esa-sceva/satcom-chunk-collection huggingface dataset:
# Wind Speed Spectrum of a Moving Vehicle under Turbulent Crosswinds
## 6 Conclusions  
An investigation of the wind speed spectrum characteristics of a moving vehicle under turbulent crosswinds has been conducted. The influencing factors, including the ratio of the vehicle speed to the wind speed, and the wind yaw angle from 15\({}^{\circ}\) to 175\({}^{\circ}\), were analyzed. Some conclusions were obtained:  
1. The wind speed spectrum peak changes with the wind yaw angles into the sinusoidal curve at different \(V_{\mathrm{ratio}}\) values. When the \(V_{\mathrm{ratio}}\) value is 0.1, the wind speed spectrum peak of the moving point gradually coincides with the peak of the static point at different wind yaw angles. As the \(V_{\mathrm{ratio}}\) is increased, the wind speed spectrum peak is at its maximum under smaller wind yaw angles, which is from 175\({}^{\circ}\) to 105\({}^{\circ}\) generally.
2. As the wind yaw angle gradually increases, the influence of the transverse wind speed spectrum also gradually increases. When the \(V_{\mathrm{ratio}}\) is 2, 3, 4 or 5, the frequency corresponding to the wind speed spectrum peak is first increased and then decreased.
3. The increases in both coherence and the power spectrum contribute to the larger aerodynamic forces and wind-induced vehicle vibration as the speed ratio increases.  
Figure 11: Coherence function of points on vehicle at different \(V_{\mathrm{ratio}}\) values.  
The correlation characteristics of the moving vehicles, which are of great influence, could be investigated by wind tunnel tests in further studies.
4. The wind speed spectrum and cross-correlation characteristics of moving points could be experimentally investigated in wind tunnels to further analyze the mathematical model. Furthermore, investigation of the aerodynamic characteristics of the moving vehicles should also be conducted.  
The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: the research described in this article was financially supported by the Natural Science Foundation of China (grant no.: 52078438); the Natural Science Foundation of the Southwest University of Science and Technology (grant no: 21 ZX7148); the key research base of the Humanities and Social Sciences of Sichuan Education Department of Chengdu Information Engineering University--Key project of the Meteorological Disaster Forecast, Early Warning and Emergency Management Research Center in 2022 (grant no.: ZHYJ22-ZD02); and the Wind Engineering Key Laboratory of Sichuan Province (grant no.: WEKlSC202302).  
Not applicable.  
Data are contained within the article.  
The author declares no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 4 Field Data Application Results and Discussion
Our method is suitable for VSP data with short arrays, and it is based on the assumption of straight events and linear variation of slowness. For long arrays or complex strata, data could be decomposed into several short array datasets, which is an approach that will be assessed in future research. Because we separate the wavefields in common-shot gather using our method, it is applicable for 2D and 3D offset-VSP data. In regard to the results derived with this method, there were some errors encountered in approaching the first and the last trace. In general, less geophones for VSP data will result in larger ratios of the error trace, which is caused by the systematic error of median filtering. The median filtering can be replaced by mean filtering and singular value decomposition (SVD), and the application effect will be realized in future research. Figure 15: Results for the separation of the VSP wavefield. (**a**,**e**,**i**) Downgoing P-wave; (**b**,**f**,**j**) Downgoing S-wave; (**c**,**g**,**k**) Upgoing P-wave; (**d**,**h**,**l**) Upgoing S-wave; (**a**–**d**) Results for our method; (**e**–**h**) Results for traditional median filtering; (**i**–**l**) Results for the high-resolution Radon transform method.
## 2 Theoretical Background of Applied Method
### Advantages and Disadvantages of Machine Learning Methods
of the network structure and unexplained network behavior, which reduces confidence in its use. However, these disadvantages are continually eliminated over time and by scientific research. The MARS technique is a computationally intensive methodology that fits a nonparametric regression model in the form of an expansion in product spline basis functions of predictor variables. The MARS algorithm produces continuous nonlinear regression models for high-dimensional data using a combination of predictor variable interactions and partitions of the predictor variable space. The MARS models seem well suited for considering the complex interactions among multivariate and cross-correlated predictor variables. The advantage of this technique is the ability to model large datasets more flexibly than linear models do. In addition, MARS automatically models non-linearities and interactions between variables. The \(k\)-NN algorithm is straightforward to understand and just as easy to implement. This algorithm does not explicitly create any model, and it simply indicates new learning based on entering data from historical data. One of the biggest problems of \(k\)-NN is selecting the optimal number of neighbors to be considered for new data. The performance improvements can be found by optimizing this parameter. In general, the \(k\)-NN does not work well with unbalanced data. The RF algorithm creates as many trees on the subset of the data and combines the output of all the trees. In this way, it reduces the overfitting problem in decision trees, reduces the variance, and improves accuracy. On the other hand, this algorithm requires much more computational power and resources. Generally, the RF is a very stable algorithm that can automatically handle missing values. The RF algorithm is usually resistant to noise and extreme values.
## 4 Results and Analysis
### Consistency Assessment with WELD Products  
#### 4.2.1 Visual Assessment  
Figure 6 shows the browse images of HJ and WELD monthly composites (true color composite) and the corresponding DOY flag images. In general, both HJ and WELD composites can capture the phenology change trend of green vegetation, but the HJ composites were visually better than WELD. For HJ, all of the HJ monthly composites were cloud-free, and the transitional regions between observations from different dates seemed homogeneous for each month. For WELD, half of the images of the 12 months were affected by the Scan Line Corrector (SLC)-off missing data or persistent cloud contamination. Particularly, the no data pixels approximately account for 72.17% and 65.21% in February and October, respectively. The cloud cover percentages for some months (from May to August, and November) in WELD were 9.17%, 29.84%, 6.81%, 10.34% and 28.53%, respectively.  
Figure 5: Browse images for the 16-day composited Top Of Atmosphere (TOA) reflectance results shown with bands 3, 2, 1 in red, green and blue. “DOY” means Day Of Year.  
Statistics for the total observations considered in the compositing period were made for both HJ and WELD. For HJ composites, more than 67.21% pixels had 10 or more days of observations within the target month, whilst 26.61% pixels had 15 days or more of observations. About 65.14% of pixels were acquired within four days in the composites. For WELD, only 1.51% pixels had clear observations for all 12 months. The span of days between most WELD pixels ranged from seven days to 25 days. Particularly, about 25.14% of WELD pixels were acquired over more than 15 days.  
Figure 6: _Cont._
# Real-Time Feedback in Swimming Exercises Using Embedded Devices in Water
## 2 Materials and Methods
### Communication  
The core contribution of this work consists of the integration of the ESP-NOW protocol into the application system. This protocol enables comprehensive communication functionalities within the application, ranging from control data transmission and visualization data exchange to inter-node communication for context-aware data sharing, as well as debugging via Wi-Fi messages.  
ESP-NOW employs a peer-to-peer communication model, enabling ESP devices to send and receive data directly, independent of external network infrastructure. In Figure 4, an overview of the system is presented.  
By combining the ESP-NOW protocol with the ESP32 Lolin Lite microcontroller and the MPU6050 sensor, the system supports real-time data acquisition and transmission. Efficient communication between devices is critical for ensuring low-latency data exchange and minimizing power consumption. Developed by Espressif Systems, ESP-NOW is a protocol that facilitates data exchange between ESP32 microcontrollers without requiring an active Wi-Fi connection or router. It is particularly suited for applications where low  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sensor** & **Axes** & **Interface** & **Power (mA)** & **Cost** &
\begin{tabular}{c} **Read Time Per** \\ **Sample/Interface** 1 \\ \end{tabular} & **Features** \\ \hline MPU6050 & & & & & & \\ (TDK InvenSense, San Jose, CA, USA) & 6 & I2C & \(\sim\)3.9 & \(\ell\)1–4\(\ell\) & \(\sim\)1.1 ms & Acc. \& Gyro. \\ LSM6D3 & & & & & & \\ (STMicroelectronics, Geneva, Switzerland) & 6 & I2C/SPI & \(\sim\)0.9 & \(\ell\)4–\(\ell\)7 & \(\sim\)1.0 ms/\(\sim\)0.03 ms & Acc. \& Gyro. \\ MPU9250 & & & & & & \\ (TDK InvenSense, San Jose, CA, USA) & 6 & I2C/SPI & \(\sim\)3.4 & \(\ell\)3–\(\ell\)10 & \(\sim\)1.6 ms/\(\sim\)0.1 ms & Acc. \& Gyro. \\ BNO055 & & & & & & \\ (Bosch Sensortore, Reutlingen, Germany) & 9 & I2C/UART & \(\sim\)12 & \(\ell\)13– & \(\sim\)1.6 ms/\(\sim\)2 ms & Acc., Gyro. \& Mag. \\ PmodACL2 & & & & & & \\ (Digilent, Inc., Pullman, WA, USA) & 3 & SPI & \(\sim\)0.002–0.01 & \(\ell\)13– & \(\sim\)0.01–0.02 ms & Acc. \& Gyro. \\ BMI270 & & & & & & \\ (Bosch Sensortec, Reutlingen, Germany) & 6 & I2C/SPI & \(\sim\)0.03 & \(\ell\)5-\(\ell\)10 & \(\sim\)1.0 ms/\(\sim\)0.03 ms & Acc. \& Gyro. \\ \hline \hline \end{tabular} \({}^{1}\) Read Time per Sample based on the interface that it uses.  
\end{table}
Table 1: Comparison of mutiple sensor used for movement monitoring.  
power consumption and rapid communication are essential, such as Internet of Things (IoT) systems, sensor networks, and smart devices [16].
# Sound Event Detection Using Derivative Features in Deep Neural Networks
## 1 Introduction
In the recent DCASE 2019 and 2020 challenges, a CRNN architecture based on the mean-teacher model was chosen as the baseline classification model for SED. As mentioned previously, deep neural networks for SED have evolved from simple FNNs to recent CRNNs, where an attention-based architecture as well as mean-teacher model-based training and evaluation are used. In the aforementioned studies on SED, the log-mel filterbank was calculated for each frame of the signal and was subsequently used input to the deep neural network. However, it is known that derivative features are frequently used in addition to the static features for improved performances in speech recognition [22]. Considering the characteristic similarity between speech and audio signals, we can expect that SED may be improved by using derivative features. Accordingly, the first and second delta features of the log-mel filterbank were extracted and two-dimensional feature maps were constructed using these features in the time-frequency spectrogram domain. Three feature maps, (corresponding to the first and second delta, and static features) were input to the CRNN for improved SED. The effectiveness of this technique was evaluated using a state-of-the-art CRNN. The remainder of this paper is organized as follows. In Section 2, we introduce the feature extraction method. The architecture of the CRNN used in this study is explained in Section 3. In Section 4, we introduce the databases and present the experiments. Section 5 concludes the paper.
## 2 Materials and Methods
### GC-MS Analysis  
Volatile compounds extracted using the SPME method were analysed by an Agilent gas chromatograph (7890A) equipped with a HP-5 MS capillary column (30 m length \(\times\) 0.25 mm I.D. \(\times\) 0.25 \(\upmu\)m \(d_{l}\)) and a 7200 accQTOFMS (Agilent Technologies, Mulgrave, VIC, Australia). Injection port temperature is 175 \({}^{\circ}\)C with splitless injection (split vent flow 50 mL/min from 0.5 min). The SPME fibre desorption time was 5 min. Helium (UHP, 99.999%) was used as a carrier gas with a constant flow rate of 1 mL/min. The oven temperature was held at 33 \({}^{\circ}\)C for 5 min, then ramped at 10 \({}^{\circ}\)C/min to a final temperature of 200 \({}^{\circ}\)C, where it was held for 5 min. The transfer line temperature was 200 \({}^{\circ}\)C. MS quadrupole and ion source temperatures were 150 and 230 \({}^{\circ}\)C, respectively. Total analysis time was 26 min. The MS was operated in full scan mode with a scanning range of \(m/z\) 30-400. The data acquisition rate was 50 spectra/s. Since this system is normally used for comprehensive two-dimensional gas chromatography, which generates very narrow peak widths, 50 Hz is our default setting, and was not adjusted for this specific study. Whilst this will decrease mass accuracy, the ion mass can still be reliably used to predict ion empirical formulae for compounds reported here. Data processing was performed using MassHunter Qualitative Analysis 10.0 (Agilent Technologies, Mulgrave, VIC, Australia). Compound identification was achieved using the NIST10 mass spectrum library, retention index (\(l\)), and mass accuracy. \(I\) (retention index) was calculated based on the elution times of \(n\)-alkane standards C7-C20 analysed under identical GC-MS parameters for the calculation of the Kovats Index values of the compounds.
## 5 Conclusions  
This work introduced a VR telerehabilitation system that uses off-the-shelf hardware, a real-time remote setup, and a bimanual training application that aims to improve the upper extremity's motor function. It is made of six activities and was evaluated by five physiotherapists. Two of them specialised in neuromotor disorders at Fundacion de Lesion-ado Medular and three of them specialised in functional rehabilitation and occupational therapy at CEU University. The definition of the sequences of actions and the range of movement for each joint and action for all six VR games for bimanual ADL exercises have been established. From the evaluation of this solution, it can be concluded that all six activities could be used for upper limb functional rehabilitation, with the exception of wrist and finger movements. Except for these caveats, the experts agree that the activities enable the user to perform suitable upper limb ADL-exercises in a realistic and immersive context. On top of that, the authors believe that this discussion offers a set of valuable lessons that can be applied to any future design of a VR application for rehabilitation of patients with ULD, particularly for bimanual interaction. Finally, the high usability score of the application reflects the reliability of the virtual reality tool for patients with UDL.  
Figure 3: Boxplot of System Usability Scale outcomes, overlapped with the 7-point adjective SUS scale interpretation [45].  
**Author Contributions:** A.R.: conceptualisation (equal); study implementation; software (lead); formal analysis (lead); writing--original draft (lead). J.A.S.-P.: writing--review and editing (equal). A.S.-P.: writing--review and editing (equal). R.R.: writing--review and editing (equal). R.G.-C.: conceptualisation (lead); writing--original draft (supporting); writing--review and editing (lead). All authors were accountable for the final version of the article. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The study was funded by the Spanish Ministry of Science, Innovation and University, under the grant European Regional Development Fund (ERDF) with project reference IDI-20191120, and also by the SPANISH GOVERNMENT (FEDER/Ministry of Science and Innovation/AEI), grant number RTI2018-097122-A-I00.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We want to thank our collaborators at Nokia Spain S.A. and Fundacion Lesion-ado Medular for their help during the development of the JIRAFA research project.  
**Conflicts of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.
## 4 Simulation Results
### Effect of Buffer Size  
A packet buffer must be able to hold received packets until they can be forwarded. In low duty cycle networks, a node cannot forward packets immediately, but must be able to hold packets received during  
Figure 12: Goodput in ZigBee with varying offered network load and different packet error rates.  
Figure 13: Goodput in TUTWSN with varying offered network load and different packet error rates.  
own active period until the active period of the next hop occurs. Assuming that a cluster head is routing data generated by \(n\) nodes, the average buffer requirements \(B\) can be calculated as  
\[B=n\cdot\frac{t}{i}, \tag{4}\]  
where \(t\) is the buffering time and \(i\) is the traffic generation interval. When packet errors do not occur, the buffering time \(t\) equals to the access cycle length. Packet errors cause beacon misses and thus increase the buffering time.  
In the buffer simulation, 12 nodes generated packets with 15 s average interval (15 B/s offered load). Thus, the average buffer usage should be 3.2 with the used 4 s access cycle. The end-to-end reliability with the ZigBee and TUTWSN is presented in Figure 16 and Figure 17, respectively. Without packet errors, 100% the maximum goodput is reached with the buffer size of 6 as expected. When packet errors occur, larger buffer sizes allow retransmissions and error recovery. TUTWSN requires more buffers than ZigBee because its routing protocol transmits periodic route advertisements to neighbors, thus causing an additional buffer requirement of 1-2 packets. The ZigBee used the simple cluster tree routing algorithm that does not cause route messaging but is only able to route to the PAN coordinator.  
Figure 14: Availability intervals with 90%-99.9% availability in ZigBee (12 sources, 5.5 B/s offered load).  
Figure 15: Availability intervals with 90%-99.9% availability in TUTWSN (12 sources, 5.5 B/s offered load).
## 2 Related Work
### Automation and Data Mediation  
An ontology is \"_a conceptualization of a domain represented in terms of concepts and the relationships between those concepts_\" [10]. Moreover, ontologies provide a common nomenclature to improve interoperability. The ontology layer is the higher conceptual model where disparate Web Services are matched _i.e._, two concepts match if they have been annotated with the same ontology concept [10]. Matching is how automation is enabled.  
Integration of discrete Web 2.0 service datasets is known as a \"mashup\". The creation of a mashup is a non-trivial exercise that requires the use of tools e.g., Yahoo! Pipes [15]. The tools are however, customized for specific services and/or data formats and to ensure seamless integration _i.e._, no manual intervention, a semantic mashup (or \"smashup\") is required [10]. The authors in [10] propose SA-REST (Semantic Annotation of Web Resources, formerly Semantic Annotation of RESTful WS). By semantically annotating the RESTful WS description, automation of matching and data mediation is enabled.  
Data mediation using semantic technologies is best explained with the aid of an example. Figure 4 outlines how the semantic layer ontology is used to facilitate data mediation. Let us assume that the services in Figure 4 are address-based e.g., the SOAP WS outputs its address in one line of XML and that the RESTful WS requires its input address over two lines in JSON format. The concepts, once they are matched at the semantic layer can mediate the data. This is done as follows: the SOAP service provides a \"lifting\" schema (XSL transformation) to transform the XML message into an ontology-compliant data structure. The RESTful WS provides a \"lowering\" schema to transform the common ontology data structure into the input format it requires.  
At this point it is worth pointing out a novel use in our solution of the SWS technology stacks outlined in Figure 4. The novelty in StoRHMv2 is the _use_ of the stacks. These stacks were invented for mediation and integration within and across the SOAP and RESTful HTTP paradigms _i.e._, to \"_...chain together the output from one Web service to the input of another Web service_\" [7]. As explained above, Figure 4 demonstrates the mapping of a SOAP WS output up to the (common) semantic layer so that the output can then be mapped to the input format required by the RESTful WS. Rather than mediate an output response from a SOAP WS to the input of a RESTful WS, we are mediating the SOAP WS _input_ to the RESTful WS input. This is demonstrated in Figure 5. The complete replacement of the SOAP message with its RESTful HTTP counterpart was not envisaged when the stacks were created.
## 2 System Model for Pairwise Clock Synchronization
### Clock Offset and Skew  
As discussed earlier, each sensor node in a WSN works independently and has its own clock. Ideally, the clock of a sensor node is modeled as \(c(t)=t\), where \(t\) represents the reference time [9]. However, due to the imperfections of the clock oscillator, as well as the environmental factors, such as pressure, temperature and hardware aging, the clock is subject to change as time evolves. Generally, the clock function of node \(i\) can be expressed as:  
\[c_{i}(t)=\theta_{i}+f_{i}\cdot t \tag{1}\]  
where \(\theta_{i}\) and \(f_{i}\) denote the clock offset (phase difference) and the clock skew (frequency difference) for sensor node \(i\), respectively [9]. In this way, if the clock of Node A is selected to be the reference time, it follows that:  
\[c_{B}(t)=\theta+f\cdot c_{A}(t)\]where \(\theta\) and \(f\) stand for the clock offset and skew between Nodes A and B, respectively. A graphical interpretation of the relationship between the clocks of Nodes A and B is shown in Figure 2. It can be seen that if \(\theta=0\) and \(f=1\), then Nodes A and B are perfectly synchronized. Otherwise, the synchronization process aims to estimate the clock offset \(\theta\) and skew \(f\), such that Node B can adjust its local clock to the reference time (Node A). As time evolves, the clock offset and skew are subject to change due to various reasons, such as the changes induced by the environment and sensor hardware aging. Therefore, the clock parameters are synchronized periodically, such that they are tuned up-to-date.