esa-sceva/satcom-chunk-collection huggingface dataset:
# Linking Remotely Sensed Carbon and Water Use Efficiencies with In Situ Soil Properties
###### Abstract  
The capacity of terrestrial ecosystems to sequester carbon dioxide (CO\({}_{2}\)) from the atmosphere is expected to be altered by climate change and CO\({}_{2}\) fertilization, but this projection is limited by our understanding of how the soil system interacts with plants. Understanding the soil-vegetation interactions is essential to assess the magnitude and response of terrestrial ecosystems to the changing climate. Here, we used soil profile and satellite data to explore the role that soil properties play in regulating water and carbon use by plants. Data obtained for 19 terrestrial ecosystem sites in a warm temperate and humid climate were used to investigate the relationship between remotely sensed data and soil physical and chemical properties. Classification and regression tree results showed that in situ soil carbon isotope (\(\delta^{13}\)C), and soil order were significant predictors (r\({}^{2}\) = 0.39, mean absolute error (MAE) = 0 of 0.175 gc/KgH\({}_{2}\)O) of remotely sensed water use efficiency (WUE) based on the Moderate Resolution Imaging Spectroradiometer (MODIS). Soil extractable calcium (Ca), and land cover type were significant predictors of remotely sensed carbon use efficiency (CUE) based on MODIS and Landsat data-(r\({}^{2}\) = 0.64-0.78, MAE = 0.04-0.06). We used gross primary productivity (GPP) derived from solar-induced fluorescence (SIF) data, based on the Orbiting Carbon Observatory-2 (OCO-2), to calculate WUE and CUE (referred to as WUE\({}_{\rm SIF}\) and CUE\({}_{\rm SIF}\), respectively) for our study sites. The regression tree analysis revealed that soil organic matter and soil extractable magnesium (Mg), \(\delta^{13}\)C, and soil silt content were the important predictors of both WUE\({}_{\rm SIF}\) (r\({}^{2}\) = 0.19, MAE = 0.64 gC/KgH\({}_{2}\)O) and CUE\({}_{\rm SIF}\) (r\({}^{2}\) = 0.45, MAE = 0.1), respectively. Our results revealed the importance of soil extractable Ca, soil carbon (S\({}^{13}\)C is a facet of soil carbon content), and soil organic matter predicting CUE and WUE. Insights gained from this study highlighted the importance of biotic and abiotic factors regulating plant and soil interactions. These types of data are timely and critical for accurate predictions of how terrestrial ecosystems respond to climate change.  
MODIS; carbon use efficiency; water use efficiency; solar-induced chlorophyll fluorescence; soil properties 2021132593[[https://doi.org/10.3390/ns13132593](https://doi.org/10.3390/ns13132593) [https://www.mdpi.com/journal/remotesensing](https://www.mdpi.com/journal/remotesensing)]([https://doi.org/10.3390/ns13132593](https://doi.org/10.3390/ns13132593) [https://www.mdpi.com/journal/remotesensing](https://www.mdpi.com/journal/remotesensing))
# A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments
## 5 Summary of Recent Research Articles  
One of the main goals of this paper is to provide a brief overview of the most recent technological advances in the area of eHealthcare systems where healthcare providers are able to remotely monitor patients through the state-of-the-art WBAN systems along with existing ICTs. Since this area of research is able to significantly affect the existing healthcare systems by reducing the current operational costs, it has attracted the attention of a large number of researchers and scientists during the past decade and as a result of that many promising prototypes have been designed and developed. This section attempts to consider some of the most recent scientific publications in the field of telemonitoring systems for elderly and chronically ill patients. In order to find the most relevant research articles in this area of research, three scientific databases were used for this review paper. We used the IEEE Xplore Digital Library, the ACM Digital Library and the PubMed scientific databases. The survey is limited to recent articles no older than five years as the wireless technologies of concern were only adopted widely in this period. In order to select the related articles from a large number of papers appeared in the search results, the following specific objective criteria were used when we were examining the abstracts and the main body of the texts, (A) only articles consisting of on-body (including wearable) sensors that may or may not be considered along with off-body (ambient) sensors; (B) articles that are more focused on elderly health monitoring and addressing chronic health issues; (C) articles that use a type of wireless communication technology. In addition, this survey excluded scientific papers that mainly address in-body (implantable) sensors and ambient sensors (out of the scope of this survey). The research selected 35 articles out of the 134 results that met the selection criteria. The main information extracted from these 35 articles is presented in Table 14.
## 3 Validation and Numerical Analysis  
### Dynamic Model Validation  
In order to verify the accuracy of the proposed model, I chose two cases for verification. In case 1, [PERSON] et al.'s [14] time-varying spur gear pair system was applied to the time-varying validation. The material parameters are shown in Table 1. All the responses in this study were computed by the fourth-order Runge-Kutta method, and the time step size was selected as \(\Delta t=10^{-9}\) sec. As can be seen from Figure 4, the time responses obtained from the proposed model are similar to [PERSON] et al.'s [17] results. The amplitude and the periodic of the time responses are accurate. In case 2, [PERSON] et al.'s [8] spur-geared rotor-bearing system was applied to validate the correctness of the geared rotor-bearing model. The spur-geared rotor-bearing system is shown in Figure 5, and [PERSON] et al.'s [8] values for the system parameters are listed in Table 2. Both the results presented here and by [PERSON] et al. [8] of the natural frequencies and corresponding mode shapes are listed in Table 3 for comparison. It is shown that the maximum relative error is less than 0.8 percent. This means that the results of this study are in good agreement with those of [PERSON] et al. [8].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Pinion** & **Gear** \\ \hline Tooth number & 20 & 30 \\ Base circle radius (mm) & 18.8 & 28.2 \\ Addendum circle radius (mm) & 22 & 32 \\ Mass (kg) & 0.0784 & 0.1765 \\ Mass moment of inertia (kg-m\({}^{2}\)) & \(1.39\times 10^{-5}\) & \(7.01\times 10^{-5}\) \\ Radial damping coefficient (N-sec/m) & 5.6 & 8.4 \\ Mesh stiffness during one-pair contact (N/m) & \(0.75\times 10^{8}\) & \\ Mesh stiffness during two-pair contact (N/m) & \(1.25\times 10^{8}\) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Spur gear pair parameters.  
Figure 4: Gear mesh deformations from the proposed model.  
Figure 5: Dynamic model of the spur-geared rotor-bearing system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Shaft**} \\ \hline
**Parameters** & **Driving Shaft** & **Driven Shaft** \\ \hline Shaft length (mm) & 254 & 254 \\ Outer diameter (mm) & 37 & 37 \\ Inner diameter (mm) & 0 & 10 \\ Modulus of elasticity (Gpa) & 207.8 \\ Density of material (kg/m\({}^{3}\)) & 7806 & \\ Poisson’s ratio & 0.3 & \\ \hline \multicolumn{2}{c}{**Gear**} \\ \hline
**Parameters** & **Driving Gear** & **Driven Gear** \\ \hline Tooth number & 28 & 28 \\ Base circle radius (mm) & 44.5 & 44.5 \\ Mass (kg) & 1.84 & 1.84 \\ Mass moment of inertia (kg-m\({}^{2}\)) & 0.0009 & 0.0009 \\ Polar mass moment of inertia (kg-m\({}^{2}\)) & 0.0018 & 0.0018 \\ Gear mesh stiffness (N/m) & \(1\times 10^{8}\) & \\ \hline \multicolumn{2}{c}{**Bearing Parameters**} \\ \hline All radial bearing stiffness (N/m) & \(1\times 10^{9}\) & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Spur-geared rotor system parameters.  
In summary, the proposed new model is effective for the time-varying spur gear pair and the geared rotor-bearing system.
## 3 Results and Discussion
### The Effect of PH Value  
The effect of pH value on the adsorption of As(V) and As(III) ions from water over the surface of Fe\({}_{3}\)O\({}_{4}\)/CS/PEI nanocomposite was studied in the range of 3.0 to 11.0 as shown in Figure 5b. The pH effect is known to be a very important factor due to its dual effect on ionic form of the adsorbate and the surface charge of the adsorbent. According to Figure 5b, the adsorption of As(III) ions was decreased from pH > 9.0. While in the pH range of 5.0 to 9.0, there was no significant pH effect on their removal. This behavior could be described according to the ionic form of As(III) ions at different pH values. At pH > 9.0, As(III) ions were present in the forms of HAsO\({}_{3}\)\({}^{-2}\) and H\({}_{2}\)AsO\({}_{3}\)\({}^{-}\) that cause a repulsion with the negatively charged adsorbent besides the competition with hydroxide ions for the adsorption sites, and the overall result is the decrease of As(III) adsorption. While at pH < 9.0, As(III) ions were present in the neutral forms such as H\({}_{3}\)AsO\({}_{3}\) that allow their maximum adsorption via H-bonding and Van der Wall's forces. Thus, the maximum removal of As(III) ions was reached at pH (6.0 to 8.0). Additionally, the pH increase caused an increase in the removal of As(V) ions over the surface of Fe\({}_{3}\)O\({}_{4}\)/CS/PEI nanocomposite until the value of pH (6.7), then the removal decreased with the pH increase. This behavior could be also explained according to the change of ionic form with pH change. At pH of 6.0 to 8.0, the As(V) ions were present as HAsO\({}_{4}\)\({}^{-2}\) and HAsO\({}_{4}\)\({}^{-}\). At pH of 8.0 to 10.0, the As(V) ions were present as HAsO\({}_{4}\)\({}^{-2}\). However, at pH of 3.0 to 6.0, As(V) ions were present as HAsO\({}_{4}\)\({}^{-}\) making the maximum removal reached at pH of 6.7. These results clarify that the pH value is a key parameter for the removal of arsenic ions over the studied magnetic nanocomposite.
###### Abstract  
Time-varying mesh stiffness (TVMS) of the internal mesh transmission is a significant source of excitation that causes vibration and noise in planetary gear systems, and is also an important parameter in dynamics analysis. Currently, the calculation of mesh stiffness for internal gear pairs primarily relies on finite element simulation, and there still lacks a mesh stiffness analytical model that accounts for tooth surface nonlinear contact. Therefore, this paper proposes an analytical model for nonlinear contact mesh stiffness that comprehensively accounts for tooth surface modification and the flexibility of the ring gear. Firstly, a mesh stiffness calculation model for a sliced tooth pair was established using the potential energy method, which accounted for the influence of gear ring flexibility. Secondly, the tooth deviation ease-off diagram was derived from the modified tooth surface equations, which provided data support for the nonlinear contact analysis. On this basis, slicing element pairs that met the contact conditions were identified by combining elastic deformation with mesh clearance. The comprehensive mesh stiffness in nonlinear contact was calculated by integrating the deformation coordination equation with the principle of minimum potential energy. Finally, using a group of internal helical gear pairs as an example, the validity of the proposed method was verified through finite element simulation. The effects of load, modification amount, and face width on the TVMS and load transmission error (LTE) of an internal helical gear pair were investigated by the analytical model. The results show that the analytical model can provide a reference for the optimal design of internal gear transmission.  
internal gear; potential energy method; time-varying mesh stiffness; transmission error; tooth surface modification Article
# Resource Allocation in Downlink VLC-NOMA Systems for Factory Automation Scenario
## 3 Proposed Resource Allocation Schemes
### DNN-Based Power Allocation
Based on the locations, the channel states for the nodes are calculated based on (1). Then, the channel states \(h_{n}\) and \(h_{f}\), and noise \(N_{0}\) are used as the inputs for (30) to calculate the average BER. \begin{table}
\begin{tabular}{l c} \hline Input layers & Channel states for NU and FU, \(h_{n}\) and \(h_{f}\), and noise level \(N_{0}\) \\ \hline Output layers & Power allocation ratio for NU, \(p_{n}\) \\ \hline Structure of network & \(3\times 8\times 8\times 1\) \\ \hline \end{tabular}
\end{table} 
Table 2: Proposed MLP model structure. The power ratio for near node \(p_{n}\) is searched from 0.0 to 1.0 by step 0.0001. By iteratively searching, the minimum average BER is found as a target value to train the DNN model for DBPA. The power ratio iteratively found is compared with the output of the proposed DNN model for updating the weights. To update the weights of the model, the adaptive moment estimation (ADAM) optimizer was adopted [27]. ADAM adjusts the learning rate by estimations of the fist and second moments of the gradient and has the advantages that the magnitudes of parameter updates are immutable regardless of gradient rescaling, its step sizes are approximately bounded by the step size hyperparameter, it does not require a stationary objective, it works with sparse gradients, and it naturally performs a form of step size annealing.
## 2 Materials and Methods
### Stimuli  
The stimuli utilized in the experiment were composed of three sentences extracted from the Hebrew adaptation of the Matrix sentence [49]. These sentences were verbally recorded by a female speaker who was a native Hebrew speaker. The selection of sentences followed the approach employed in prior investigations [7; 8; 50]. To mitigate the cognitive load on working memory, the sentences were shortened to three words: subject, predicate, and object. The manipulation of the sentences was conducted by means of a 13-point stimulus continuum. This continuum was designed to follow an exponential progression of \(\sqrt{2}\) intervals, spanning from a reduction of \(-0.18\) semitone to a reduction of \(-8\) semitones, as described in previous studies [7; 8; 50]. This manipulation process created three distinct dimensions of change: (1) Fundamental Frequency (F0); (2) formants, wherein all formants were uniformly shifted in a downward direction based on the 13-point stimulus continuum; and (3) a combined variation involving both F0 and formants, where adjustments mirrored each other. For instance, in the case of the F0-manipulated sentences, the average F0 exhibited variations of 0, \(-0.18\), \(-0.26\), \(-0.36\), \(-0.51\), \(-0.72\), \(-1.02\), \(-1.44\), \(-2.02\), \(-2.86\), \(-4.02\), \(-5.67\), and \(-8\) semitones relative to the original mean F0 of the sentence. Accordingly, for the first sentence, the mean F0 was 175.62 Hz and the comparison sentences underwent exponential alterations at \(\sqrt{2}\) increments, ranging from 174 Hz to 110.35 Hz. This was accomplished by utilizing the Pitch Synchronous Overlap and Add (PSOLA) algorithm, which facilitated pitch extraction and manipulation [51]. For formant frequency modifications, a comparable exponential approach was adopted, with adjustments occurring in \(\sqrt{2}\) steps. This ranged from a value of 0.99 (representing the smallest ratio between the initial formant frequencies and the adjusted formant frequencies) to 0.63 (reflecting the highest ratio). This manipulation necessitated resampling of the stimuli to compress the frequency axis by a range of factors analogous to those employed for F0 adjustments. Subsequently, the PSOLA algorithm was applied to reinstate the original pitch and duration. Considering that the typical vocal tract length (VTL) for an adult female is approximately 140 mm [52] and that formant frequencies are inversely correlated with VTL [53], the range of formants corresponded to a hypothetical change in VTL from 2 mm to 88 mm. It should be noted that this VTL range was intentionally broader than the physiological variability seen in humans. This decision was initially made to prevent potential lower-bound effects for participants with cochlear implants (CIs) who encountered challenges in distinguishing normal differences in VTL [7]. All manipulations were executed using PRAAT software, version 5.4.17 (copyright (c) 1992-2015 by Boersma & Weenink).
## 3 Research Method
### Feature-Vector Size Reduction  
The Principal Component Analysis method was used for reducing the size of various feature vectors. The trial-and-error method was adopted to decide the number of PCA components. Initially, 20 PCA components were estimated from individual feature-vectors (i.e., from F1, F2, F3 and F4). The respective principal components were fused into a single feature-vector. A sub-dataset of 3600 such fused feature-vectors (i.e., 100 samples from each character-class) was prepared for the purpose. The sub-dataset was used to train and test the proposed classifiers. The process was repeated with the sub-dataset by increasing the number of PCA components up to 60 in the step of 10. It was observed that recognition accuracy improved in a good amount by increasing PCA components in the range of 20 to 40, but no significant improvements were noticed for the range of 40 to 60. This made the 40 PCA components an optimum choice. In the proposed work, the size of individual feature-vectors (F1, F2, F3, and F4), were reduced to 40 and the resultant vectors were marked as W, V, R, and I, respectively.
## 4 Location Estimation-Based Congestion-Aware Routing Protocol (LECAR)--Detailed Proposal
### Defining Data Tables to Store the Necessary Information  
To effectively select a suitable custodian, LECAR must keep a record of specific data. We briefly describe them in this section.  
Figure 2 presents the global pheromone map table format that a UAV uses to keep the pheromone records for the mobility model. Each UAV uses this map to determine the path. \(T_{pheromone\_update}\) denotes the update time of the pheromone value for the corresponding cell.  
Figure 3 displays the one-hop neighbor table format. Each UAV maintains this table by exchanging hello messages. The notation \(T_{1\_hop\_update}\), Curr_Cell_ID, Nxt_Cell_ID, \(\varphi\), and TTL denote the update time of the one-hop neighbor, current cell ID, next cell ID that the UAV will visit next, average buffer occupancy, and time to live of this entry. The TTL value is defined as follows:  
\[TTL=3\times hello\_interval. \tag{1}\]  
Figure 1: Illustration of the considered problem scenario: (**a**) the mission area and (**b**) the division of the mission area into cells and waypoints and the possible movements of a UAV.  
Figure 2: Data table format for storing a global pheromone map.  
Figure 4 presents the two-hop neighbor table format. A UAV updates the two-hop neighbor entry whenever it receives a new list of neighbors from the one-hop neighbors. Moreover, a two-hop neighbor entry expires whenever the TTL of the corresponding one-hop neighbor expires.  
Finally, Figure 5 illustrates the table format for storing the location information of the UAVs.
## 4 Algorithm Analysis
### Complexity Analysis
#### 4.2.1 Message Complexity  
The total number of sent messages is considered to determine the message complexity. The message complexity includes three parts, according to the analysis of the message flow between nodes in Section 4.1.  
Figure 8: Message flow during path selection. (**a**) Message flow with Sink node; (**b**) Message flow with cluster head node; (**c**) Message flow with non-cluster node.  
Figure 9: Message flow during position adjustment. (**a**) Initialization phase of position adjustment; (**b**) Phase of position adjustment; (**c**,**d**) End of position adjustment.  
\(N\) nodes are scattered on the water surface randomly and uniformly. At the beginning of the uneven clustering, \(N_{p}\) nodes become provisional cluster head nodes, and each of them broadcasts its own status message (node ID, \(d(p_{i},Sink)\), \(R(p_{i})\)), where \(N_{p}\) is formulated in Equation (17) according to Equation (8).  
\[N_{p}=\frac{N\pi d_{hot}^{2}\left(T_{h1}+3T_{h2}-4T_{h3}\right)}{length\times width }+NT_{h3} \tag{17}\]  
Each of them then makes a decision by broadcasting \(M_{CH}\) to act as the final cluster head node or \(M_{GC}\) to exit the competition. Assuming that there are \(K_{c}\) cluster head nodes, they send \(K_{c}\) \"elect cluster head\" messages, and then \((N-K_{c})\) non-cluster head nodes send \((N-K_{c})\) \"join\" messages. Thus, in the process of the uneven clustering, the total number of message is formulated as:  
\[2N_{p}+K_{c}+(N-K_{c})=2N_{p}+N \tag{18}\]  
During the process of the path selection, each cluster head node sends a request message to its next-hop node, and then, a corresponding reply message is sent to it. Thus, the total number of messages is \(2K_{c}\) in the process.  
In the phrase of in-cluster nodes' position adjustment, each cluster head node calculates the positions of in-cluster nodes, when it receives \(M_{CD}\) from its next-hop node. After the positions of nodes in its cluster are adjusted, it sends \(M_{AD}\) to its next-hop node sooner or later. Therefore, there must be two messages transmitted on the link between a cluster head node and its next-hop node. What is more, each cluster head node has only one next-hop node because of the hybrid radius path-selection method. Therefore, the network has \(K_{c}\) cluster head nodes, and a sink node has \(K_{c}\) links; and the number of message transmitted on these links is \(2K_{c}\). In addition, each cluster head node respectively sends a corresponding adjustment message to its in-cluster nodes, after calculating the depths of nodes in its cluster. Supposing that the \(i\)-th cluster has \(N_{i}\) in-cluster nodes, the number of messages in the sub-process is formulated as:  
\[\sum\limits_{i=1}^{K_{c}}N_{i}=N \tag{19}\]  
Finally, the total number of the whole process is \(2K_{c}+N\).  
Consequently, the total number of URSA messages is formulated as:  
\[2N_{p}+N+2K_{c}+2K_{c}+N=2N_{p}+2N+4K_{c}<8N \tag{20}\]  
Thus, the message complexity of URSA is \(O(N)\).
## 5 Experiments and Results
### Positional Attention Analysis
It is indeed interesting that regions that agents mostly take care of can be interpreted by visualizing the positional attention heatmaps from each eDA3-X agent. Consequently, the performance of eDA3-X agents significantly improves when using the relative view method to observe the environment. Unlike agents with the local view method that follow two strategies, agents with the relative view method seem to follow only one solid strategy: segmenting the area for each agent. Hence, agents with the relative view achieve an explicit allocation of regions per agent, resulting more efficient performance.