esa-sceva/satcom-chunk-collection huggingface dataset:
#### 1.1.6 Temperature Regulation
* Environmental impact  
Passive greenhouses typically have a lower environmental footprint, as they consume less energy and rely on natural processes, aligning well with sustainable and eco-friendly practices. Their reduced energy consumption contributes to a more minor environmental impact. On the other hand, active greenhouses tend to have a higher ecological impact due to their reliance on external energy sources, which may involve the consumption of electricity or fossil fuels. The potential greenhouse gas emissions associated with these energy sources contribute to the increased environmental footprint of active greenhouses. * Complexity and cost  
Passive and active greenhouses differ in design complexity, construction, and maintenance considerations. Passive greenhouses are characterized by simpler designs with fewer mechanical components, leading to lower initial construction costs and reduced maintenance needs. Their reliance on natural processes, such as solar radiation, contributes to their simplicity. In contrast, active greenhouses are more complex, involving mechanical systems that result in a higher initial investment. The costs are associated with the purchase and installation of equipment and ongoing energy consumption. Active greenhouses may also require more frequent maintenance and monitoring to ensure the proper functioning of their mechanical components. * Energy source  
Passive and active greenhouses represent two distinct approaches to temperature control. Passive greenhouses operate without external energy sources, relying on natural sunlight and TES to achieve self-sustainability and energy efficiency. In contrast, active greenhouses depend on external energy, typically electricity (like heat pumps) or other fuel sources, to power mechanical systems for temperature control. This fundamental difference highlights the self-sufficiency of passive greenhouses, while active ones rely on ongoing energy input for their operation. * Off-grid capability  
While active greenhouses depend on mechanical systems and outside energy sources, passive greenhouses regulate temperature and humidity through natural processes, and renewable sources are the leading energy suppliers. Accordingly, passive greenhouses can operate off the grid or in remote areas with limited access to a continuous power supply. However, it is worth mentioning that active greenhouses could be coupled with renewable systems to cover both off-grid and on-grid conditions. Moreover, TES allows a greenhouse to operate off the grid or in remote areas. This can be critical for agricultural operations in rural or isolated locations. This system can be integrated into both greenhouses. This method can also be utilized in active greenhouses; however, its application in passive greenhouses is essential. Of course, it is crucial to note that the control system, the main component of active greenhouses, will get more complex as this system is added.
## 4 Results
### Identification of Parameters and Validation
The results obtained with the proposed model in cooling mode presented an average relative error of 2.9% concerning the measured heat flux, thus verifying the efficiency and quality of the proposed model. Also, the water outlet temperature was a key result of the model. Figure 16 (right side) shows the acceptable estimation of the model. The analysis of the water outlet temperature presented an average relative error of less than 0.6%, which is why an exhaustive knowledge of it is guaranteed with applying the proposed model. Also, the results obtained in heating mode showed an average relative error lower than that of cooling, of only 2.4% concerning the measured heat flux. At the same time, the estimation of the water leaving temperature only showed a mean deviation of 0.5%. Figure 14: Obtaining model parameters. Naperian logarithm (convective heat flux) vs. Naperian logarithm (\(LMTD_{cv}\)). Figure 16 shows the convective heat flux (left) validation and heat transfer coefficient (right, \(h_{c\
u 1}\) since Equation (12)) in cooling mode. Figure 17 shows the same results for heating mode. In this case, the convective heat transfer coefficient was the \(h_{c\
u 2}\) value from Equation (13). Figures 16 and 17 allow for analysis of validation results of convective heat flux. On the one hand, the average relative error of the convective heat flux was \(1.4\%\). This results verifies the efficiency and quality of the proposed model. On the other hand, the relative error of convective heat transfer coefficient estimation was \(1.7\%\). This is considered and acceptable error. Finally, it is important to highlight that the estimation in heating mode is more accurate than cooling mode. It is due to the difficulty of measuring the local effects of air movement by buoyancy. However, the results prove the validity and robustness of the model. Figure 16: Cooling mode. Convective heat flux (**left**) and convective heat transfer coefficient (**right**) as a function of representative temperature difference \(LMTD_{c\
u}\). Figure 15: Validation. Total heat flux: estimated vs. measured values (**left**). Water outlet temperature: estimated vs. measured values (**right**).
# Signal Detection for Ambient Backscatter Communication with OFDM Carriers
## Abbreviations  
The following abbreviations are used in this manuscript:  
AmBC Ambient backscatter communication  
BER Bit error rate  
CDF Cumulative distribution function  
CLT Central limit theorem  
CP Cyclic prefix  
ED Energy harvester  
ID Information decoder  
MAP Maximum a _posteriori_ probability  
ML Maximum likelihood  
OFDM Orthogonal frequency-division multiplexing  
PDF Probability density function  
RF Radio frequency  
RFID Radio-frequency identification  
ROC Receiver operating characteristic  
SNR signal-to-noise ratio  
TV Television
###### Abstract  
Over the past few years, oceanographic lidar was applied to many fields, and polarization lidar could provide extra information for marine particles. To retrieve the water properties, many simulation models and inversion methods were developed. However, few of them account for the depolarization effect of a rough sea surface. In this study, we develop a model to calculate reflection and transmission Mueller matrices, coupled with the lidar observation geometry. Compared with another operational method, our model has a satisfactory performance. This model also considers the shadowing effects of wave facets. Then, we analyze the polarized properties in different azimuth and zenith angles and find that the reflection of sea surface has a crucial effect on the polarization properties of lidar. For unpolarized light, the reflected light tends to be partially polarized. However, for lidar light that is completely polarized, there is an obvious depolarization owing to multiple scattering, and the depolarization is not negligible at small incident angles. The retrieval of properties of ocean constituents can be effectively improved, becoming more accurate by accounting for the depolarization effects of sea surfaces based on our method.  
lidar; rough sea surface; depolarization; reflection; transmission +
Footnote †: journal: Applied Sciences
## 3 Results
### Effects of Forest Cover Change on Forest Carbon Dynamics
#### 3.1.3 Forest Cover Change in Different Regions and Its Resulting Carbon Budget  
The study area's forest cover change from 2000 to 2022 was estimated using the CLCD. Figures 5 and 6 indicate significant forest cover change. The forest area of Nanjing and Shaoguan showed a decreasing trend. The main reason for the decrease in forest area in the two cities is the conversion of forest into farmland. They had 93.8% and 96.6% of the forest loss area, respectively (Tables S5 and S6). However, they differed in the trend in forest cover change types and regions. For example, the farmland returned to forest in Shaoguan showed an increasing trend, while the farmland returned to forest in Nanjing showed a decreasing trend (Tables S5 and S6).  
Combining CASA with the bookkeeping model, we obtained the carbon budget caused by forest cover change in the two regions (Figure 7). The two regions' carbon flux caused by forest cover change showed significant spatial differences. The carbon flux in Shaoguan was predominantly observed in the western, central, northeastern, and southern zones, exhibiting a relatively dense pattern. Specifically, the carbon flux was primarily manifested as carbon loss in the west and central areas. The northeast region showed carbon sequestration. In the southern region, both carbon loss and carbon sequestration were present. Nanjing's central and southern areas are concentrated areas of carbon flux caused by forest cover change. The carbon flux in the central region was mostly carbon loss, while that in the south region was carbon sequestration.  
Figure 6: Forest cover change in Nanjing from 2000 to 2010 (**a**) and 2010 to 2022 (**b**).  
Figure 7: Carbon storage change between 2000 and 2022 in Shaoguan (**a**) and Nanjing (**b**).  
To study the carbon flux caused by different types of forest cover change, we identified two land use patterns based on forest cover change: afforestation and deforestation (Table S7). Figure 8 shows that afforestation in the two regions led to a net carbon accumulation in vegetation biomass and soil, increasing forest carbon storage by 0.269 TgC in Nanjing and 1.509 TgC in Shaoguan from 2000 to 2022. The total carbon loss caused by deforestation was 0.491 TgC in Nanjing and 2.802 TgC in Shaoguan. The results showed that afforestation increased forest carbon storage, while deforestation caused forest carbon loss. The deforestation area was much larger than the afforestation area in both regions, and the carbon budget caused by forest cover change showed a net carbon loss, with 1.293 TgC in Shaoguan and 0.222 TgC in Nanjing from 2000 to 2022.
# Filtering Organized 3D Point Clouds for Bin Picking Applications
## 2 Related Work on Filtering 3D Point Clouds
Isolation forest (IF) and elliptic envelope (EE) methods were used to classify outliers in synthetic data and two scans of a building [31]. Fully connected network (FNN) and spatial transformer network (STN) methods were used in [32] to first remove outliers and then to perform denoising. A standard RF classifier was used in two approaches, non-semantic and semantic (i.e., with a classifier trained separately per each semantic class), and applied to large datasets of outdoor scenes [33]. Removal of outliers in a pair of point clouds of the same scene acquired from two viewpoints was demonstrated in [34]. While most of the reviewed techniques present very impressive results, they are demonstrated on cases that are outside of the scenarios relevant in manufacturing for bin-picking. Many techniques were tested on outdoor scans or datasets containing only one object [35]. Point clouds acquired for bin-picking have completely different characteristics: the most noticeable is a presence of many instances of the same part, which are touching and occluding each other. This makes the removal of outliers very challenging. On the other hand, for bin-picking applications, not all outliers are important and need to be filtered, as only parts that are on the top of a pile are relevant for picking by the gripper. In this paper, we present a novel approach that is designed to address specific characteristics of outliers (spurious points) commonly found in 3D data acquired for bin-picking. This new fine-tuned algorithm outperforms existing general-purpose SOR procedures in terms of filtering efficacy, gauged by a number of the removed outliers.
## 3 Results and Discussion
### Effect of Operating Conditions
#### 3.2.5 Effect of Methanol Synthesis Operating Temperature  
First of all, in order to investigate the reaction conditions of methanol synthesis, the syngas circulation system is not considered in our simulation. The methanol synthesis reactor temperature is an influential parameter on the methanol yield. Figure 10 shows the effect of temperature on methanol production between 150 and 350 \({}^{\circ}\)C. The methanol yield is improved with increasing reaction temperature from 150 \({}^{\circ}\)C to 250 \({}^{\circ}\)C, reaching a maximum of 7.99 kmol/1000 kg at 250 \({}^{\circ}\)C. This trend is consistent with the kinetics of methanol synthesis that high temperatures favor its production. However, when the temperature increases from 250 \({}^{\circ}\)C to 350 \({}^{\circ}\)C, yields decrease significantly. This phenomenon is due to CO\({}_{2}\) liquefaction, which is an exothermic reaction. With respect to methanol synthesis thermodynamics, the methanol synthesis reaction is favored at low temperatures. Figure 10 indicates that the optimal temperature for methanol synthesis is around 250 \({}^{\circ}\)C, which is in agreement with various studies reported [8, 42]. In those works, the typical operation temperature for methanol synthesis is in the range of 220 \({}^{\circ}\)C to 280 \({}^{\circ}\)C.  
Figure 8: Effect of Rectisol pressure on syngas composition (\(-\)20 \({}^{\circ}\)C).  
Figure 9: Effect of Rectisol pressure on SN and methanol yield (\(-\)20 \({}^{\circ}\)C).
## 4 Conclusions  
This study proposes a new concept of employing UAV for structural health monitoring of civil infrastructures, using a vibration-based NDE method. The concept surpasses the current limitations of using a drone in combination with a visual inspection method and image processing. The vibration-based method can detect small damages which are difficult to identify, and internal damages such as thickness reduction. The key idea of the concept is to make a PZT transducer that can be temporarily attached and detached to a structure, which is a different way of conducting the EMI method, in which the PZT transducer is permanently attached to the host structure for damage identification.  
The first part of the study investigates the effect of measured impedance signature while the drone is in a hovering state. Due to the high frequency usage of the NDE method, the effect on the impedance signature can be ignored. The second part of the study evaluated the repeatability performance of the PZT device which is attached, detached, and reattached, to a stack of metal plates, where the RMSD values were calculated and analyzed. The last section of the study investigated the performance of the PZT device against two different damage types. During the first, the PZT device was attached to a metal plate with 1 mm progressive damage being introduced up to 20 mm, and during the second, the PZT device was tested against a stack of metal plates subjected to a thickness loss. The RMSD  
Figure 7: Thickness reduction case: (**a**) Impedance signatures; (**b**) RMSD results.  
values for the progressive damage case were noticeably less than the RMSD values calculated from the signatures acquired by reattaching the PZT device on an undamaged plate. Thus, more research is needed to solve this problem. However, the case of thickness reduction on a metal stack showed results which validates the drone SHM concept. For each reduction in metal layers, the RMSD values were within a specific range for all five layers. These were 2%-4%, 4%-7%, 6%-10%, 10%-14% and 12%-18% for 5 layers, 4, 3, 2, and 1 layer, respectively. Therefore, through a consideration of the concept of using a drone equipped with a vibration-based NDE method, new possibilities have been realized and existing problems will need solutions for turning this new concept into reality.  
The research was supported by a grant from \"Next-generation high speed transfer system based technology development (20160582-001)\" funded by Korea Institute of Civil Engineering and Building Technology (KICT), Korea.  
[PERSON] is the principal author for the research involved in writing the manuscript, performing experiments and analyzing data; [PERSON] contributed in conducting experiments and analyzing data.  
The authors declare no conflict of interest.
## 3 Grant-Free Scheduling
These analytical expressions are a valuable contribution to the community since they can be easily utilized to evaluate 5G NR grant-free scheduling. The availability of these exact analytical expressions is particularly useful when considering applications with very demanding reliability and latency URLLC requirements. This is the case of certain Industry 4.0 applications. For example, motion control requires a maximum latency of 1 ms and a reliability of 1-\(10^{-6}\). Control-to-control applications require a maximum latency of 4 ms and a reliability of 1-\(10^{-8}\). Factory automation applications usually demand maximum latency values in the range 0.25-2.5 ms and reliability levels up to 1-\(10^{-9}\). In this case, simulations can be very computationally expensive if we want to compute the packet reception rate (\(1-P_{c}\)) with reliability demands in the order of 1-\(10^{-6}\) to 1-\(10^{-9}\). In these scenarios, errors are very rare, and we need long and computationally expensive simulations to achieve accurate results. The analytical methodology utilized in this study is then an adequate and efficient tool for scenarios with demanding URLLC communication requirements.
## 3 Results and Discussion
### SHM-System for Oil Refinery Equipment with Defects Presence  
At an oil refinery, non-permissible defects were found in the upper part of the gas adsorber of the circulating gas (commissioned in 1970). From 1999 to 2009, the lamination was known to exist in the main upper end plate (the area of the lamination \(\sim\)0.4 m\({}^{2}\), the depth of the lamination: 6.1-12.4 mm). On the same unit, the defect of the planar type (crack) in the weld of the shell to the end plate (length: 140 mm, depth: 12-14 mm) was also present (Figure 12). This gas adsorber has the following parameters: working pressure: 4.2 MPa, operating temperature +45 \({}^{\circ}\)C, wall thickness: 22.0-24.0 mm, the inner diameter: 1.156 m and the height: 16.45 m.  
A preliminary decision was made to install a monitoring system for the upper part of the adsorber. The main task of the monitoring system was to ensure safe operation of the adsorber before its replacement within a year.  
Prior to installation of the monitoring system during the overhaul in the spring of 2012, its AE testing was carried out (Figures 12 and 13). AE sources of class I and II were registered, the locations of which were correlated with the place of previously detected defects on the vessel head of the adsorber (Figure 12). This allowed us to assume the possibility of further expansion of the lamination zone during the life extension period of the adsorber.  
Based on the results of the AE testing, the previously approved decision on the installation of the monitoring system for the vessel head of the gas adsorber was confirmed. The expanded SHM-system included 4 AE sensors and a weather station (Figure 14). The threshold data acquisition approach was used, and the threshold value was about 45 dB\({}_{\text{AE}}\).  
Figure 12: Previously found defects of gas absorber head plate: lamination in the vessel head (gray areas) and locations of the registered AE source (orange). Color of points indicates total number of events in this coordinates. Yellow points: 3–10 events, orange points: 11–25 events, red points: 26–100 events.  
Figure 14: SHM-system for gas adsorber: (**a**) overall view of the head part; (**b**) AE sensor mounting place; (**c**) workplace of operator; (**d**) dialogue box of the software A-Line Mon. Color of points indicates total number of events in this coordinates. Green points: 1–2 events, yellow points: 3–10 events, orange points: 11–25 events, red points: 26–100  
Figure 13: Gas adsorber schematic drawing with places of AE sensors mounting.
## 2 Methodology
### BSSD PPP/INS Tightly Coupled Integration
BSSD coefficient matrix \(H_{BSSD,TCI,k}\) and innovation vector \(Z_{BSSD,TCI,k}\) can be calculated by  
\[H_{BSSD,TCI,k}=SSDM\times H_{TCI,k}\times SSDM^{T} \tag{18}\]  
\[Z_{BSSD,TCI,k}=SSDM\times Z_{TCI,k}\times SSDM^{T} \tag{19}\]  
\[R_{BSSD,TCI}=SSDM\times R_{TCI}\times SSDM^{T} \tag{20}\]In addition, there is Inter-System Bias (ISB) between GPS and BDS [35; 36]. To solve this problem, we consider the receiver clock offset of different GNSS as independent parameters and estimate them separately. This method is presented in detail in the works of [37; 38]. Anyway, there are two other methods to process ISB [6; 36; 39]. It is worth mentioning that the receiver clock offset, and drift can be removed in the BSSD PPP/INS TCI model, with the state vector of  
\[X_{BSSD,TCI,k}=\left[\delta p_{INS}^{n}\quad\delta v_{INS}^{n}\quad\delta\psi \quad\delta B_{a}\quad\delta B_{g}\quad\delta S_{a}\quad\delta S_{g}\quad \delta d_{wet}\quad\delta N_{IF}^{km}\right] \tag{21}\]  
where \(\delta p_{INS}^{n}\), \(\delta v_{INS}^{n}\), and \(\delta\psi\) represent the corrections of position, velocity, and attitude; \(\delta S_{g}\) and \(\delta B_{g}\) represent the scale factor and bias of gyroscope; \(\delta S_{a}\) and \(\delta B_{a}\) represent the scale factor and bias of accelerometers [40]; \(\delta d_{wet}\) is a wet component of tropospheric zenith delay, and \(\delta N_{IF}\) represents ambiguity.
# 3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video
## 4 Results and Discussion
### Results Analysis  
General results are summarized in Table 3, while a typical excerpt is presented in Figure 9. The Bland-Altman plots presented in Figure 10 represent the differences between estimates against ground-truth measurements. Means are represented by dash-dot lines and 95% limits of agreement (\(\pm\)1.96 SD) by dashed lines. Note that each ME value in Table 3 corresponds to each dash-dot line in the Bland-Altman representations.  
The results presented in Table 3 exhibit significant agreement between the estimated and ground-truth measurements: the RMSE computed from the pulse rate values assessed with the 3D CNN is lower than for the other methods. The MAE is, however, the lowest for POS, which is globally the most relevant benchmark method. Figure 9,b presents the estimation for subject #31. Apart from the couple of erroneous beats at the beginning of the POS series, both methods performed well.  
In addition, and concurrently with previous findings, GREEN, which is in fact the most straightforward method, produced noisy PPG signals and thus pulse rate series full of artifacts. GREEN presents the largest RMSE and standard deviation of pulse rate error. Surprisingly, metrics computed from CHROM estimations are very close to GREEN ones. This contrasts with the findings of [PERSON] et al. (2019), in which CHROM was even superior to POS. It is worth mentioning that the results cannot be directly compared because image processing techniques like skin detection and super-pixels were used in their work. These methods improved the signal-to-noise ratio and reduced noise and artifacts from PPG signals.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **ME** & **STDE** & **MAE** & **RMSE** \\ \hline GREEN (S  
The Bland-Altman plots confirm the metrics presented in Table 3. The distribution is far wider for GREEN, ICA, and CHROM methods than for 3D CNN and POS. Visually, it can be observed that the distribution is more concentrated for POS (Figure 10d) than 3D CNN (Figure 10e). From this observation, we can conclude that POS estimations are globally more accurate, while the method we propose presents fewer irrelevant beats (which are characterized by outliers in the figures). Excluding these artifacts from pulse rate series with a dedicated filtering technique [50] would presumably ascertain this remark. This kind of procedure has not been included because the main objective consisted in assessing the relevance of direct beat-to-beat pulse rate values.  
Training and prediction were executed on a computer equipped with an Intel Xeon CPU E5-1607 v4 and a GPU NVIDIA GeForce GTX 1080 Ti. Without any software or hardware optimization, estimating a pulse rate value from a 25 \(\times\) 25 \(\times\) 60 video patch takes 4 ms.