esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Heat fluxes through the terrestrial surface layer are major drivers of climate. For land areas with sparse or no vegetation, the quantities involved in this energy exchange are fundamentally linked with the moisture in the soil surface. Techniques for monitoring the surface moisture on the spatial scales relevant for climate and meteorological research are therefore of particular interest [1-5].  
Almost 25 years ago, it was suggested that soil moisture could be retrieved from remotely sensed thermal radiance received with an L-band radiometer [6,7]. Today L-band radiometry is one of the most promising approaches for remote soil-moisture retrieval since: (i) the atmosphere and clouds are almost transparent, thus allowing for all-weather measurements; (ii) the impact of vegetation canopies and surface roughness is less distinct compared with passive measurements at higher frequencies and active remote sensing techniques (radar); (iii) solar radiation affects radiometer measurements at the L-band only insignificantly, which allows for measurements at any time of the day; (iv) the1,400\(-\)1,427 MHz frequency band is protected, which means that distortions of thermal radiance due to man-made radio frequency interferences (RFI) are minimized. However, in the past years several field experiments performed in Europe have shown that RFI is present even in the protected part of the L-band.  
During the calibration and validation activities associated with ESA's SMOS mission [8] it turned out that further ground-based passive L-band experiments would be indispensable for the commissioning and the operative phase of the mission. To address this need, the three identical radiometers ELBARA II depicted in Figure 1 were built by Gamma Remote Sensing (Gumligen, Switzerland) as ordered by the ESTEC, in the framework of the contract ESTEC 21013/07/NL/FF\" L-band Radiometer Systems to be deployed for SMOS Cal/Val Purposes\".  
In the following paragraphs, we describe some basics of microwave radiometry, the requirements of the SMOS mission, and the corresponding research activities. The design and the main characteristics of the ELBARA II instruments are described in Sections 2 and 3. The Appendix contains a list of the abbreviations used and the specifications of the electronic components used in the radiometer design.
# Development of a Drone's Vibration, Shock, and Atmospheric Profiles
## 2 Materials and Methods  
### Drones and the Data Logger  
The DJI Matrice 600 Pro and DJI Matrice 100 (SZ DJI Technology Co., Ltd., Shenzhen, Guangdong, China) were the two drones used for this research study. These drones were selected as they have similar payload capacity with multi-rotor configurations as compared to other drones currently being explored in the area of parcel delivery. Their specifications and capacities are shown in Table 1. Figure 2 illustrates the different drones and the locations in which the data loggers were rigidly mounted.  
Pak-Track(tm) data loggers were utilized to capture triaxial vibration and shock, temperature, humidity, light, and pressure conditions (Rochester Institute of Technology, Rochester, NY, USA). Each drone was outfitted with two Pak-Track(tm) data loggers to understand and correlate the differences between the rigid chassis and potential compartment where the payload could be stored for transport. For the DJI Matrice 600 Pro, one data logger was rigidly mounted to the top platform, while the other was mounted above the frame of the payload. The DJI Matrice 100 had one data logger rigidly mounted to the top platform, and the other data logger was placed on the quadcopter frame. The orientation of the mounted sensors was such that the \(X\)-axis was the longitudinal (forward) direction of travel, \(Y\)-axis was the lateral direction of travel, and \(Z\)-axis was the vertical direction. The signal trigger sampling method was used for collecting all field data, meaning the data were recorded during an event when the data intensity exceeded a preset threshold. The signal trigger threshold levels are displayed in Table 2 and were selected to ensure only events of interest were collected. The vibration sampling frequency was set to 1600 Hz, and each sampled vibration period was determined to be 1.28 s for 2048 data points [20,21]. The flight time for the DJI Matrice 600 was approximately 60 min. The flight time for the DJI Matrice 100 was 10 min due to the limited flight capacity of the drone being 25 min of potential flight time.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Name** & **DJI Matrice 600 Pro** & **DJI Matrice 100** \\ \hline Number of rotors & 6 & 4 \\ Drone Weight & 22.05 lbs. (10 kg) & 5.35 lbs. (2.43 kg) \\ Payload weight & 34.17 lbs. (15.5 kg) & 7.93 lbs. (3.6 kg) \\ Propellers & 2 Blade & 2 Blade \\ Motor model & DJI 6010 & DJI 3510 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of the DJI Matrice drones.  
Figure 1: Air vibration PSD profile comparison.
# Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach
## 3 Methodology
### Proposed Model Design
#### 3.1.3 Novel Hybrid EEMD-Based LSTM Model  
The proposed novel hybrid EEMD-based LSTM forecasting model is depicted in Figure 2. With the proposed new model, the real DO concentration data set is first decomposed by EEMD into several components to improve the forecast accuracy. The detailed procedures illustrated in Figure 2 show the three crucial steps that lead to the development of the new hybrid EEMD-based LSTM forecasting model. In the first step, DO time series data \(x(t)\) are decomposed into several IMFs and a residual item \(R_{N}(t)\) by EEMD algorithm. The data set decomposition is performed through an iterative sifting process, which is expressed as  
\[x(t)=\sum_{i=1}^{N}IMF_{i}(t)+R_{N}(t). \tag{13}\]  
Figure 2: Hybrid ensemble empirical mode decomposition (EEMD)-based LSTM model architecture.  
In the second step, each IMF and residual item is normalized and used for forecasting by the LSTM NN. Finally, reverse normalization of individual forecasting results of the LSTM NN is carried out prior to combining all of them together through summation operation to get the final forecasted values, as illustrated in Figure 2. When the final forecasted result is obtained, performance evaluation of the proposed novel hybrid EEMD-based LSTM forecasting model is carried out through the application of three statistical metrics such as mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE).
## 4 Fairness-Aware Machine Learning Based Power and RB Allocation with Multiple Relays  
In practical scenarios, multiple relays are deployed to facilitate the cell-edge users as shown in the Figure 7. The multiple relay deployment causes inter-relay interference. This interference can be minimized by the careful deployment of relays, transmit power control, and the scheduling of time/frequency resources. Though, L3 relays incur more processing delay as compared to the L1 and L2 relays but they provide robust transmission in the presence of interference [26]. Assume there are \(Q\) relays in a cell, such that relay \(q\in\mathcal{Q}=\{1,...,Q\}\). The signal-to-interference-and-noise ratio (SINR) at UE \(k\) in direct link is given as  
\[SINR^{Direct\_link}_{k,n}=\frac{p^{BS}_{k,n}\delta^{Direct\_link}_{k,n}}{\sum _{q\in\mathcal{Q}}p^{q}_{k^{\prime},n}\delta^{q}_{k,n}+N_{0}\frac{B}{N}} \tag{20}\]  
where \(p^{q}_{k^{\prime},n}\) is the transmit power of relay \(q\) assigned to its associated user \(k^{\prime}\) and \(g^{q}_{k,n}\) is the channel gain between relay \(q\) and the UE \(k\). Similarly, the SINR at UE \(k\) in relay \(q\) link is given as  
\[SINR^{q}_{k,n}=\frac{p^{q}_{k,n}\delta^{q}_{k,n}}{\sum_{q^{\prime}\in\mathcal{ Q}-\{q\}}p^{q^{\prime}}_{k^{\prime},n}\delta^{q^{\prime}}_{k,n}+p^{BS}_{k,n} \delta^{Direct\_link}_{k,n}+N_{0}\frac{B}{N}} \tag{21}\]  
As seen from the above equation, the interference and fairness causes a significant increase in the computational cost when deploying multiple relays. Therefore, we present a machine learning based approach that utilizes relay deployment and users' association data to develop RB allocation and Power allocation strategy that maximizes the sum EE. Once trained, the proposed approach can save cost of scheduling in every TTI. This is shown in Figure 8, the machine learning model takes the inputs: number of relays, relays' coordinates, CSI, SNR, and total transmit power and produces the outputs: optimal relays' coordinates with associated users, set of RBs assigned to each user \(k\), and the optimal power allocation (\(p^{*}_{k,n}\)) to each user \(k\) in the RB \(n\). Based on single relay performance, the RB allocation block is trained using SAMM and power allocation block is trained using BOPA. Since the relay deployment can significantly alter the RB and power allocation, a clustering approach is presented that determines relay positioning and corresponding users' association based on a pre-defined metric.  
Figure 6: The system energy efficiency with and without BOPA based power allocation.
# Experimental Study of Entrainment and Mixing of Renewable Active Particles in Fluidized Beds
## Materials and Methods
### Particle Entrainment Measurements  
Figure 3 shows the schematic view of the lab-scale test rig used in the particle entrainment experiments. The test rig consists of a riser from plexiglass and has an internal diameter of 192 mm. Here, only the SRF, bark, sunflower shell, and wheat shell particles were investigated (used and put into the facility), which are otherwise the active particles. The air volume flow was measured with an inlet orifice connected to a manometer. A radial blower was used to produce the desired airflow through the air distributor, which consists of 1160 nozzles of 1 mm diameter each. At the outlet of the riser, a separator is used to detach the entrained particles from the airflow.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **SRF** & **Bark** & **Sunflower shell** & **Wheat shell** \\ \hline Skeleton density, kg/m\({}^{3}\) & 1400 & 1253 & 1876 & 1446 \\ \hline Apparent density, kg/m\({}^{3}\) & 325 & 600 & 338 & 415 \\ \hline Particle shape, – & diverse irregular shapes & stringy and chips-like & half-ellipsoidal shell-like & ellipsoidal shell-like \\ \hline Particle structure, – & ductile (big-sizes), hard (small-sizes) & ductile (stringy), hard (chips) & hard & elastic \\ \hline Source, – & waste (textile, plastic, etc) & wood industry & sunflower seeds & wheat crop \\ \hline \hline \end{tabular}
\end{table}
Table 3: Physical and geometric characteristics of the active particles used in the present experiments.  
Figure 2: The setup of the particle size analysis.  
The measurement method consists of the following steps:  
* Filling the riser with about 50 g of the investigated particles.
* Increasing the fluidization air velocity.
* Measuring the entrained fuel amount in the filter at five fluidization velocities within the range of 1-3 m/s.
* Plotting the entrainment curve between the normalized entrained mass and the fluidization velocity.
## 1 Introduction  
The first ruby laser was reported by [PERSON] in 1960 [1], and researchers were quick to propose exciting new applications, especially in space. This was largely due to the fact that, for a given initial beam diameter, infrared and visible light diverges orders of magnitude less rapidly than RF or microwave frequencies, allowing much more energy to impinge on the target. In 1964, Dr. [PERSON] and his team at NASA Goddard Space Flight Center in Greenbelt, MD reported the first successful laser ranging to an artificial satellite equipped with retroreflectors, which reflected a portion of the incoming laser light back to its source [2]. Since that time, dozens of artificial satellites have been tracked to support a number of scientific and engineering programs. In 1998, the International Laser Ranging Service (ILRS) was created to better coordinate international tracking, orbit determination, and scientific processing of the data [3]. Figure 1 shows the current global distribution of satellite laser ranging (SLR) stations. The largest concentration of stations can be found in Europe and East Asia. As will be discussed later, these existing stations can likely be used by researchers to test future deep space laser transponder and communications systems.  
As early as 1969, following the placement of a retroreflector array on the Moon by the Apollo 11 astronauts, a team of scientists successfully measured the range to the Moon with centimeter accuracy [4]. Today, a total of five retroreflector arrays reside on the lunar surface, i.e., three installed by Apollo astronauts and two French retroreflector arrays attached to Soviet Lunakhod landers. However, since the optical losses for the roundtrip to a passive reflector vary as R\({}^{-4}\) (where R is the range), for decades, only three large meter-class telescopes, located, respectively, on mountaintops in southern France and NASA-supported sites at the University of Texas and the University of Hawaii, were capable of detecting return signals. Subsequently, a large telescope in Matera, Italy was added to the roster, but the larger 3.5 m telescope at Apache Point Observatory in Sunspot, New Mexico, USA made a huge difference in data yield as well as providing unprecedented ranging precision at the few millimeter level [5].
## 4 Discussion
### Research Findings  
Summarising the results of the analysis of the selected documents, several real-time monitoring technologies can be used to improve occupational safety and health in construction.  
* Real-time monitoring technologies are important for understanding the location of workers and selecting the most accurate technology, depending on the type of site (indoor or outdoor). In their study, [PERSON] et al. [76] presented the main indoor positioning technologies that are very promising and can enable a proactive approach to construction safety.
* The integration of different monitoring technologies can be beneficial, as it allows for filling the gaps that exist separately in each of the technologies [73; 78]. However, this is an issue that is not addressed in the selected studies, so future research directions should focus on this issue.
* BIM is a tool that, as we already know, can contribute to safety management at all stages of a building's life. Some studies have analysed the possibility of integrating BIM with different types of tools for multifunctional applications. In fact, some studies aim to merge BIM with other technologies such as sensors, GPS, virtual reality tools, etc., specifically to develop proactive solutions. This is an aspect that represents a promising research trend to further develop and achieve an integrated Building Safety 4.0 environment [24].
## 4 Discussion
It raises a question about the effectiveness of the method, especially given the temporal nature of the data involved in this study. Consequently, our choice is to push the use of k-means during unsupervised learning because the validation accuracy reached 0.66. Even if this value was lower than the one observed for calibration, a 66% accuracy on a large scale is deemed satisfactory when examining the literature. For instance, in [18], continuous validation information resulted in the correct detection of 63% of moving events. Higher detection rates, up to 86%, have been reported [17] using change detection in the time series of S1 and S2 data. However, it is important to note in this case that the precision of this model dropped to 57% when considering all grassland types; our study also considered all grassland types but considered them as pasture. Challenges in identifying moving events in this study may stem from the mixed practices of farmers, as evidenced in [18], where half of the false positives were detected in pastures that were also grazed. Another potential factor affecting model performance is intense grazing over a short period, identified as a significant confounding factor in [17]. Now, we will examine the potential significance of the developed predictive models. The aim of this study was to create a model for identifying mown or grazed parcels using routinely recorded data (S1, S2, and meteorological data). This model serves the purpose of verifying compliance with label specifications such as \"Happy Marguerite cow\" in Belgium or \"Luit de paturage\" in France, ensuring that cows are genuinely on pastures for a specific duration rather than being fed with mown grass [15]. While the 66% validation performance achieved might seem a bit low, it is important to note that the validation set consisted of only 68 records. Enhancing the performance of this potential labeling check could involve supplementing the obtained probability with information about milk composition. It is well established that milk composition, particularly the fatty acid profile, is influenced by diet. This method opens up another potential application: estimating damages, such as those resulting from droughts. Utilizing a meticulously assessed small reference dataset, the study suggests that mowing events may diminish grassland resistance to drought [53]. For instance, mowing can limit the water uptake capacity of plants by directly reducing their evapotranspiration [54]. This application is of particular significance in Wallonia, where the extensive time required for drought damage assessments often results in a standardized rate assigned to the area without a precise evaluation of farmers' losses. Implementing this approach could offer advantages for both insurers and farmers, ensuring more accurate financial compensation that aligns with the actual losses incurred.
## 2 Numerical Analysis
### Variation of Inflow Length Scale  
In this study, the flow characteristics were observed with variations in the integral length scales at the inlet section. As mentioned earlier, the streamwise local length scale, \(L_{x}(y)\), is determined by the local mean velocity, \(\overline{u}(y)\), and the constant time scale, \(T\), where the constant \(T\) could be obtained by \(L_{x}\) and \(\overline{u}\) at \(y/d=1\). The spanwise length scale could be applied as a multiple of \(L_{z}^{*}\), where \(L_{z}^{*}\) is the reference spanwise length scale, which is equivalent to the value \(L_{z}\) used in Figure 2. To observe the effect of the \(L_{z}\) distribution on the flow field, this study chose several different values, such as \(0L_{z}^{*}\) (white noise), \(0.15L_{z}^{*}\), \(0.3L_{z}^{*}\), \(0.5L_{z}^{*}\), \(2L_{z}^{*}\), and \(3L_{z}^{*}\) (see Table 1). To achieve a precise matching with the \(L_{z}^{*}\) distribution of the existing DNS database, in the length scales \(L_{z}^{*}\), the domain needed to be divided into three different zones. These zones were separated in several salient positions at \(y/d=0.1,0.2\). Table 1 presents the configuration of each case depending on the size of the length scale in each zone. For instance, Case 02 did not consider the length scale in the inlet condition, whereas Cases 01, 07, and 08 had a uniform spanwise length scale in \(L_{z}\). Besides, to observe the effect of different length scales on different zones, Cases 03, 04, 05, and 06 would play an important role, in addition to studying the variation of flow statistics based on length scales. Depending on the location of the length scale, the zone was divided at \(y/d=0.1\) and 0.2. In particular, Cases 07 and 08 had length scales that were two and three times larger, respectively, than that of Case 01 for all the streamwise, vertical, and spanwise directions.
# Portable Sensor System for Registration, Processing and Mathematical Analysis of PPG Signals
## 3 Results  
### Experimental Results Obtained with a Portable Cardiology Device  
An experimental multisensory system for simultaneous real-time recording of ECG and PPG signals of a healthy individual with a 30-min. measurement time has been created in order to prove the validity of the created portable PPG signaling device. The experimental system consists of:  
* ECG sensor for ECG signal recording;
* Three photoplethysmographic sensors for a recording of three photoplethysmographic signals received from different places of the body. The sensors are placed by pinching the middle finger of the left hand, the earring of the left ear, and the index finger of the left hand touches the sensor that is built into the device.  
The simultaneous registration of both types of signals (ECG and PPG) enables comparative analysis by modeling RR and PP time intervals. Figure 4 shows diagrams of the signals registered with the device:  
* ECG signal;
* PPG\({}_{1}\) is the photoplethysmographic signal recorded by the sensor placed on the left ear;
* PPG\({}_{2}\) is the photoplethysmographic signal recorded by the sensor placed on the middle finger of the left hand; and,
* PPG\({}_{3}\) is the photoplethysmographic signal registered by touching the left hand index finger to the sensor integrated into the portable device.  
The registrated signals were obtained at a sampling rate of 128 Hz. ECG records were obtained while using the MAX30001 EVSYS system from Maxim Integrated Inc [64]. We use the original software of the system, adding the ability to synchronize with our developed system for the registration of PPG signals, ie. the reading of ECG and PPG data takes place at the same time.  
Table 3 shows the results of the study of 30 min. recorded ECG and PPG signals, such as: number RR/PP intervals; average intervals; min. value; max. value; and, MSE.  
Figure 4: Diagrams of registered electrocardiographic (ECG) and PPG signals.  
Figure 5 shows the graphs of the four tested signals (ECG (Figure 5a)), PPG-ear (Figure 5b)), PPG-middle finger (Figure 5c), and PPG-index finger (Figure 5d)), as obtained by the Poincare graphical nonlinear method.
## 2 Materials and Methods
### Materials and Analyses  
Structural steel of grade S355J2+N as specified by EN 10025-2 was used as base plate material, because it is readily available carbon steel. The major element analysis of the weld wire presented in Table 1 is the supplier's specification sheet analysis (Afrox Ltd., Johannesburg, South Africa). The base-plate steel was analysed by optical emission spectroscopy (OES), SPECTRO Analytical Instruments GmbH, model SpectroMax XF, 2018. The total oxygen content in the base plate and weld wire was analysed by combustion method. Table 1 summarises the weld wire and base plate analyses.  
Cross-section samples of the welded plate were cut from the middle position of the weld run. One of these volume samples was analysed for major elements by inductively coupled plasma optical emission spectroscopy (ICP-OES), Thermo Fisher Scientific, Waltham, MA, USA, model iCAP 6000 series, 2008. The second volume sample was used for total oxygen and carbon analysis, by combustion method. The third weld metal cross-section sample was analysed by SEM (scanning electron microscope) after polishing to a mirror finish and etching with 2% Nital solution to reveal the weld metal boundary on the base plate. The SEM equipment assembly consisted of a Zeiss (Oberkochen, Germany) crossbeam 540 FEG (field emission gun) SEM with EDS (energy dispersive spectrometer), Oxford Aztec system with X-Max 80 detector (2015).  
A commercial agglomerated flux was selected for use in the welding tests following initial testing of different fluxes [30]. The main selection consideration for the flux was the thermodynamic stability of the flux constituents in the presence of aluminium metal. This flux can be classified as an aluminate basic flux (Basicity Index (BI) = 1.4) and its chemistry and mineralogy have been extensively investigated previously [30]. It has a 0.2-1.6 mm agglomerate particle size. The bulk chemistry of the flux is shown in Table 2. Metal powders of pure Al (99.7% Al and--1 mm) and pure Co (99.9% Co and--0.15 mm) were sourced from Sigma-Aldrich.