esa-sceva/satcom-chunk-collection huggingface dataset:
# The RUNE Experiment--A Database of Remote-Sensing Observations of Near-Shore Winds
## 4 Results  
### Wind Climate  
The RUNE campaign was characterized by periods of very windy weather with sustained winds from the west. Figure 5a panel shows the wind speeds from the cup anemometer at 100 m AGL at the Hevsore meteorological mast (pos. 8). The wind speed at 100 m reaches more than 25 m\(\cdot\)s\({}^{-1}\) several times, with a maximum wind speed of 35.3 m\(\cdot\)s\({}^{-1}\) at 18:30:00 UTC on 29 November. The wind direction (Figure 5b panel) is taken from measurements of a wind vane at 100 m AGL at the Hovsere meteorological mast. These records show that the flow was characterized by westerlies during November and December and then a period of several weeks with mostly easterlies followed. After this intermezzo, westerlies were again predominant.  
Figure 5: _Cont._  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Name** & **Range Gate Height ALL (m)** \\ \hline Alíé & 100, 150, 175, 200, 250, 300, 350, 400, 450, every 100 m from 500–1500 \\ Bura & 30, 35, 40, 50, 62, 75, 87, 100, 112, 125, 140, 155, 175, 200, 250, 300 \\
3E & 40, 57, 70, 88, 107, 125, 150, 200, 250, 300 \\ WLS66 & 40, 50, 60, 74, 80, 90, 100, 110, 124, 150 \\ Lidar buoy & 40, 47, 59, 79, 97, 117, 134, 147, 172, 197, 209, 247 \\ \hline
**Name** & **Range Gate Height ASL (m)** \\ \hline Virtual mast & 5, 28, 52, 75, 99, 122, 146, 169, 192, 217, 275, 298, 322, 345, 368, 392, \\  & 415, 438, 461, 486, 520, 544, 568, 591, 615, 639, 662, 687, 710, 734, \\  & 757, 793, 816, 840, 864, 888 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The heights of the range gates of the VAD lidars and those of the virtual mast scenario.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 5 Experiments and Results
### Experiment of Carrierless PPM Demodulation Using an Integration Method  
For the demodulation of carrierless PPM signals, the methods of integration, PLL, and conversion to quasi-FM-PWM have been implemented using hardware circuits. For the integration method, the circuit shown in Figure 6 is implemented. In the experiment, a 12 kHz PPM carrier was used with input signals of a 180 Hz sine wave and a 180 Hz triangle wave as the information signals. The demodulation results are manifested in Figure 18.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**SNR** & **Pearson Correlation** \\ \hline No noise & \(-0.8908\) \\ \(-8.4387\) & \(-0.1505\) \\ \(-3.0871\) & \(-0.6692\) \\ \(1.4888\) & \(-0.8291\) \\ \(6.1815\) & \(-0.8802\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative performance analysis of PPM demodulation under varying noise conditions.  
Figure 17: PPM generation and demodulation performance with VCO noise injection. (**a**) Input sinusoidal signal (5 Hz, 1 Vpp); (**b**) Differentiated input signal serving as VCO control; (**c**) Noise-corrupted VCO output showing degraded frequency modulation characteristics; (**d**) PPM signal generated from noisy VCO output, exhibiting reduced position accuracy; (**e**) Demodulated signal after monostable processing showing noise-induced distortion; (**f**) Final recovered signal with noise-related degradation, demonstrating system performance under realistic operating conditions.
## 3 Proposed Method
### Route Generation
#### 3.5.2 Chromosome Encoding  
Since this paper plans corresponding travel routes for users according to the value of urgency, different chromosome coding methods should be used for different values of urgency. Several effective edge selection strategies for generating different chromosomes are described below.  
Strategy 1: The closest distance priority strategy. Priority is given to selecting the effective edge closest to the current starting point in space so that more edges can be added to the path.  
Strategy 2: The shortest travel time priority strategy. Under time \(t_{0}\), the effective edge with the shortest travel time is preferred so that more time budget can be reserved.  
Strategy 3: The highest quality ratio priority strategy. Give priority to the effective edge with the largest quality ratio to ensure higher cost performance.  
Strategy 4: The maximum scenic score priority strategy. Priority shall be given to the effective edge with the maximum scenic score to ensure a higher scenic score.  
Strategy 5: Random priority strategy. An edge is randomly selected from the effective area to avoid the problem that the above strategies may search for effective edges in small areas of the road network.  
Based on the above effective edge selection strategy, Algorithm 1 gives the pseudo code of chromosome encoding.  
In Algorithm 1, first, initialize an empty chromosome set, and then execute lines 3-24 to return the chromosome set under the condition that user constraints are met. Line 3 obtains the valid search area according to the method in Section 3.5.1; Lines 4-14 acquire candidate edge sets, that is, using different effective edge selection strategies according to different urgencies to generate candidate edge sets. Specifically, when \(urgency>0.7\), the edge that is closer to the current starting point and shorter travel time is selected from the valid region each time to join the candidate edge set. When \(urgency<0.3\), the candidate edge is selected and added to the candidate edge set according to strategies 1, 2, 4, and 5. When \(0.3\leq urgency\leq 0.7\), the candidate edge is selected and added to the candidate edge set according to strategies 1, 2, 3, and 5. Lines 15 to 18 update the departure time, remaining time budget, and starting point for each edge in the candidate edge set. Lines 19-24 recursively call the chromosome encoding function encoding () to generate all the sub-chromosomes and carry out subsequent chromosome growth.
## 2 Materials and Methods  
This study has been made with the database provided by AEMET, an agency that depends on Ministry of Agriculture, Food and Environment of the Spanish government. From all the stations that AEMET has deployed around Spain, this work used two meteorological stations, which are located at Gran Canaria (GC) airport (Gran Canaria, Canary Islands) and South Tenerife (TF) airport (Tenerife, Canary Islands), respectively. This database offers data relative to temperature, humidity, solar radiation, meteors, precipitation, wind speed and clouds. The data from each phenomenon is given in an Excel sheet format (.xls) including the information collected hourly by both stations under study alongside the whole period of study from 2003 to 2007. In the same way a.txt file is given with explanatory information about each.xls sheet as a help to understand data contained on it. Figure 1 shows the map of the Canary Islands (Spain) and the details of Gran Canaria and Tenerife; the airports' locations are marked with red dots.  
Canary Islands are located in a subtropical climate area beaten by Trade Winds with warm temperatures. However, they suffer from the remains of tropical storms that hit the Caribbean when they turn around the Atlantic Ocean. The study areas was located on the southeastern of both islands, were winds allows the generation of green energy.  
To achieve an accurate prediction, a preprocessing stage is applied to adapt the information given to the input of the prediction system. Consequently, the procedure must be quick, avoiding large delays when the prediction is performed; and thus reaching almost a real-time prediction approach.  
Firstly, it is necessary to check each sheet to not introduce false or nonexisting data to the system. The reason is due to the periods of time where there is no data, caused by any damage or inactivity of the station or anyone of its sensors.  
Secondly, the concatenation and adaptation (in terms of measurement unit) are done before to insert the parameter information to the system. Temperature and wind speed are two examples of meteorological phenomena that have more information during the study period. For this reason, they are two of the phenomena chosen to obtain the prediction.  
Finally, once the preprocessing stage has been done, resulting in the obtaining of the input data, it is possible to apply the classification systems. Our forecasting system is based on the use of the regression functions, which is described below.  
The number of samples that will be used in this study are shown in Table 1.  
Figure 1: Map of the Canary Islands, detail of Gran Canaria to the right and Tenerife to the left.
###### Abstract  
Light use efficiency (LUE) models have been widely used to estimate terrestrial gross primary production (GPP). However, the estimation of GPP still has large uncertainties owing to an insufficient understanding of the complex relationship between water availability and photosynthesis. The plant water stress index (PWSI), which is based on canopy temperature, is very sensitive to the plant stomatal opening and has been regarded as a good indicator for monitoring plant water status at the regional scale. In this study, we selected a cork oak plantation in northern China with an obvious seasonal drought as the research object. Using the ground-observed data, we evaluated the applicability of the LUE models with typical water stress scalars (MOD17, MODTEM, EC-LUE, ECM-LUE, SM-LUE, GLO-PEM, and Wang) in a GPP simulation of the cork oak plantation and explored whether the model's accuracy can be improved by applying PWSI to modify the above models. The results showed that among the seven LUE models, the water stress scalar had a greater impact on the model's performance than the temperature stress scalar. On sunny days, the daily GPP simulated by the seven LUE models was poorly matched with the measured GPP, and all models explained only 23-52% of the GPP variation in the cork oak plantation. The modified LUE models can significantly improve the prediction accuracy of the GPP and explain 49-65% of the variation in the daily GPP. On cloudy days, the performance of the modified LUE models did not improve, and the evaporative fraction was more suitable for defining the water stress scalar in the LUE models. The ECM-LUE and the modified GLO-FEM based on PWSI had optimal model structures for simulating the GPP of the cork oak plantation under cloudy and sunny days, respectively. This study provides a reference for the accurate prediction of GPP in terrestrial ecosystems in the future.  
crop water stress index; light use efficiency model; gross primary production; water stress scalar; weather condition +
Footnote †: journal: remote sensing
## 4 Spectrum of Heterogenous Services for Transport Means and the Communication of Information
### The Information Flows and Modelling for the Context of Accidents  
Recent studies present the behavioural models, in which vehicle driver selects the optimal speed to avoid accident cases. There are networks where costs are not considered but just the maximum traffic flow. Of course, studies have not yet proven the optimal design of a transportation network configuration for various time-periods. There are empirical researches that investigated the main reason for the variability in travel time in the occurrence of accidents, including major accidents, which block traffic lanes for long periods and several minor incidents such as vehicle failure and other factors [31]. Such analysis describes the lost time that happened due to such accidents on roads.  
There are on-going discussions about how to report accidents. Automatically detected traffic accidents using accelerometers could be reported to the central server immediately after the crash event using GPS coordinates. Along with the data, the vehicle number is reported as well. The following messages are created after traffic accidents are detected through the arm (ARM) controller. In such cases, ARM7 controller, accelerometer (sensor used for accident detection), Global System for Mobile (GSM) communications, and GPS are used to provide information about the situation. By using accident detection and messaging systems in the vehicle, it is possible to reduce response time. However, such systems are not available in all truck due to being expensive. In case of installation, the system \"On Star\" for accident detection and communication is widely used.  
Other studies are oriented to identification of risky locations in the network. For such cases, the critical issue is data gathering and the identification of locations. In many cases reported messages in social media are not always linked with GPS coordinates of accident locations. Some of this data is available from traffic cameras and/or traffic monitoring devices. The new data collection methods involve satellite images collected from Google Map API (Application Programming Interface) after the messages on social media appear with the identified street section which is blocked because of the accident [32].  
The third type of studies focuses on accident removal time. These investigations extend the first type of studies and add information on accident detection and communication, and analyse traffic light control options. Authors propose the control traffic signals automatically by taking the Radio Frequency (RF) module, which indicates when an emergency vehicle is arriving at crossroad signal lights (closer than 100 m [33]) and when it is approaching to help the actors of an accident. Such a system decreases the time of road block.
## 1 Introduction
In [29], this scheme was analyzed for a strongly convex function with a Lipschitz gradient, and the range of the possible convergence rate was presented. Despite the results obtained for different methods with momentum mentioned above, there is a lack of correct understanding of the roles of parameters in computational schemes with momentum. As mentioned by investigators, understanding the role of momentum remains important for practical problems. For example, in [19], the authors demonstrated that momentum is critical for good performance in deep learning problems. However, in another modification of the HBM, [PERSON] and [PERSON] [17] demonstrated that momentum in practice can have a minor effect, which is insufficient for acceleration of convergence. Therefore, additional theoretical analysis of methods with momentum is important in our time. The presented paper is devoted to the analysis of a method with two momentum parameters, as proposed in [28]. For the functions from \(\mathcal{F}_{I,L}^{1,1}\) (\(l\)-strongly convex \(L\)-smooth functions), this method was analyzed in [29], where global convergence for the special choice of parameters was proven. In the presented paper, we try to focus our attention on the case of quadratic functions from \(\mathcal{F}_{I,L}^{1,1}\), in order to obtain the inequalities for parameters that guarantee global convergence, to obtain optimal values of the parameters, and to understand the effect of an additional momentum parameter on the convergence rate. Convergence conditions are obtained, and corresponding theorems are formulated. The constrained optimization problem for obtaining optimal parameters is stated. As demonstrated in numerical experiments, in the quadratic case, the inclusion of an additional parameter does not improve the convergence rate. The role of this parameter is demonstrated with the use of the ordinary differential equation (ODE), which is equivalent to the method. This parameter provides an additional damping effect on the oscillations, typical for the HBM, according to its non-monotonic convergence, and can be useful in practice. In the numerical experiments for non-quadratic functions, it is demonstrated that this parameter also provides damping of the oscillations and leads to faster convergence to the minimum in comparison with the standard HBM. Additionally, the effect of this parameter is demonstrated for the non-convex function that arises in recurrent neural network training. The paper has the following structure: Section 2 is devoted to the theoretical analysis method in application to strongly convex quadratic functions. The effect of an additional momentum parameter is analyzed. The results of the numerical experiments for non-quadratic, strongly convex, and non-convex functions are presented in Section 3. Some concluding remarks are made in Section 4.
# Sensor-Based Solid Waste Handling Systems: A Survey
## 8 Conclusions  
A comprehensive study on various technological approaches towards solid waste management for smart cities is presented in this paper. The literature is categorized into three sections based on the evolution of technology. Firstly, RFID-based approaches for solid waste management have been discussed and analyzed with typical system architecture. The lack of real-time monitoring, short-range communication, and the need for additional infrastructure for trash bin monitoring are some of the challenges observed in this approach. Secondly, WSN-based approaches for solid waste management have been analyzed. These approaches are superior to RFID but face challenges in designing power-efficient and easily deployable systems. Finally, state-of-the-art IoT-enabled solid waste management systems have been studied. Low power sensors, wireless microcontrollers, and lightweight communication protocols enhanced IoT-based approaches toward solid waste management. Moreover, these approaches uplifted the WSN based systems with interoperability and dynamic adaptation of the nodes. On the basis of this survey, it is concluded that IoT-based solutions are optimal for the deployment of solid waste handling systems in urban areas. In addition, LoRaWAN is observed as the preferred communication protocol among LPWAN technologies that enabled IoT-based trash bin deployments in urban areas. Nevertheless, a lot of research scope is there for further improvement of these systems in ultra-low power node design and optimized energy harvesting methods for self-powered nodes. We envisioned that this comprehensive study on solid waste management systems emphasizing technology phases would be a good reference for future researchers to develop advanced systems for smart cities. Moreover, this would be a reference to the municipalities or waste management companies to plan the establishment of efficient solid waste handling systems in smart cities by adopting appropriate system models.  
Investigation and methodology, S.V.; project administration and supervision, S.R.J.R. and M.S.S.R.; writing, review and editing, S.V., S.R.J.R., M.S.S.R. and A.M.A.-M. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Council for Scientific and Industrial Research, Pretoria, South Africa, through the Smart Networks collaboration initiative and IoT-Factory Program (Funded by the Department of Science and Innovation (DSI), South Africa).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Application
### Clinical Application
#### 4.1.1 Neonatal Monitoring  
Neonates and infants have very delicate and sensitive skin so non-contact methods would be the preferred approach to monitoring them. Several studies have used iPPG methods to monitor neonates. For example, [PERSON] et al. [122] introduced a baby-friendly non-contact approach to monitor several vital signs such as HR, RR, skin temperature, and SpO\({}_{2}\) of infants and neonates in NICUs with gestational ages of 24 to 39 weeks. To measure HR and RR, RGB color magnification and IR-thermography were used, respectively. Another study by [PERSON] et al. [123] applied the iPPG technique to continuously monitor HR, RR, and SpO\({}_{2}\) of neonates and infants in an NICU. They also detected bradycardia while monitoring participants continuously for long periods. Other studies [108; 109; 110] have also proved the promise of iPPG in neonatal monitoring.
# Adjustments to SIF Aid the Interpretation of Drought Responses at the Caatinga of Northeast Brazil
## 2 Materials and Methods
### Environmental Indicators  
To cover the basic temperature and water-availability aspects of environmental characterization we used surface temperature, precipitation rate and soil moisture data. While the relative influence of the different environmental variables involved is species-specific and because the effects of micro-climate can not be adequately incorporated in our experimental design, we assumed that surface temperature, precipitation and soil moisture together are the main drivers of the observed plant responses to environmental conditions.  
Daytime land surface temperature from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument (MOD11C3, version 6), at the monthly temporal resolution was used to represent temperature at the observed communities level. This data was used in a 0.05\({}^{\circ}\) by 0.05\({}^{\circ}\) grid and at monthly temporal resolution [46].  
Monthly precipitation rate from the Tropical Rainfall Monitoring Mission (TRMM--TMPA 3B43, version 7), which combines active and passive measurements [47], was employed as a measure of precipitation at the observed communities' level. This data is originally gridded at 0.25\({}^{\circ}\) by 0.25\({}^{\circ}\) and monthly temporal resolution. We regridded TMPA data to a 0.05\({}^{\circ}\) by 0.05\({}^{\circ}\) grid using the Nearest Neighbor method. This method, also known as Pixel Replication, does not effectively interpolates data but only regrids it into a finer or coarser grid by repeating the original information into corresponding pixels without changing its values or location and thus, maintaining the statistical structure of the original gridded data [48; 49].  
Since precipitation and water availability can be uncorrelated due to various edaphic and ecological factors, we have chosen to also include a measure of soil moisture in the analysis. For that end we used ESA's Soil Moisture CCI product [50], which combines both active and passive soil moisture measurements at daily frequency and in a 0.05\({}^{\circ}\) by 0.05\({}^{\circ}\) grid. We, therefore, resampled daily soil moisture data to monthly means for compatibility with other data sources used in this study.  
All environment-related data were obtained for the period from February 2007 to December 2017.
## 4 Experimental Validation
### Results of Experimental Test
#### 4.2.2 Comparison of Experimental Data and Numerical Calculation  
Table 4 shows the comparison of experimental data and numerical calculation conducted based on FEM. In general, the average errors of the braking torque for each rotation speed between the experiments and numerical calculation are very small (almost below 5%). These different values are considered due to several aspects, including the coil temperatures and heat generation during the braking process. In addition, it is difficult to maintain the rotation speed of the electric motor as a constant when braking occurs. Moreover, the vibrations affect the load cell readings, as well as the material of the brackets used in the electromagnet. As the error value is very small (below 5%), it is believed that the data generated by the numerical calculation are valid.