esa-sceva/satcom-chunk-collection huggingface dataset:
# Agriculture 5.0 in Colombia: Opportunities Through the Emerging 6G Network
## 4 Results and Discussion
### Current Colombian Agricultural Outlook
#### 4.3.5 Autonomous Aerial Systems for Smallholder Farming
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Features** & **Existing or Potential Applications** & **Limitations and Challenges** \\ \hline Space–Air–Ground Integrated Networks & Integration of data collected from drones, satellites, and multispectral imaging. & Yield prediction using drones and multispectral cameras; crop mapping for irrigation and fertilization. & Rural connectivity limitations and high technological infrastructure costs. \\ \hline Terahertz Technology (analog: advanced sensors and NIRS spectroscopy) & High sensitivity and fast analysis. & Quality analysis of milk and forage using NIRS; remote sensors such as EM38 and NDVI & Limited access to specialized equipment in rural areas and need for technical training. \\ \hline Reconfigurable Intelligent Surfaces (RIS) & Smart reconfiguration of the wireless propagation environment (analog: IoT with microsensors). & Microsensors for IoT in precision agriculture; real-time environmental monitoring. & Not yet industrialized; requires field validation. \\ \hline Wireless AI & Continuous learning and automatic model adjustment. & AI system for fertilization plan prediction across more than 100 crops; human–machine collaboration improves predictions. & Still limited to specific applications; more integration with communication networks needed. \\ \hline Integrated Sensing and Communication (BAC) & Simultaneous sensing and data transmission from the field. & Monitoring platforms for decision-making in tomato production, pest control, and price prediction. & Lack of standardization; adverse environmental conditions may impact accuracy. \\ \hline Massive MIMO (analog: multisource and multisensor platforms) & High spectral efficiency and wide coverage with multiple access points. & Multisource platforms like AlimentTo, with over 1.8 million analyses and real-time data for livestock nutrition. & Technical complexity in integrating multiple sensors and data sources; interoperability challenges. \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Sixth-generation technologies in agriculture in Colombia. Source: [18].
###### Abstract  
The corrosion of prestressing steel in prestressed concrete bridges is a critical safety issue. To evaluate the strength of a prestressed concrete beam with corroded strands, it is necessary to know the mechanical properties of the corroded strands in terms of their tensile strength and ductility. In this study, material models were suggested using tensile tests of corroded strands which had been taken from existing bridges. Five prestressed concrete beams with multiple internal corroded strands of different corrosion levels and locations were fabricated and tested using the three-point bending test. The beams with corroded strands near the support did not show meaningful flexural behavior changes, while the beams with corrosion in the mid-span showed significant strength reduction. In order to suggest the appropriate evaluation of the flexural strength of a prestressed concrete beam with corroded strands, material models of the corroded strands were divided into two model categories: a bi-linear material model and a brittle material model. Strength evaluations of the corroded prestressed concrete beams according to \(f_{\text{Ps}}\) approximation and strain-compatibility using OpenSEES were conducted. Results suggested the use of the strain compatibility method only when the section loss was greater than 5%.  
**Keywords: corrosion; prestressed concrete beam; internal strand; section loss; strength; ductility**
# Convolutional Sparse Modular Fusion Algorithm for Non-Rigid Registration of Visible-Infrared Images
## 4 Modular Training Strategy and Loss Functions
### Registration Stage Loss Functions  
When the input images are unregistered, the self-supervised registration loss is proposed to align \(I^{x}\) and \(I^{y}\). Based on the previous hypothesis, the multimodal image registration can be approximated as a single-modal image registration by utilizing the visible homologous feature image \(I_{h}^{x}\) and the unregistered infrared homologous feature image \(I_{h}^{y}\) as references. The self-supervised registration loss is defined as follows:  
\[L_{SR}=\frac{1}{MI(I_{h}^{x},I_{h}^{y})+\epsilon}, \tag{21}\]  
where \(MI(X,Y)\) is the mutual information [23] operator. During the training process, the objective is to maximize the mutual information \(MI(f_{h}^{x},f_{h}^{y})\), which ensures optimal registration between \(I_{h}^{x}\) and \(I_{h}^{y}\). Given that the feature encoding and decoding processes do not alter the pixel positions of the images, the same registration procedure can be directly applied to the source images, thereby achieving precise registration between \(I^{x}\) and \(I^{y}\). Here, \(\epsilon^{\prime}=0.01\) is used to prevent division by zero.  
In the generated deformation dataset training, the fully supervised prediction loss is proposed to ensure that the predicted deformation matrix \(\hat{H}\) closely matches the actual deformation matrix \(H\). Since the non-rigid registration is primarily employed for correcting minor-scale image distortions, the term associated with the non-rigid deformation field \(S\) becomes negligible after hyperparameter optimization. Consequently, the fully supervised prediction loss exclusively incorporates the terms related to the rigid transformation matrix \(H\). The deformation matrix fully supervised prediction loss is defined as follows:  
\[L_{FP}=\|H-\hat{H}\|_{2}^{2}. \tag{22}\]  
The total loss function for the registration stage, combining the self-supervised registration loss and fully supervised prediction loss, is defined as follows:  
\[L_{RE}=L_{SR}+\mu_{4}L_{FP}, \tag{23}\]  
where \(\mu_{4}\) is a tuning parameter.
## 3 Results and Discussion  
### Ozone Deposition Velocities  
#### 3.1.1 On Wood-Based Materials  
Ozone deposition on the specimens comprising three types of wood-based panels was conducted with 100 ppb and 300 ppb of ozone. The ozone deposition velocities on the materials \(v_{\mathrm{d,s}}\), were calculated using Equations (1)-(3), as shown in Figure 3.  
It can be observed from Figure 3 that, among the selected materials, the deposition velocities were the highest on the green-core OSB and the lowest on the paint-free pine. When the inlet ozone concentration, \(y_{\mathrm{ozone,in}}\), was controlled at 100 ppb, it took only 30 min for ozone deposition to reach a steady state on the pine, while approximately 2.5 h was needed for ozone deposition to reach a steady state on the OSB. The outlet concentrations of ozone, \(y_{\mathrm{ozone}}\), which were assumed to be equal to the concentration of well-mixed ozone in the chamber, were the lowest when testing with OSB and the highest with pine. This is reasonable since both OSB and plywood are engineered wood types manufactured by binding or fixing strands or veneers of wood together using adhesives or other methods of fixation. Compounds that may be reactive to ozone were far more densely present on or in the OSB and plywood than on or in the pine, which, in this case, was the paint-free pine. By comparison, when \(y_{\mathrm{ozone,in}}\) was at 300 ppb, a similar trend was observed between the three specimens of the panels. It took approximately 1 h for ozone deposition to reach a steady state on both the OSB and the plywood. However, it nearly took 6 h for the outlet concentration of ozone to reach a steady state when a piece of pine was in the chamber. Moreover, when elevating \(y_{\mathrm{ozone,in}}\), the \(v_{\mathrm{d,s}}\) values decreased for both the plywood and pine but increased for OSB.
###### Abstract  
Density is a crucial parameter for quantitatively describing the physical properties of liquids. It serves as an important indicator for scientific research, production process control, pipeline transportation, and other aspects. In oil pipeline transportation and raw material processing, the real-time online measurement of liquid density is of great significance. This paper analyzes the working principle of an online vibrating tube densitometer and derives the fitting equation for temperature, pressure, and density; it also conducts experiments with an online vibrating tube liquid densitometer and establishes a traceability chain for the experimental device. The experimental setup includes a desktop densitometer system, a multi-temperature field constant-temperature stirring system, a walk-in constant-temperature box, an automatic blowing system, and a frequency acquisition and calculation system. The uncertainty of the device's evaluation is \(U=0.08\) kg/m\({}^{3}\), \(k=2\). We built a set of pressure-density static test systems, statically testing the online vibrating tube's liquid-density meter vibration frequency at different pressures; the whole set of systems can be used to assess the specific density, temperature, and pressure range of online vibrating tube liquid density meters in the experimental research to derive the standard temperature. Through the experimental research, we can accurately derive the fitting coefficients under the standard temperature, specific temperature, and pressure of online vibrating tube liquid densitometers, and calculate the fitting error of online vibrating tube liquid densitometers under different temperatures and pressures within the experimental range through fitting equations and coefficients, so as to realize the practical application of online vibrating tube liquid densitometers in engineering by utilizing straight-tube-type and curved-type online vibrating tube densitometers. A preliminary study was conducted on the effects of different densities, temperatures, and pressures on the vibrating tube system's vibration cycle. The fit coefficient and error were calculated, and the experimental results were compared to the theoretical analysis to confirm the device's conformity. The study verified the device's scientific and reasonable design, and demonstrated that it is feasible to use the device for follow-up research. Using this device in subsequent experiments can verify the effects of viscosity, inlet, installation, and other factors on the online vibrating tube liquid densitometer's metrological performance. Further experimental research on the pressure-frequency-density test system and the establishment of a wide range of temperatures and pressures within the pressure standard density test system are needed to achieve a wide range of temperatures and pressures under the standard density test.  
online vibrating tube liquid densitometer; theoretical analysis; experimental setup; temperature; pressure +
Footnote †: journal: Applied Sciences
###### Abstract
This means that the inside of the exposed material is quicker to heat, with lower temperature gradients between the surface and the core--a process referred to as \"volumetric heating\" [20]. MW heating is much quicker and more energy-efficient [21]. Furthermore, heat transfer can be stopped immediately by turning off the power [22]. MW heating of a medium via a wave-guide eliminates energy waste and problematic fouling/blockage of heat exchangers [23]. Microwaves can also be focused and directed as needed [24]. Many literature reports have noted increased enzymatic activity in systems heated with MW. This is directly attributable to the nonthermal effects of microwave radiation, which induces the microbial community to develop within specific lines [25]. Therefore, there is real basis for examining the applicability of MW as a heat source for anaerobic digestion. So far, little attention has been given to research on the use of EMR to stimulate thermal conditions in fully stirred fermentation reactors (ACSTRs). These were only preliminary and quite limited studies on the possibility of methane fermentation of expired food products, focusing only on the production efficiency and qualitative composition of biogas in mesophilic conditions [21]. The presented research is the first such comprehensive and multivariant research, where the type of heating used and thermal conditions (mesophilic, thermophilic) for biogas production were taken into account. They also assessed the impact of these elements on the biodegradation of organic pollutants and the removal of biogenic compounds, and the taxonomic characteristics of the population of anaerobic bacteria. The aim of this study was to assess the influence of electromagnetic microwave radiation used as a heat source on impacts to anaerobic processing of high-load poultry wastewater, and to compare the performance of the MW-heated system against a conventionally heated one. The investigation also includes studying the bacterial communities with PCR-DGGE.
## 5 Conclusions  
In this study, we improved the disturbance-attenuation performance of an FSM control system by using an IMP-based controller with DOB to improve the control performance.  
When the IMP-based controller with DOB (\(Q_{20}\) filter 15 Hz) was used at 60, 65, and 71 Hz, the CES values decreased by approximately 12.67, 15.3, and 28.1%, respectively, compared to those when using the IMP-based controller alone. When the IMP-based controller with DOB (\(Q_{20}\) filter 30 Hz) was used at 60, 65, and 71 Hz, the CES values decreased by approximately 15.1, 17.5, and 30.9%, respectively, compared to those when using the IMP-based controller alone. When the IMP-based controller with DOB (\(Q_{30}\) filter 15 Hz) was used at 60, 65, and 71 Hz, the CES values decreased by approximately 18.5, 21.1, and 32.8%, respectively, compared to those when using the IMP-based controller alone. When the IMP-based controller alone. When the IMP-based controller with DOB (\(Q_{30}\) filter 30 Hz) was used at 60, 65, and 71 Hz, the CES values decreased by approximately 22.4, 24.1, and 34.2%, respectively, compared to those when using the IMP-based controller alone.  
Furthermore, to improve the disturbance-attenuation performance of the FSM control system by using an IMP-based controller with DOB, the IMP-based controller was set to have multiple frequency components. As summarized in Table 3, when we used the IMP-based controller with DOB, the CES values were lower than those obtained when using the IMP-based controller alone. These results indicated that the performance of the proposed controller was superior to that observed in a previous study of FSM control systems.  
Figure 17: Experimental results of FSM control system when using IMP with DOB: (**a**) \(Q_{20}\) (15 Hz), (**b**) \(Q_{20}\) (30 Hz), (**c**) \(Q_{30}\) (15 Hz), and (**d**) \(Q_{30}\) (30 Hz).
## 4 Discussion
This latter study also showed that for women a significant lower fat mass was only apparent in the legs. A possible explanation of the disparity in results for fat mass in the arm according to sex may be related to the use of different BIA and DXA devices. More specifically, [PERSON] et al. [28] and [PERSON] [23] used a single-frequency BIA (i.e., 50 kHz) whereas more recent devices such as the Inbody S10 apply six frequencies for estimating bodycomposition. These recent devices allow more accurate body composition estimates (i.e., particularly regarding intra- and extra cellular fluids) [29]. The present study is novel in its use of more recently developed body composition assessment devices that also enable the analysis of segmental body composition. To the best of our knowledge, our study is the first to compare both specific BIA and DXA assessment devices in young healthy adults. Nevertheless, our study also contains some limitations. First, this study was exclusively conducted with Caucasian healthy young adults aged between 18 and 24 years. Therefore, the findings are age and population specific and could not be generalized across other age and ethnic groups, due to body composition variability observed between ethnicities [30]. Second, the participants' actual hydration statuses were not assessed in the current study because of practical reasons. Although the participants followed a strict (pre-)test protocol, such as no alcohol consumption 24 h prior to testing and voiding their bladder, their actual hydration status may have impacted the BIA estimations. Third, our study results might have been influenced by external factors, such as the ambient temperature, humidity and circadian rhythm (i.e., individual measurements were carried out between 8:45 and 16:15), which were not taken into account. A last consideration is that, due to time constraints related to the DXA research scans, only the body composition of the right arm and leg segments were measured in this study. Therefore, we were not able to identify the agreement between BIA and DXA for monitoring bodily asymmetries.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 2 Theoretical Background of TD-NIRS
### Radiative Transfer Equation (RTE)  
We start from the radiance, or the specific intensity, \(I(\mathbf{r},\hat{s},t)\), defined as the average radiant power flowing at position \(\mathbf{r}\) and at time \(t\) through the unit area oriented in the direction of the unit vector \(\hat{s}\) and through the unit solid angle along \(\hat{s}\) in a medium as shown in Figure 1. The most fundamental equation describing light propagation in biological tissue, which is accepted in this field, is the radiative transfer equation in time-domain (TD-RTE) (or the Boltzmann transport equation) for radiance [5],  
\[\frac{\partial}{c\partial t}I(\mathbf{r},\hat{s},t)+\hat{s}\cdot\
abla I( \mathbf{r},\hat{s},t)+[\mu_{a}(\mathbf{r})+\mu_{s}(\mathbf{r})]I(\mathbf{r}, \hat{s},t)=\mu_{s}(\mathbf{r})\int_{4\pi}p(\hat{s},\hat{s}t)I(r,\hat{s}t,t)d \Omega+q(\mathbf{r},\hat{s},t) \tag{1}\]  
where \(c\) is the velocity of light in the medium, \(\
abla\) is the spatial gradient operator, \(\bullet\) is the scalar product operator, \(\mu_{a}(\mathbf{r})\) and \(\mu_{s}(\mathbf{r})\) are the absorption and scattering coefficients, respectively, \(p(\hat{s},\hat{s}t)\) is the scattering phase (angular) function describing the probability of scattering from direction \(\hat{s}\) into direction \(\hat{s}\), \(d\Omega^{\prime}\) is the solid angle for integration, and \(q(\mathbf{r},\hat{s},t)\) is the light source. Here we assume that the radiance is for a specific wavelength and that the velocity of light is constant throughout the medium. The RTE is an energy conservation equation, and each term has physical meanings; the total temporal change in the radiance, the energy inflow due to the gradient of the radiance (or the diffusion of the radiance), the energy gain by absorption and scattering, the energy inflow to direction \(\hat{s}\) by scattering from direction \(\hat{s}^{\prime}\) over the entire solid angle, and the energy gain by light sources. Here we note: if the radiance, \(I(\mathbf{r},\hat{s},t)\), having a dimension of W/(m\({}^{2}\)sr) is divided by the velocity of light, \(c\)it has a dimension of \(J/(m^{3}sr)\) and is often called as the photon energy density, \(u(\mathbf{r},\hat{s},t)=I(\mathbf{r},\hat{s},t)/c\). Equation (1) is sometimes expressed as \(u(\mathbf{r},\hat{s},t)\) instead of \(I(\mathbf{r},\hat{s},t)\).  
The RTE is an integro-differential equation and it is not easy to calculate even with the use of modern computers due to the integral term on the right-hand side. Many studies have been carried out on how to solve the RTE by numerical and analytical methods, and by equivalently statistical methods. Analytical and statistical methods are briefly reviewed in the following, while numerical methods are briefly reviewed in a later section.
## 2 Materials  
### Study Sites and Meteorological Data  
This study examined two irrigation sites in Occitanie, Southern France. The first site, an experimental field, was situated in the northern part of Montpellier in the southeast of France. The second site was near Tarbes in the southwest (Figure 1). Montpellier experiences a Mediterranean climate with a warm and temperate environment, averaging an annual temperature of 15.0 \({}^{\circ}\)C. The region receives an average annual precipitation of 629 mm, primarily during the winter season.  
Figure 1: Location of the study sites in Occitanie, France (outlined in red): Montpellier in 2017 (P1) and Tarbes in 2018 (P2), 2019 (P3), and 2020 (P4). The figure displays the daily average air temperature (Tavg in \({}^{\circ}\)C), Penman–Monteith reference evapotranspiration (ETo in mm), and rainfall (R in mm) for each maize plot throughout the crop growing cycle. DAS refers to days after seedling.  
In contrast, Tarbes has a humid oceanic climate with a higher annual precipitation and a summer season that is more humid than in Montpellier. Despite the climatic differences, irrigation is applied during the summer season in both areas, typically between June and September, depending on the local conditions. Field trials were conducted in four reference plots: the P1 plot in Montpellier in 2017, followed by a plot near Tarbes in 2018, 2019, and 2020, denoted P2, P3, and P4, respectively (Figure 1).  
Occitanie is an important agricultural region that cultivates various cereals and spring/summer vegetables. The most common cereals grown there, such as maize, soybean, wheat, barley, and sunflower, cover 34% of the region's cultivated land, as per the French Land Parcel Identification System (LPIS) called RPG (Registre Parcellaire Graphique) [85]. Cereal growers in Occitanie face the challenges of coping with low and unpredictable rainfall, recurring water, and heat stress. To ensure sufficient yields, they rely on safe irrigation, but they also grapple with the limited availability of resources.  
Moreover, Figure 1 presents the collected data from the weather station in proximity to each site, including the average air temperature (Tavg), global solar radiation (Rg), rainfall (R), and Penman-Monteith reference evapotranspiration (ETo). The monthly meteorological data are summarized in Table 1.
## 4 Experiments
### Fault Diagnosis of Rolling Bearings
#### 4.1.2 Results
In [44], the recognition model was based on SAE, and a combination of digital wavelet frame (DWF) and nonlinear soft threshold method was carried out to denoise the signals. For the fault diagnosis task, the SDAE method could achieve better recognition result compared to these methods with the same benchmark. Compared with SVRM, which needs select features manually, the SDAE can automatically extract typical features. For LDA and DWF methods, the integral model of SDAE can achieve better fitting performance. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**IR Fault Accuracy (\%)**} & \multicolumn{3}{c}{**B Fault Accuracy (\%)**} & \multicolumn{3}{c}{**OR Fault Accuracy (\%)**} & \multicolumn{3}{c}{**Health Accuracy (\%)**} \\ \cline{3-10}  & & **Training** & **Testing** & **Training** & **Testing** & **Training** & **Testing** & **Training** & **Testing** \\ \hline \hline Bearing & SDAE & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ Dataset I & DBN & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ \hline Bearing & SDAE & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ Dataset II & DBN & 100 & 100 & 100 & 100 & 98 & 98 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Identification results of bearing fault obtained by SDAE and DBN. \begin{table}
\begin{tabular}{l l l l} \hline \hline \multirow{2}{*}{**Fault Size**} & \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Recognition Accuracy**} \\ \cline{3-4}  & & & **Training (\%)** & **Testing (\%)** \\ \hline \multirow{4}{*}{0.007} & SDAE & 100 & 100 \\  & SVRM & 100 & 97.91 \\ \cline{1-1}  & LDA & 100 & 99.57 \\ \cline{1-1}  & DWF + SAE & 100 & 99.25 (1 hp load) \\ \hline \multirow{4}{*}{0.014} & SDAE & 100 & 100 \\  & SVRM & 100 & 98.33 \\ \cline{1-1}  & LDA & 99.02 & 99.02 \\ \cline{1-1}  & DWF + SAE & 100 & 99.62 (2 hp load) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of different Methods in CWRU data set.