esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
Following what is described in Section 2, a few prototypes of Smart Drifters were developed. The MARTA Smart Drifter is part of a group of floating elements that communicate with each other by transmitting and receiving wireless data, so they collectively behave as a cluster. The suffix \"smart\" indicates the ability of the drifter to make decisions about different communication strategies (frequency of data sending, type of data sent, signal strength, synchronization process with other elements, etc.) according to different occurrences (rough sea, very expanded cluster, etc.). The MARTA drifter cluster consists of two types of floating units, one simpler and cheaper (the secondary) and the other more sophisticated and expensive (the primary). Almost all the elements in the cluster belong to the secondary type. These are passive in the sense that they have no autonomous navigation capabilities. They are equipped with LPWAN transmission technology, GPS, sensors, SD flash memory, some data elaboration capability, and enough solar panels and storage batteries to ensure unlimited continuous operation time. The few primaries are similar to the secondaries, but they are \"active\" in the sense that they are equipped with a pair of underwater thrusters and have a more advanced data-processing capacity and long-range communication capability (GSM or satellite for some applications). During regular operations, all the MARTA drifters are moving along with the surface sea current.
# Priority Pricing for Efficient Resource Usage of Mobile Internet Access
## 3 System Behavior Analysis Model
So far, when computing \(\{\lambda_{i}\}^{t}\), only the jobs that have arrived over one broadcast interval between (\(t\)\(-\)1) and \(t\) are considered. I implicitly assumed that the size of the \(\{\lambda_{i}\}\) broadcast interval was equal to that of the interval over which the job arrival rate was measured. If the \(\{\lambda_{i}\}\) broadcast interval is smaller than the interval for the job arrival rate measurement, then \(\{\lambda_{i}\}^{t}\) must be computed by considering the job arrivals over multiple broadcast intervals. To this end, this study uses a sliding window with slots, \(\Delta_{1}\)-\(\Delta_{F}\), where \(F\) is the number of \(\{\lambda_{i}\}\) broadcasts during which the job arrival rate is measured. For instance, if the job arrival rate \(\lambda\) is measured by counting the job arrivals during the last 1 s period and the \(\{\lambda_{i}\}\) broadcast interval is 0.1 s, \(F\) equals 10. Each \(\Delta_{f}\) slot contains the job arrival rate for each service level during a broadcast interval. The \(\Delta_{F}\)slot keeps the information about the most recent broadcast interval, while \(\Delta_{1}\) keeps that of \(F\) intervals before. The procedure for computing the job arrival rate vector at time \(t\) is formulated using the following equations:  
\[\Delta_{f-1}\;=\;\Delta_{f}\;\;\text{for}\;\forall f>1, \tag{5}\]  
\[\Delta_{F}\;=\;\lambda\cdot Prob\big{(}i,\;S^{t}\big{)}\;/\;F\;\;\text{for}\; \forall i\in I, \tag{6}\]  
\[\lambda_{i}^{t+1}\;=\;\sum\Delta_{f}\;\;\text{for}\;f,\;\forall i\in I. \tag{7}\]  
Equation (5) shifts the value in the oldest slot \(\Delta_{1}\) to automatically shift the sliding window and keep track of the most recent \(F\) number of update information. Equation (6) computes the job arrival rate for each service level for the newest slot, \(\Delta_{F}\). The values of all \(F\) slots are summed to obtain \(\left\{\lambda_{i}^{t+1}\right\}\) in Equation (7). Instead of Equation (7), exponential averaging [38] can be used.
## 1 Introduction
The main radiometric calibration methods used thus far for remote sensing sensors are laboratory integrating sphere calibration [20; 21; 22], on-station calibration [23], diffuse reflectance plate calibration [24; 25], etc. Laboratory integrating sphere calibration is mainly a high-precision test of parameters, such as the remote sensor electronic gain and bias, central wavelength and bandwidth of each channel, signal-to-noise ratio, spatial resolution and spectral response function using relevant laboratory equipment, which is traceable with a low-temperature absolute radiometer or a standard blackbody uniform radiation quantification standard. On-station calibrations use a calibration system mounted on a satellite platform to periodically monitor changes in the radiation response of the satellite during its orbital operation. Diffuse reflectance plate calibration uses a diffuse reflectance plate and a spectrometer probe for simultaneous fixed-point continuous observation. Concerning the radiometric calibration methods for remote sensing sensors, only laboratory integrating sphere calibration and field diffuse reflectance plate calibration methods can meet the requirements for the radiometric calibration of micro-spectrometers. The field diffuse reflector calibration method is simple and easy to operate, whereas the laboratory integrating sphere calibration requires a lot of time and money, and our group does not meet the conditions for conducting laboratory integrating sphere calibration. Therefore, the field diffuse reflector calibration method is chosen for the radiation calibration of the micro-spectrometer. The main purpose of this article is a preliminary evaluation of the feasibility and accuracy of micro-spectrometer measurements of the R_rs_ of plateau inland water colors. Specifically: (1) we calculated the micro-spectrometer radiometric calibration coefficientsusing different fiber types; (2) we quantified the accuracy of measuring the R_rs_ of clear and turbid waters in plateau inland water bodies and (3) we provided a case study of the application of the micro-spectrometer to R_rs_ of plateau inland water bodies in unmanned areas.
###### Abstract  
Remaining useful lifetime (RUL) predictions of electric motors are of vital importance in the maintenance and reduction of repair costs. Thanks to technological advances associated with Industry 4.0, physical models used for prediction and prognostics have been replaced by data-driven models that do not require specialized staff for feature selection, as the model itself learns what features are important. However, these models are usually trained and tested with the same datasets. That makes it difficult to reuse models with different datasets, so they should be retrained with data from the specific motor being analyzed. This paper presents a novel and robust health prognostics technique that predicts the remaining useful lifetime of the bearings of electric motors under different motor conditions (shaft frequency, load, type of bearing) without retraining or fine-tuning the model used. The model integrates the frequency-domain signal analysis and a stacked autoencoder (SAE) with a bidirectional long short-term memory (BiLSTM) neural network. The proposed model is trained with the IMS-bearing dataset and is then tested with IMS, FEMTO, and XJTU-SY datasets without retraining it, providing accurate results in all of them, and proving its robustness with different electric motors and work conditions.  
health prognostics; remaining useful lifetime prediction; feature fusion; stacked autoencoder; bidirectional long short-term memory +
Footnote †: journal: Applied  
Article  
A Robust Health Prognostics Technique for Failure Diagnosis and the Remaining Useful Lifetime Predictions of Bearings in Electric Motors
## 4 Proposed Approach to Assure Anonymity and Privacy in E-Voting Using Blockchain Technology
### Evaluation of Storage and Energy Consumption  
Various data, such as voter data, electoral zone data, and other comparable data, are processed and stored during the voting process. Depending on the number of voters, the storage size may increase. Data are redundant because the Blockchain is distributed. The redundant data depend on the number of nodes used to mine in the Blockchain. The storage calculation to store the voting records is based on the Blockchain's structure. The organization of data in the block depends on the number of transactions and the platform used. Since, in the current Blockchain, the size of the block is almost 1 MB (megabyte), calculations are based on 1024 bytes (1 kilobytte). According to I BM calculations [52], a 1 MB block must be able to store 1000 votes. Based on the assumptions above, the formula to calculate the needed storage for the voting system is:  
\[storage\_size=(number\_of\_voters/1000)^{*}1\text{ MB}\]  
In the case of 10 million voters, the minimum storage size of one node must be about 10,000 MB or approximately 10 GB (gigabytes). The redundant data are calculated by multiplying the storage_size by the number of nodes performing the mining. Energy consumption should be considered regardless of whether of the two most popular platforms are used, whether the Ethereum platform as a public network or the Hyperledger platform as a limited access or allowed blockchain network. The amount of energy consumed by the blockchain is determined by the block's difficulty and the number of hashes generated per second (called the hash rate) [53]. The total energy consumption is also determined by the total number of nodes, which can range from a few tens to several hundreds depending on the type of election and actors involved, such as ministries, municipalities, civil society,  
Figure 6: Vote transfer, decryption, and results.  
universities, and other important institutions. The assumption of the overall cost of all systems (energy consumption only for transaction mining) was calculated as follows:  
\[\textit{energy\_cost\_per\_day}=(\textit{no\_of\_nodes}\ ^{*}\ \textit{node\_power\_consumption})\ ^{*}\ \textit{prices\_per\_kWh}\ ^{*}\ 24\ \text{h}\]  
A similar approach of calculation is given in [54], which defines the average energy for storing a data unit for one year. However, because electronic voting only takes a few days or weeks, disk size and energy usage may be less relevant.
## 3 Results and Discussion
### Analysis of Corrosion Resistance
Figure 19: Appearance of corroded samples with electrolytic zinc coating (immersion time: 4500 s) after seven degradation cycles (evaluated surface area: 143 \(\times\) 55 mm): (**a**) blasted with synthetic corundum (52.8%), (**b**) blasted with steel grit (46.1%), (**c**) ground (44.7%), and (**d**) reference sample (57.3%). Figure 20: Appearance of corroded samples with hot-dip zinc coating (immersion time: 210 s) after 33 degradation cycles (evaluated surface area: 143 \(\times\) 55 mm): (**a**) blasted with synthetic corundum (47.5%), (**b**) blasted with steel grit (50.3%), (**c**) ground (38.9%), and (**d**) reference sample (42.4%). The corrosion tests, consistent with the mechanical tests, further confirmed that the choice of mechanical pre-treatment technology is crucial for the overall corrosion protection performance of the component. Additionally, the measurable differences in corrosion-mechanical properties among the four tested substrates validate the effectiveness of the technologically distinct mechanical pre-treatment methods.
## Materials and Methods
### Eddy Covariance and Meteorological Measurements  
#### 2.2.1 EC and Meteorological Instrumentation  
The EC measurement system is made up of a three-dimensional sonic anemometer 81,000 V (R. M. Young Company, Traverse City, MI, USA) and an open path infrared gas analyser--IRGA LI-7500 (LI-COR, Inc., Lincoln, NE, USA). Both instruments are operating at a sampling frequency of 20 Hz. Meteorological variables are measured at 30 s intervals and then averaged and recorded as half-hourly values by a CR1000 data-logger (Campbell Scientific, Inc., Logan, UT, USA). Air temperature and humidity are measured with a humidity and temperature probe (HMP45A, Vaisala Oyj, Vantaa, Finland), incoming and outgoing photosynthetic photon flux density--PPFD is measured with a quantum sensor (LI190 SB, LI-COR, Inc., Lincoln, NE, USA), net radiation is measured with a net radiometer (NR Lite, Kipp & Zonen, Delft, The Netherlands), while incoming shortwave radiation is measured with the pyranometer (CMP 3, Kipp & Zonen, Delft, The Netherlands). Soil temperature is measured at three depths (5 cm, 15 cm, and 25 cm) with type T thermocouples. Average soil water content in the 0-30 cm soil layer is measured with two water content reflectometers (CS616, Campbell Scientific, Inc., Logan, UT, USA).  
CO\({}_{2}\) storage term was measured with a profiler system for the CO\({}_{2}\) concentration measurement at six levels: 1, 2, 4, 8, 16, and 23 m (until 30 March 2011), and 27 m (from 1 April 2011). The profiler consisted of six tubes, pump, set of valves, 16 channel relay multiplexer (AM16/32B, Campbell Scientific, Inc., Logan, UT, USA), the CO\({}_{2}\) analyser (SBA4, PP System, Amesbury, MA, USA), and was controlled by the CR1000 data-logger. However, due to occasionally compromised data from the profiler (due to ants invading the filters of the tubes) and a final general failure of the profiler system in 2014, from 2014 until 2017 we decided to compute the storage term using only the CO\({}_{2}\) concentration measured at the top of the tower, as suggested by [40].  
In the cases of gaps in meteorological measurements, we used half-hourly meteorological data (air temperature, global radiation, relative humidity, and precipitation) from a small, auxiliary meteorological station (WatchDog 2900 ET, Spectrum Technologies, Aurora, IL, USA), which was installed within the same forest, approximately 2 km NE from EC tower.
###### Abstract  
This study aims to investigate the influence of the Mezieres method and Isostretching postures on countermovement jump (CMJ) performance in rhythmic gymnasts with low back pain. A total of 17 rhythmic gymnasts with low back pain (LBP) by examining changes in jump height, movement efficiency, and pain perception. A randomized controlled trial (RCT) with a parallel-group design was conducted. Participants were randomly allocated into one of two intervention groups: the Mezieres or the Isostretching group. Both groups underwent a total of 24 sessions. The Baiobit sensor was used as the primary assessment tool for measuring CMJ performance in elite rhythmic gymnasts with low back pain. A total of 17 rhythmic gymnastics athletes with LBP participated in the study. No differences were observed between groups in age, weight, or height; nevertheless, the Isostretching group had greater variability in age (SD = 4.82 vs. 0.91), while the Mezieres group showed higher variability in height. As per maximal speed and height, the overall treatment effect was significant (\(p\) = 0.006, \(\eta^{2}\) = 0.431), indicating long-term benefits for Mezieres training, which also had a significant impact on CMJ Maximal Force, particularly at four sessions (\(p\) = 0.036), with improvements continuing over time (\(p\) = 0.003, \(\eta^{2}\) = 0.672). The Mezieres group showed higher force values compared to the Isostretching group at all time points. The results indicate that Mezieres training significantly improved CMJ Flight Time over time (\(p\) = 0.005, \(\eta^{2}\) = 0.440), with notable increases at all time points compared to the Isostretching group. Improvements were particularly evident in the 12 th and 24 th sessions, confirming its sustained effectiveness. CMJ Contact Time also showed a significant overall effect (\(p\) = 0.027, \(\eta^{2}\) = 0.521), with Mezieres training leading to greater reductions in contact time, particularly at 24 sessions (\(p\) = 0.003), highlighting improved efficiency in jump execution. Lastly, CMJ VAS (perceived exertion or discomfort) showed a significant overall effect (\(p\) < 0.001, \(\eta^{2}\) = 0.896), with Mezieres training leading to a progressive reduction in perceived exertion, particularly from eight sessions onwards, and the most pronounced effect at 24 sessions. The [PERSON] method and Isostretching postures offer substantial benefits for gymnasts with low back pain, though their mechanisms of improvement differ.  
p 2025 15 347 +
Footnote †: journal: Applied sciences
# Path Loss Determination Using Linear and Cubic Regression Inside a Classic Tomato Greenhouse
## 2 Materials and Methods
### Software  
The Contiki operating system has been used in the wireless nodes. The applications were written in programming language C so that the sensor node sends information to the sink node, and it receives it and forwards it to the embedded computer (Raspberry Pi). The sensor node is programmed  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Equation** \\ \hline The modified exponential decay model of & \(\text{L}_{\text{Weiss}}=0.45^{d/2.28d}\), \(0\) m \(<d<14\) m \\ Weissberger & \(\text{L}_{\text{Weiss}}=1.33^{d/2.28d}\)\(0.558\), \(14\) m \(<d<400\) m \\  & The frequency \(f\) in GHz and the depth of the trees, \(d\), in meters. \\  & Applicable at frequencies of 0.23–95 GHz. \\ \hline  & \(\text{L}_{\text{ITU-R}}=0.27^{0.3}\)\(d^{0.6}\), \(d<400\) m. \\ Loss factor of the ITU-R model & The frequency \(f\) in MHz and the depth of the trees, \(d\), in meters. \\  & Applicable to the frequency of 0.2–95 GHz. \\ \hline  & \(\text{L}_{\text{COST252}}=26.6^{d-2.0}\)\(d^{0.5}\)\(out\)-\(leaf\) \\ Loss factor of the COST235 model & \(\text{L}_{\text{COST235}}=15.6^{d-0.009}\)\(d^{0.26}\)\(in\)-\(leaf\) \\  & \(f\) is the transmission frequency (MHz), \(d\) is the depth of the trees \\  & in meters. Applicable to the frequency of 0.2–95 GHz \\ \hline  & \(\text{L}_{\text{ITU-R}}=0.37^{d-0.18}\)\(d^{0.50}\)\(out\)-\(leaf\) \\ FITU-R & \(f\) is the frequency \(\text{MHz}\) and \(d\) is the tree depth in meter, based \\  & on millimeter VHF wave measurement data on a short foliage \\  & depth (maximum of 400 m) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Empirical propagation models.  
Figure 2: Wireless sensor networks (WSN) nodes. (**A**) Sink node connected to the Raspberry Pi. (**B**) Sensor node powered by an external lithium battery.  
to save energy consumption so that its radio module at 2.4 GHz is not active all the time, giving greater durability to the lithium-ion battery while the sink node is constantly fed from the computer. The Raspberry Pi works with the Raspbian operating system, and through a script written in Python, it collects the information from the sink node in its USB port and then stores it in CSV format in its \(\upmu\)SD memory.
# Efficient Route Management Method for Mobile Nodes in 6 TiSCH Network
## 3 Efficient Route Management Method for Mobile Nodes in 6 TiSCH Network
### Mobile--Root Node Fixed-Path Construction Techniques
When the cell negotiation process between nodes E and M is complete, Mobile Node M propagates negotiation completion messages and moving root fixed path routing tables to neighboring nodes in the DIO frame. The DIO is shared with all nodes participating in the network, and the entire node in the network stores the MAC address of the moving-root fixed-path and moving node.
## 1 Introduction
### Background
In [23], authors captured images from an UAV with Parrot Sequoia and assessed canopy reflectance consistency in avocado and banana orchards in Australia, while in [24] reflectance anisotropy of potato canopies in the Netherlands was mapped with a frame camera mounted on an UAV. Although in recent years sensor manufacturers have improved in describing sensor performance and providing tools for performing radiometric corrections [25], the radiometric quality of data is still uncertain. The reliability of spectral information acquired by multispectral sensors mounted on UAVs is not completely clear [26]. Absolute accuracy might be insufficient for some applications, so that calibration procedures will be required [27]. The proposed radiometric calibrations are based on the availability of spectral targets, whose reflectance response is measured in situ with a spectrometer [28; 29; 30; 31; 32; 33]. After evaluating costs, surveying and processing times, and required instrumentations and expertise, this calibration methodology cannot be adopted as a routine method in precision agriculture. Indeed, farmers must take into consideration data quality as well as economic impact [26]. To ensure the dissemination and the use of calibration procedures in the agriculture sector, it is advisable to optimize the exploitation of consumer-friendly tools, and best practices must be simplified.
## 4 Discussion
### Potential of HTC-Sand Filters for Pathogen Removal  
Potable water can be provided either by centralized water treatment and supply systems or by decentralized (point-of-use) technologies that are installed on a house. Decentralized technologies have been recognized as appropriate options for poor rural communities. Biosand filters (BSF) are an example of a point-of-use technology that have been applied in developing countries [41]. Since the formation of a biofilm layer (Schmutzdecke) in a BSF plays a key role in removing microbial agents, insufficient pathogen removal during the startup (ripening) period is one of the main limitations of BSF. Previous research on BSF reported only 60-70% of _E. coli_ removal during the ripening period, i.e., the first 3 weeks of operation [33; 42]. Considering the superior _E. coli_ removal efficiency (96.5%) in the same period, amendments of activated hydrochar to the sand bed would be an attractive option to overcome the inferior performance of BSF during the startup period. Since the experimental conditions employed in this research differed from the general BSF design and operational parameters, extrapolation of the results of this research to supplementation of BSF setups with hydrochar needs to be done carefully. Accordingly, it is recommended to perform more experiments employing (i) smaller sand grain sizes (!0.7 mm), (ii) standing heads for ensuring sufficient oxygen supplementation, and (iii) influents with a higher microbial heterogeneity than that of the AGW used in this study, e.g., surface water from lakes, rivers, or canals [43; 44].  
Though it is reported that hydrothermal treatment decreased the environmental risk of abiotic contaminants, such as heavy metals and pharmaceuticals embedded in sewage sludge [45; 46], the release of these contaminants from hydrochar has not yet been investigated. In case the hydrochar contains considerable amounts of these undesirable compounds, long-term monitoring on the effluent quality is recommended prior to the practical implementation of BSF supplemented with the hydrochar adsorbent. The properties of hydrochar are largely determined by parameters such as reaction temperature, time, and pressure, as well as catalyst and feedstock composition [1]. Further research on the optimization of these parameters can improve the stability of heavy metals in the hydrochar [45], and completely degrade the pharmaceutical residues during the hydrothermal treatment [46].