esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance of Modulation Schemes over IBC Channel
The BER is lower than \(10^{-3}\) when \(E_{b}\)/\(N_{0}\) is higher than 6.5 dB with the maximum bit rate of 70 kbps for both BPSK and QPSK modulation. For 8 PSK, BER is lower than 0.01 when \(E_{b}\)/\(N_{0}\) is higher than 7 dB with the bit rate lower than 30 kbps. Among the three modulation methods, 8 PSK obtains the highest bandwidth efficiency; while QPSK is twice as high as that of BPSK. However, the 8 PSK suffers high BER at the same bit rate. In other words, 8 PSK trades bandwidth efficiency off BER while QPSK obtains lowest BER with higher bandwidth efficiency. Thus, considering the bandwidth efficiency and BER, QPSK modulation scheme obtains the highest bit rate with low BER and is the optimal modulation scheme among BPSK, QPSK and 8 PSK.
## 3 The Hybrid Distributed Topology Control Scheme (HDTC)
### Phase I: Forming the Communication Group  
#### 3.1.1 Initial Selection (Group 1): Modified Cone-Based Method  
In order to determine the initial forwarding direction, we may apply the geometrical information and the moving directions of the source and destination vehicles. Referring to Figure 3, define the forward cone:  
\[K_{+}=\{v_{w}\in\mathcal{R}^{2}:\cos(\theta)<(v_{w},v_{u})<1\} \tag{1}\]  
where \(v_{(\cdot)}\) is the normalized unit vector of movement, \((\cdot,\cdot)\) denotes the ordinary inner product on \(\mathcal{R}^{2}\), \(\theta\) is related to the opening angle of the cone and \(0<\cos(\theta)<1\). Analogously, the backward cone \(K_{-}\) is defined by the property:  
\[K_{-}=\{v_{w}\in\mathcal{R}^{2}:-1<(v_{w},v_{u})<-\cos(\theta)\} \tag{2}\]  
For a fixed destination vehicle, the forwarding cone can be determined by:  
* If the inner product \((\overrightarrow{P_{u}P_{w}^{\prime}},v_{u})\geq 0\), forward the information based on \(v_{u}\).
* If the inner product \((\overrightarrow{P_{u}P_{w}^{\prime}},v_{u})<0\), forward the information based on \(-v_{u}\).  
Note that \(P_{u}\) is the position information of vehicle \(u\). Similarly, for a moving destination vehicle, the criteria are:  
* If \((v_{w},v_{u})\geq 0\;\&\;(\overrightarrow{P_{u}P_{w}},v_{u})\geq 0\), forward the information based on \(v_{u}\).
* If \((v_{w},v_{u})\geq 0\;\&\;(\overrightarrow{P_{u}P_{w}},v_{u})<0\), forward the information based on \(-v_{u}\).
* If \((v_{w},v_{u})<0\), select the nearest vehicle in the opposite moving direction. Then follow the above two steps.  
As shown in Figure 3 (left), in order to form the communication group of vehicle \(u\), let the forward vector \(v_{u}\) be a reference direction. Note that the forward vector \(v_{(\cdot)}\) can be determined by the geometrical information of the destination vehicle. Based on the vector \(v_{u}\), vehicles are selected to be group members of \(u\). The cone of degree \(2\theta\) is bisected by the vector \(v_{u}\). Referring to Figure 3 (left), if a node has the angle information \(0\leq\cos X\leq\cos\theta\), add it to Group 1; otherwise, it is excluded. In the modified cone-based method, when the node moves slower than the observation node (say, vehicle \(u\)), there may be a gap to make the role of a node oscillate between being a group member and not being a group member. Thus, as shown in Figure 4, in order to reduce the occurrence frequency of this scenario, a timer is implemented in the cone-based scheme to further observe the behaviors of neighboring vehicles. Consequently, we renew the timer as long as the angle information of a node passes the test of the cone-based method.
# Gradient-Guided and Multi-Scale Feature Network for Image Super-Resolution
## 2 Related Works
### Gradient Feature  
In the past few decades, the prior-based image restoration methods have achieved remarkable results. Since the edge is one of the most important image features, the edge prior has also become one of the most effective image priors. [PERSON] et al. [36] utilized edge prior to guide image super-resolution, which effectively enhanced the edge details of the reconstructed image. At the same time, structure prior has also received more and more attention because it is able to provide effective clues for the estimation of high-frequency details. [PERSON] et al. [37] obtained the image structure information by extracting gradient features, and then used the structure prior to guide image super-resolution to improve restoration ability of high-frequency details. [PERSON] et al. [38] proposed a combination of multiple gradient description models and estimated the parameters of multiple gradient descriptors at different resolutions to obtain descriptions of different edge contours to obtain effective gradient contours. These methods are mainly based on statistical correlation, observing the parameter estimation from LR to HR to obtain edge estimation parameters to model. However, such methods are often very complicated and inefficient.  
Currently, for the most popular deep-learning-based SR methods, some models also explore and exploit image priors in the network to promote super-resolution performance. [PERSON] et al. [39] introduced the image edge structure to CNN for the first time and proposed the DEGREE model. However, DEGREE directly uses the LR map obtained by Bicubic, which may introduce additional noise, resulting in poor super-resolution performance. To boost the restore ability of details, [PERSON] et al. [40] incorporated gradient feature into the GAN model, and used the gradient map as a structure prior to guide image super-resolution, thereby alleviating the geometric distortion and artifact noise generated by the GAN model.
# Behavior Characteristics of a Booted Sleeper Track System According to Substructure Deformation
## 1 Introduction
[PERSON] and [PERSON] predicted and assessed track performance according to the changes in dynamic wheel-rail force and track support stiffness through a qualitative prediction model [14]. Furthermore, [PERSON] et al. [15] performed finite element analysis (FEA) to explain the connection due to an increase in dynamic wheel load in the transition zone when there was high moisture and connection; they confirmed that high moisture is a source of transition zone problems. Therefore, a study was conducted to analyze the structural behavior of the train-track system through several experiments and FEA [16, 17, 18]. The results of previous studies on the interaction between a concrete slab track and the supporting bridge have shown that the deflection of the bridge and the consequent bridge-end rotation angle directly affect the behavior of the track, inducing uplift and compression forces in the rail fastening system. Similarly, in underground rail structures, subsidence or uplift occurring within a certain range owing to substructure deformation can interact with the track, adversely affecting the rails, sleepers, and rail fastening systems. Notably, the behavioral characteristics of a sleeper floating track system in which the concrete bed and sleeper are structurally separated by a resilience pad will differ from those of a general concrete slab track consisting of sleepers integrated with the concrete bed. Therefore, in this study, the effects of substructure deformation on the behavior of a sleeper floating track system installed in a subway box tunnel structure were numerically analyzed. Figure 1 shows a block diagram of the STEDEF sleeper floating track system, the target structure of this study. The mechanism of interaction between this sleeper floating track system and the supporting substructure was analyzed in this study considering the structural characteristics of the track system. This interaction mechanism is illustrated in Figure 2, in which it can be observed that as the subway box tunnel deforms owing to uplift or subsidence, the concrete slab track constructed within is affected in different ways. In the target sleeper floating track system, the reinforced concrete (RC) sleeper (consisting of two RC blocks connected by a tie-bar) and concrete bed are separated by the resilience pad, as shown in Figure 2a. Thus, the deformation of the RC sleeper and rail is  
inconsistent with that of concrete bed. As a result, in the uplift condition, the resilience pad is compressed between the RC sleeper and concrete bed, as shown in Figure 2b; in the subsidence condition, the RC sleeper and the concrete bed are separated, creating a gap between them that constitutes a non-contact state, as shown in Figure 2c. To quantitatively analyze the effects of substructure uplift and subsidence on the behavior of a sleeper floating track system inside an underground box tunnel, a three-dimensional (3D) finite element analysis of the sleeper floating track system-substructure interaction was performed in this study.
## 3 Results and Discussion
### Applicability of HWWC  
The stability of the catalyst is an important index for practical applications in wastewater treatment. Therefore, sequential OTC degradation experiments were performed to test the reusability of the catalyst (Figure 8a). The reaction was conducted for 24 min, after which the HWWC was recovered using an external magnet. The degradation performance of the HWWC did not significantly decrease after eight repeat experiments with a final degradation efficiency of 92.7%. This result indicated that the catalyst exhibited excellent regeneration performance and stability. Since the actual environmental water contains a large amount of organic and inorganic compounds, the activity of SO4*- and HO* may be reduced. Therefore, it is necessary to investigate the effect of radical scavengers of these organic and inorganic compounds. Chloride ion (Cl-), one of the representative inorganic compounds present in large amounts in environmental water, can reduce degradation efficiency by the following equations (Equations (10)-(11)) [38; 43]. In the present condition, however, the effect of chloride ion ((Cl-)0 = 10 mg/L) was negligible (Figure 8b). By contrast, the OTC degradation was significantly reduced by the organic compounds present in the secondary effluent (pH = 7.2, [DOC]0 = 4.71 mg/L, UV254 = 0.100, SUVA = 2.12) (Figure 8c). This is because the electron-rich moieties in the molecular structure of natural organic matter (NOM) present in the secondary effluent can be readily attacked by electrophilic radicals such as SO4*- and HO* [44].  
\[\text{SO}_{\text{4}}\text{${}^{-}$}+\text{Cl}\text{${}^{-}$}\rightarrow\text{SO }_{\text{4}}\text{${}^{2-}$}+\text{Cl}\text{${}^{\bullet}$} \tag{10}\]  
\[\text{Cl}\text{${}^{-}$}+\text{HO}\text{${}^{\bullet}$}\rightarrow\text{Cl} \text{${}^{\bullet}$}+\text{HO}\text{${}^{-}$} \tag{11}\]  
Figure 7: Oxytterracycline (OTC) degradation efficiency with different pH conditions ([PS]0 = 1 mM, [OTC]0 = 20 \(\mu\)M, [HWWC]0 = 0.2 g/L). PS: persulfate; HWWC: hand-warmer waste catalyst.
## 2 Methods
### Analyses  
Analyses of these comprehensive datasets were conducted to enhance our understanding of ecosystem form and function in this managed coniferous forest. Flux tower data are summarized in Figure 3. We examined the impact of LP stand age and structure dependencies on spectral optical indices derived from fluorescence and reflectance measurements, and their use as bio-indicators of ecosystem LUE for a set of forest ROIs having discrete age classes, biomass and canopy closure. We provide examples of data collected at the native 2 m\({}^{2}\) spatial resolution (Figure 4), data aggregated at 14 m\({}^{2}\) in the standard G-LiHT format that was originally implemented for LiDAR (Figures 5-7), and data which were aggregated to \(\sim\)49 m\({}^{2}\) (Figures 8-14) to obtain representative values per ROI. These examples demonstrate the relevance of this type of data to science questions and characteristics of future satellite missions such as ESA's FLUorescence EXplorer (FLEX, 2022 [1; 2]) and the NASA Pre-Phase A concept Hyperspectral Infrared Imager (HyspIR) [21].  
Imagery from both the G-LiHT VNIR spectrometer and the _HyPlant_ DUAL VSWIR spectrometer were used to compute two well-known normalized difference spectral reflectance (R) indices: the PRI (e.g., [22; 23; 24]), and the NDVI (e.g., [25; 26]), where the standard PRI = [(R531 - R570)/(R531 + R570)] and the NDVI = [(R750 - R660)/(R750 + R660)], using 5 nm bands. The _HyPlant_ DUAL Spectrometer data were also used in conjunction with accepted spectral algorithms to derive canopy parameters such as leaf area index (LAI) and total canopy chlorophyll (TChl) content. TChl was estimated using the MERIS Terrestrial Chlorophyll Index, MTCI [27; 28],which was developed as a standard ESA product for the Medium Resolution Imaging Spectrometer (MERIS), where MTCI = [(R754 - R709)/(R709 - R681)]. The red/far-red fluorescence ratio (Fratio), F685/F740, was computed from the _HyPlant_ FLUO. This ratio has been used to describe photosynthetic efficiency at leaf level (e.g., [29; 30; 31]) and canopy level (e.g., [32; 33]).  
Analyses focused on fluorescence and reflectance variables for the ROI stand age groups (\(n\) = 30) across the Parker Tract forest site. For the mature stand associated with the AmeriFlux NC2 tower, analyses examined the comparisons of tower-estimated LUE (GEP/PARi) and optical variables derived from _HyPlant_'s FLUO fluorescence and DUAL reflectance data. Statistical analyses were conducted with Systat 13 (Systat Software, Inc., San Jose, CA, USA).
## 4 Conclusions  
In this study, drying of kiwifruit was performed at thicknesses of 4, 6 and 8 mm with a hybrid _HAI_ dryer at air temperatures of 50, 60 and 70 \({}^{\circ}\)C and ultrasonic times of 0 (control sample), 10, 20 and 30 min. The results showed that, by increasing the temperature, ultrasonic time and decreasing the thickness, the drying time and _SEC_ decreased. The logistic model was able to predict the MR with the highest value of \(R^{2}\) = 0.9997 and the lowest values of \(RMSE\) = 0.0117 and \(\chi^{2}\) = 0.0007 were chosen as the best model. Additionally, by increasing the thickness of the samples, ultrasonic pretreatment time and by prolonging the air temperature, the amount of \(D_{\mathit{eff}}\) increased from 3.09 \(\times\) 10\({}^{-10}\) to 2.26 \(\times\) 10\({}^{-9}\) m\({}^{2}\)/s. The _SEC_ value decreased significantly (\(p\) < 0.05) by increasing the temperature, ultrasonic time and decreasing the thickness. The lowest _SEC_ value was obtained at 70 \({}^{\circ}\)C, ultrasonic time of 30 min, and thickness of 4 mm. The highest values of drying efficiency, energy efficiency, and thermal efficiency were 11.09%, 7.69% and 10.58%, respectively, at air an temperature of 70 \({}^{\circ}\)C, an ultrasonic time of 30 min, and sample thickness of 4 mm. The lowest color changes and shrinkage values were 13.2%9 and 25.25%, respectively, at 50 \({}^{\circ}\)C air temperature, 30 min ultrasonic time, and 4 mm thickness.  
Figure 8: Shrinkage of kiwifruit slices under different drying conditions.  
**Author Contributions:** Conceptualization, E.T. and M.K.; methodology, E.T. and M.K.; validation, E.T. and M.K.; formal analysis, E.T.; investigation, E.T. and M.K.; resources, E.T. and A.S.; data curation, E.T. and M.K.; writing--original draft preparation, E.T. and A.S.; writing--review and editing, E.T., A.S.; and M.K.; visualization, E.T. and A.S.; funding acquisition, E.T. and A.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the office of vice chancellor for research at Mohaghegh Ardabili University, and Wroclaw University of Environmental and Life Sciences.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data for this research will available.  
**Acknowledgments:** The authors are highly thankful to Department of Agricultural Technology Engineering, Mohaghegh Ardabili University, Ardabil, Iran for providing facilities to conduct this research work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Methodology  
### Proposed Framework  
The proposed intelligent farming system model for greenhouses is depicted in Figure 1. The four essential architectural layers are the cloud, fog, edge, and sensor/device. The sensor layer includes the various sensors and actuators related to the field environment. The edge layer mainly consists of a controller unit with which multiple sensors and actuators are interfaced for data acquisition and for sending data to the fog layer for further processing. The main objective of the fog layer is to develop the analytics and decision-making model based on data acquired from the edge layer and to provide the control signals to the edge layer for controlling the actuators. Finally, a data representation of sensors and actuators is displayed in the cloud layer in the form of a user interface (UI) dashboard. The suggested system is notable for its ability to assist farmers by providing greenhouse management using an IoT-based precision farming framework. The purpose is to supply agriculturists with remotely controlled greenhouse agricultural elements, such as soil moisture, CO\({}_{2}\), light, and temperature, from afar, and depending on the soil moisture values; furthermore, a command for the greenhouse doors or windows to roll up/down may be given using this system. Agriculturalists are unable to physically visit the fields as a result of this.
## 4 Simulation Studies and Numerical Justifications
### Performance of RASMR, Algorithm 1  
In this section, we evaluate the performance of Algorithm 1 on Bernoulli, Poisson, and gamma regression models, respectively. **Example 1**. : _In this example, we consider Bernoulli regression models (see (8)) for binary classifications, with one of four commonly used link functions, namely logit, cloglog, probit, and cauchi. The goal is to check not only the estimation performance of RASMR when the link function is correctly specified but also its performance when the link function is misspecified, which is crucial when selecting the link function._  
_The results of the corresponding simulation study as described in Section 4.1 are summarized in Table 2. In almost all scenarios, the RMSEs of RASMR are significantly lower than those of the mean representative (MR) approach or the original SMR approach, which implies that the RASMR algorithm (Algorithm 1) outperforms MR and the original SMR algorithm in this example. The estimates based on RASMR are not only much more accurate than the estimates using MR or SMR but also more robust._  
_Nevertheless, for the case with the cauchi link and nzNormal distribution, the RASMR algorithm performs as poorly as other methods. In Section 4.3, we will show further improvement by using Algorithm 2._  
**Example 2**. : _In this example, we consider Poisson regression with its canonical link function, namely, log link. The corresponding simulation is repeated for 100 times and the results are summarized in Table 3._  
_From Table 3 we can see that (1) In terms of the average RMSE (the smaller, the better), the original SMR outperforms MR, and RASMR further improves the accuracy of estimates impressively, although not as accurate as the full data estimate in this case (see Table 3 in [43]); (2) In terms of the percentage of NAs (the smaller, the more robust) out of 100 simulations, both MR and RASMR achieve \(0\%\) and outperform the original SMR._  
**Example 3**. : _In the example, we consider Gamma regression with its canonical link function, namely, reciprocal link. Since Gamma regression with reciprocal is relatively fragile on the distribution of features, we only consider features generated from Beta distribution. Again, the data clusters are generated from K-means clustering with K \(=1000\). Similar to Examples 1 and 2, we obtain the RMSE of the MR, SMR, and RASMR estimates from the full data estimate and summarize the results in Table 4. The pattern of the results is similar to the ones in Table 3, which confirms the significant improvement of RASMR against both MR and SMR under Gamma regression models._
###### Abstract  
Monitoring phenological changes of crops through remote sensing methods is becoming a new perspective in assessing heavy metal contamination in agricultural farmlands. This paper proposes a method that combines the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI) to detect heavy metal stress-induced variations in satellite-derived rice phenology. First, we applied the enhanced spatial and temporal adaptive reflectance fusion model to obtain the NDVI and NDWI time series for the NDVI-NDWI phase-space construction. Then, six specific rice phenometrics were derived from the NDVI and the phase-space, respectively. Last, we introduced a relative phenophase index (RPI), which characterizes the relative change of the phenometrics to identify the rice paddies under heavy metal stress. The results indicated that satellite-derived rice phenometrics are generally influenced by human and natural factors (e.g., transplanting date, air temperature, and solar radiation), while the RPI showed weak correlations with all of these variables. In the determination of heavy metal stress, the NDVI- and phase-space-based RPIs of unstressed rice both show significantly (\(p<0.001\)) higher values than those of stressed rice, while the phase-space-based RPI shows more apparent statistical difference between the stressed and unstressed rice compared to the NDVI-based one. Our work proved the capability of the phase-space-based method as well as the RPI in the discrimination of regional heavy metal pollution in rice fields.  
heavy metal stress; satellite remote sensing; rice phenometrics; relative phenophase index +
Footnote †: journal:  
Article
# Mobile Augmented Reality Application to Evaluate the River Flooding Impact in Coimbra
## 4 Materials and Methods
### Data
#### 4.2.2 Meteorological Data  
Meteorological data from Open-Meteo were utilised to gather rainfall information for Coimbra from 2000 to 2024, from [[https://open-meteo.com/en/docs/historical-weather-api](https://open-meteo.com/en/docs/historical-weather-api)]([https://open-meteo.com/en/docs/historical-weather-api](https://open-meteo.com/en/docs/historical-weather-api)) (accessed on 28 October 2024). The \"Copernicus Land Global Service\" (CLMS) portfolio from [[https://land.copernicus.eu/en/products/water-bodies/water-level-rivers-near-real-time-v2.0](https://land.copernicus.eu/en/products/water-bodies/water-level-rivers-near-real-time-v2.0)]([https://land.copernicus.eu/en/products/water-bodies/water-level-rivers-near-real-time-v2.0](https://land.copernicus.eu/en/products/water-bodies/water-level-rivers-near-real-time-v2.0)) (accessed on 28 October 2024) was selected for the Mondego river water height. Access to the CLMS platform required an account, through which data could be collected by selecting the specific region.  
The \"Historical weather API\" dataset contains hourly measured weather attributes and data since 1940. A subset of 24 years, from 2000 up to 2024, was chosen. An even smaller subset, used to correlate with the river height dataset, was selected, with a range from 2020 up to 2024. The features that were gathered are as follows:  
* Rain precipitation in mm;
* Evapotranspiration in mm;
* Temperature in \({}^{\circ}\)C.