_{lim}=10.000\). To ensure that there are streamings in both periods, time series with less than 10 percent of the threshold in either of the periods are also excluded.  
With all songs and the interval used for evaluation, this process generates 252,378 training samples. Training with this many training samples is time consuming and takes around 10 h on the available machinery (CPU: Intel Exeon E5-2620v4, 2.1 GHz, GPU: nVidia GeForce GTX 1080 TI 11 GB GDDR5). We, therefore, select a random sample of 15,000 time series for training. Experiments with different sample sizes are described in Section 4.
## 3 Results
### Surface Energy Characteristics  
The static contact angle was measured on the films of the PVDF NF layer with and without ZnO NWs using a sessile deionized water (DI water) droplet (4 uL) for surface energy characteristics analysis, as shown in Figure 4. PVDF is a fluoropolymer whose higher fluorine contents increase hydrophobicity of the membrane [25]. The surface morphology of the NF layer induces the increased roughness, which enhances the hydrophobicity.  
Figure 3: X-ray diffraction analysis results of electrospun PVDF NF layer, with and without ZnO NWs.  
Figure 2: Scanning electron microscope (SEM) images showing surface morphology analysis of (**a**,**b**) PVDF NF layer and (**d**,**e**) ZnO NW on the PVDF NF layer. (**c**,**f**) Average diameter and distribution analysis of the NFs.  
Contact angle variation by surface roughness is expressed by the Cassie-Baxter model, as in Equation (1):  
\[\cos\theta_{CB}=r_{f}\Phi_{S}\cos\theta+\Phi_{S}-1 \tag{1}\]  
where \(r_{f}\) is the roughness ratio of the film and \(\Phi_{S}\) is the ratio of the adjacent solid surface to the liquid interface; \(\theta\) is the equilibrium contact angle from a derivation of [PERSON]'s equation.  
In contrast, the ZnO NWs structure on the NF surface converts the PVDF NF layer to hydrophilicity. The hydrophilic property of the intrinsic ZnO is changed to superhydrophilicity, depending on the increasing surface roughness, which contacts with a water droplet. The contact angle changed by the surface roughness of the hydrophilic layer is expressed in [PERSON]'s model, as in Equation (2):  
\[\cos\theta_{w}=r\cos\theta \tag{2}\]  
where \(r\) is the true area of the NF surface to the apparent contact area. The increased surface roughness of the ZnO surface on the PVDF NF layer improves the surface-to-volume ratio, whose structure is favorable for charge transportation for gas detections on the ZnO NWs in continuous airflow. The sequential images in Figure 5, using a high-speed camera, show a DI water droplet dripping onto the ZnO NWs-PVDF NF layer. After dripping onto film, the droplet is absorbed beneath the film surface within 5 s. The hygroscopic film characteristics shows that the ZnO NWs on the PVDF NF surface attract a water droplet due to the capillary force between the NW structures, while simultaneously trapping the liquid in the pores of the PVDF NF layer. Depending on the surface energy of the base material in contact with the droplet, a transition to the Cassie-Baxter state and Wenzel state occurs as the surface roughness increases, which induces reinforcement of the hydrophobicity and hydrophilicity, respectively [26].  
Figure 4: **(a)** The static contact angle measurement and analysis on the film surface of (**b**) an PVDF NF layer and (**c**) an PVDF NF layer with ZnO NWs.
## 3 Results and Discussion
### Sensory Analysis  
We hypothesized that the distinct metabolomic profiles of the two amazakes would be reflected in the sensory attributes of the amazakes. The preliminary tastings indicated that this was indeed the case. To explore this further, we performed a consumer study that assessed acceptance and sensory attributes for the novel ABA amazake in comparison to the traditional AAO amazake. To assess their acceptance, a hedonic test was conducted that measured 'liking' in terms of aroma, texture, appearance, sweetness, taste, and the overall profile. The scores for all of the attributes fell between 3.8 and 4.9 out of 9, with no significant differences between samples, suggesting that the study participants found the flavors to be similar. In particular, the scores for 'overall liking' for both amazakes fell between \"neither like nor dislike\" and \"dislike a little\" (mean values of 4.52 and 4.47 for AAO and ABA, respectively), with no significant differences detected between samples, suggesting that the study participants found both amazakes about equally palatable (Figure 3A).  
The consumer study also assessed the sensory attributes with a CATA questionnaire (Figure 3B). The consumers described both amazakes as sweet, with AAO being perceived as more fruity, tart, and liquid, whereas the ABA was described as more nutty, yeasts, and lumpy (Figure 3). However, none of these attributes were deemed significantly different between AAO and ABA.  
The flavor profile of an amazake relates to the amino acid and organic acid contents. Argin, which we found to be more abundant in AAO, has been reported to produce  
Figure 3: Sensory analysis and volatile aroma compounds in _A. oryzae_ and _B. amyloliquefaciens_ amazakes fermented at 55 \({}^{\circ}\)C for 16 h. (**A**) Hedonic test (1 = extremely dislike to 9 = extremely like) and (**B**) check-all-that-apply test. There were no statistically significant differences in any of the attributes. Results are reported as means; \(n=105\). (**C**) Volatile aroma compounds detected. Three fermentation replicates were performed for each condition. Results are reported in area.  
pleasant, fruity, and sour aromas at pH 5.2. Similarly, proline, which is also more abundant in AAO, produces pleasant, flowery, and fragrant aromas [40]. In addition, fumaric acid, which is also more abundant in AAO, tastes fruity [41]. Taken together, these findings correspond with the sensory data, which describe AAO as being more tart and fruity than ABA.  
In summary, although the metabolic profile analysis showed that the amazakes are different, this was not reflected in the sensory analysis data, indicating that these differences are not noticeable to the average consumer.
## 3 Results and Discussion
### The Validation of Results
Furthermore, percentages up to 40% can be reported incorrectly between the two categories as a consequence of the heterogeneity of colors representing different types of vegetation. However, if vegetation is classified as a unique class, the accuracy increases considerably. Similar to 'Bare land,' any area in degradation tends to be erroneously classified, for the vegetation zones as well, supporting the interpretation done previously for this land class. In general, these results show that the HRC_ehas high accuracy when differentiating man-made infrastructure from natural elements, but higher accuracy requires further effort when discerning between high and low vegetation. In conclusion, an inaccurate classification cannot be interpreted only in terms of a general average as stated in, but as the relation between specific classes and the tendency to be misclassified with each other. We observed that 'HighVeg' and 'LowVeg,' and 'Building' and 'Paved,' have a strong connection, which is different in comparison with other classes. Discerning between them could be hard and a time-consuming activity if an editing process is required. However, the quality of the results increases considerably if the main goal of the LULC classification is differentiating between man-made structures and green areas. A high-resolution LULC study, also based on commercially provided images but using a different methodology [40], achieved similar accuracy results.
# Review of Satellite Interferometry for Landslide Detection in Italy
## 6 How InSAR Products Have Been Used for Landslide Studies in Italy
### Back-Analysis
[140] in the case of the Ponzano landslide (Abruzzo region). X-band images were the input for the pixel-offset analysis. Amplitude-based investigations were supported by multi-interferometric products and geomorphological evidence collected during ground surveys.
# Polling Mechanisms for Industrial IoT Applications in Long-Range Wide-Area Networks
## 1 Introduction
Power consumption: polling can also increase the power consumption of devices, as they must wake up from sleep mode to respond to polling requests. LoRaWAN is better suited for a polling system than other LPWANs because it supports different medium access policies that enable a more efficient downlink communication, which is key to enable new solutions to reduce latencies, take advantage of multicast support to reduce overhead, and have better management of power consumption. Within the LoRaWAN device classes, Class B and Class C have fundamental differences in their ability to receive messages and manage power. The choice between them depends on various factors related to the environment and specific application requirements. Class A focuses on uplink, where only the end device can initiate data exchange, so it is discarded for polling applications. The following explores scenarios where polling is more appropriate for each class. Class B allows for asynchronous bidirectional communication, meaning Class B devices can receive messages at scheduled times known as \"receive slots\". This approach is useful in environments where there is a need for a balance between power savings and responsiveness. This is particularly beneficial in scenarios where devices need to communicate periodically, like in remote monitoring applications, or in general when traffic is low, as it helps conserve energy by minimizing unnecessary listening. Class C, unlike Class B, allows for continuous message reception, partially sacrificing energy efficiency for increased responsiveness. Here, synchronous polling becomes an effective strategy in case environments where latency is critical, such as security systems or real-time asset tracking, or in situations where node density is high, and data traffic is constant. Both classes can be used to implement a polling system. The choice between Class B and Class C in a LoRaWAN polling application depends on the nature of the application and power consumption requirements. Class B is suitable for environments where energy efficiency is crucial and data traffic is sporadic, while Class C is preferable in situations that require higher responsiveness and are willing to accept higher energy consumption. This paper presents the challenges of polling for IIoT over LoRaWAN. Then, it proposes a number of techniques that can be used to facilitate the adoption of polling on this technology, given different requirements. Finally, we discuss the future of polling for IIoT over LPWANs. Abbreviations includes all abbreviations and their meaning for readers convenience.
# TEG Design for Waste Heat Recovery at an Aviation Jet Engine Nozzle
## 1 Introduction
If the additional impact on the propulsive efficiency due to the heat transfer from the core to the bypass flow is taken into account, the requirement on the specific power density of the TEG reduces to 100 Wkg\({}^{-1}\) (\"APD + CFD\" limit). Although the implementation of a TEG with the specified power density will improve fuel efficiency, it will reduce the maximum range of the aircraft due to the limitation on the maximum take-off weight (MTOW) of the aircraft. In order to reach a range invariant implementation of TEG, an increased electrical power density \(>\) 520 Wkg\({}^{-1}\) (\"APD range-invariant\" limit) is required according to the aircraft model. Thermal boundary conditions from the CFD model have been transferred to a finite element model (FEM) of a thermoelectric uni-couple in this work. Operational characteristics of this uni-couple have been extrapolated to a full coverage of the nozzle surface with the specified geometry from GasTurb. Parametric studies on the TEG design have been conducted in order to verify the feasibility to satisfy the gravimetric power density requirements and to specify the requirements on TEG design and its resulting performance. The gravimetric power density is optimized by variation of the TE element lengths and the filling factor, which is a known concept for the matching of thermal resistance of a TEG module [30; 31; 32; 33]. The filling factor is the ratio between the active surface of a TE module that is covered by the TE materials by the total surface of the module, which acts as the thermal coupling face to the heat source and sink, respectively.
# Can IMU Provide an Accurate Vertical Jump Height Estimate?
## 5 Conclusions  
In conclusion, a simple IMU-based system can provide coaches a lightweight, portable alternative to photoelectric cells and contact mats for in-field VJ height measurement as well as an alternative to the IMU placement on the athletes back in terms of simplifying the JH calculation.  
Conceptualization, S.M., S.T., A.U. and M.D.; methodology, M.D. and A.N.; software, A.U. and A.K.; investigation, A.U., S.M., S.T., M.D., A.N. and A.K.; writing--original draft preparation, S.M.; writing--review and editing, A.U., S.M., S.T., M.D., A.N. and A.K.; visualization, A.N.; supervision, M.D., S.T., A.N. and A.U.; project administration, A.K. All authors have read and agreed to the published version of the manuscript.  
This work is sponsored in part by the Slovenian Research Agency within the research program ICT4 QoL-Information and Communications Technologies for Quality of Life (research core funding no. P2-0246) and within the bilateral project between Slovenia and Serbia titled \"Sensor technologies as support systems for the detection and selection of talents in sport and monitoring the performance of athletes\" (research core funding no. BI-RS/20-21-023). This paper is a part of the project \"Effects of the Applied Physical Activity on Locomotor, Metabolic, Psychosocial and Educational Status of the Population of the Republic of Serbia\", number III47015, funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia-Scientific Projects 2011-2019 Cycle.  
The study was conducted in accordance with the postulates of the Declaration of Helsinki and was approved by the Ethics Committee of the University of Belgrade Faculty of Sport and Physical Education (02 No. 484-2).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.  
The authors declare no conflict of interest.
## 4 Summary  
This paper aims to provide researchers with more information about the achievements in the study of InSAR image co-registration. Co-registration is the first and one of the most important steps in the interferometric InSAR data processing. In this step, the distribution of tie points, the size of window for tie point matching, extraction of feature points and the measurement for the quality of an interferogram can affect the accuracy of the co-registration, which in turn determines the quality of any InSAR products, and thus the quality of the final InSAR product, e.g. DEM. Therefore, the enhanced techniques on these critical factors for improving the accuracy of InSAR image co-registration with tie points are reviewed in this paper. Specially, the most recent achievements on extraction of feature points, effects of interval of tie points, determination of an optimum window size and an indicator of an interferogram quality are emphatically reviewed.
## 2 The Principle of NMR Method  
Nuclear magnetic resonance (NMR) measurements of rock pore structure are closely related to the relaxation characteristics of hydrogen-containing fluids in the pores due to the magnetism of the nucleus and its interaction with an external magnetic field [17]. After the specimen is situated into the magnetic field, the H proton resonates by emitting a certain frequency of radiofrequency pulses, and the H proton absorbs the energy of the radio frequency pulse. The process of releasing energy from the H proton can be detected by a special coil, which is the NMR signal. The energy release rate of samples with different properties is different, and therefore, a change in the characteristics of rock pore structure can be directly reflected by comparing the difference in sample nuclear magnetic signals before and after the application of stress.  
The detected magnetization signals are featured in the relaxation time, depending on the properties of the pore-filled fluids interactions between filled fluids and pores, pore size distribution, and surface relaxivity [18]. The total transverse relaxation time is determined, as shown in Equation (1) [19]:  
\[\frac{1}{T_{2}}=\frac{1}{T_{2 surface}}+\frac{1}{T_{2 molecules}}+\frac{1}{T_{2 free}} \tag{1}\]  
where \(T_{2}\) is the total transverse relaxation time; \(T_{2 surface}\) is the transverse relaxation time of the surface; \(T_{2 molecular}\) is the transverse relaxation time of the free diffusion of molecular hydrogen; and \(T_{2 free}\) is the transverse relaxation time of the free fluid.  
Under ideal diffusion conditions (i.e., the porosity of the rock is small, and the surface relaxation rate is very slow), the transverse relaxation time of the surface for a fluid-filled rock pore can be simply expressed as follows:  
\[\frac{1}{T_{2 surface}}=\rho_{2}(\frac{S}{V})_{\rm{pore}} \tag{2}\]  
where \(\rho_{2}\) is the surface relaxivity; \(S\) is the surface area of the pores; \(V\) is the volume of the pores; and \(S/V\) is the surface-to-volume ratio of the pores.  
As illustrated by [PERSON] et al. [20], for saturated rocks, \(T_{2 molecular}\) and \(T_{2 free}\) can be negligible. Therefore, combining Equations (1) and (2), \(T_{2}\) can be simply expressed as follows [21]:  
\[\frac{1}{T_{2}}\approx\frac{1}{T_{2 surface}}=\rho_{2}(\frac{S}{V})_{\rm{pore}} \tag{3}\]  
As shown in Equation (3), the pore structure characteristics of rock specimens can be accurately obtained by measuring \(T_{2}\).  
Magnetic resonance imaging (MRI) is based on the different attenuation characteristics of the released energy in different structural environments within the specimen. The position and type of the atomic nucleus of the specimen can be obtained by detecting the electromagnetic waves emitted from an external gradient magnetic field, and a structural image of the specimen can be drawn accordingly. Therefore, characteristics of the pore structure of a rock can be obtained by MRI.
## 2 Materials and Methods
The eddy covariance direct measurements are the net carbon dioxide fluxes, as the balance between photosynthesis and ecosystem respiration, here defined as Net Ecosystem Exchange, NEE. Raw data were processed using the eddy covariance processing software EddyPro, version 6.2 [17] to determine NEE with an averaged half-hourly period. Data processing followed standard methods and included coordinate rotation with tilt corrections, linear detrending, despiking, time lag corrections, correction of low-pass filtering effects with Moncrieff [18] and Webb-Pearman-Leuning (WPL) [19] correction. Snow melting and canopy growth as affecting surface roughness were included in the model according to the EddyPro standards. Footprint distances (1D footprints) were estimated according to the \"simple footprint parameterization\" [20] on a half-hourly basis. For the Moscow station, half-hour flux measurements with footprints crossing the border of the field were filtered out. Since for the Kursk field, yield was assessed for the area around the station--a 300 m \(\times\) 300 m square--with the station at the center, any footprints not fitting in this area were filtered out. Quality-control tests for fluxes (1 to 9 flags) were performed according to Foken [21]. Subsequently, quality filtering was applied to the half-hour flux data according to the following rejection criteria: (1) incomplete half-hour measurements; (2) NEE with quality flag values of 8 or 9; (3) data with the cumulative 70% flux footprint originating outside the footprint borders of the Moscow or Kursk fields, respectively (Figure 1, blue lines); (4) excessive spikes of NEE exceeding 3\(\sigma\) for the half-hour of monthly averaged data. Negative nighttime CO\({}_{2}\) fluxes were also removed from the datasets. After post-processing and quality filtering, 48.0% of the CO\({}_{2}\) flux data for the tower in Moscow and 53.0% for the tower in Kursk were suitable for analysis. The gaps in PPFD values were filled by linear regression based on the values of the air temperature (R\({}^{2}\) = 0.45). The gap-filling of the eddy covariance and meteorological data was performed through methods proposed by the Eddy covariance gap-filling and flux-partitioning tool of the Department of Biogeochemical Integration at the Max Planck Institute for Biogeochemistry-REddyProc [22]. The methods used were similar to [PERSON] et al. [23] but consider both the co-variation of fluxes with meteorological variables and the temporal autocorrelation of the fluxes [24]. A paired samples _t_-test was used to test the significant differences in diurnal variations of NEE between the tower in Moscow and the tower in Kursk. In all tests, a significance level of 0.05 was used. Quality filtering, flux gap filling and statistical analysis were performed using R language [25].