esa-sceva/satcom-chunk-collection huggingface dataset:
_Applied_  
_sciences_  
_Article_  
**Surface Decorated Zn\({}_{0.15}\)Cd\({}_{0.85}\)S Nanoflowers with**  
**P25 for Enhanced Visible Light Driven Photocatalytic Degradation of Rh-B and Stability**  
**[PERSON]\({}^{\copyright}\), [PERSON] and [PERSON] \({}^{\ast}\)**  
State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, School of Materials Science and Engineering, Wuhan University of Technology, Wuhan 430070, China;  
[EMAIL_ADDRESS] (M.A.); [EMAIL_ADDRESS] (Y.L)  
\({}^{\ast}\) Correspondence: [EMAIL_ADDRESS]; Tel: +86-27-87651107  
Received: 22 January 2018; Accepted: 17 February 2018; Published: 26 February 2018  
**Featured Application: P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanocomposite possesses a great potential to be a green catalyst for the photolytic degradation of hazardous dyes due to its low cost, efficient reactivity and high stability. The prepared catalyst may further be used for the degradation of organic compound and toxic heavy metals.**  
**Abstract:** Decoration of Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanoflowers with P25 for forming P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanocomposite has been successfully synthesized with fine crystallinity by one-step low temperature hydrothermal method. Photocatalytic efficiency of the as-prepared P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S for the degradation of Rh-B is evaluated under the visible light irradiation. The synthesized composite is completely characterized with XRD, FESEM, TEM, BET, and UV-vis DRS. TEM observations reveal that P25 is closely deposited on the Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanoflowers with maintaining its nanoflower morphology. The photocatalytic activity of the as-obtained photocatalyst shows that the P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S exhibits very high catalytic activity for degradation of Rh-B under visible light irradiation due to an increasing of the active sites and enhancing the catalyst stability because of the minimum recombination of the photo-induced electrons and holes. Moreover, it is found that the nanocomposite retains its photocatalytic activity even after four cycles. In addition, to explain the mechanism of degradation, scavengers are used to confirm the reactive species. Photo-generated holes and \({}^{\bullet}\)OH play a significant role in the visible light of P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanocomposite induced degradation system, but electrons play the most important role.  
**Keywords:** Zn doped CdS; P25 decoration; photocatalysis; hydrothermal method; scavenger method
## 2 Theoretical Background
### Dwell Time Solution Based on Linear Equations  
According to Equation (1), given the material removal map and material removal function, the solution of the dwell time can be regarded as a deconvolution process, and its solution algorithm has an important effect on the deterministic polishing process and results. Existing dwell time algorithms are mainly split into two categories: discrete convolution model and linear equation model. The former has a fast computation speed, but usually low convergence accuracy. The latter has high accuracy, but the solution speed is slow. With the introduction of regularization and sparse matrix operation, the solution algorithm based on the linear equation model is becoming more stable and fast, and it can be applied to a flexible and changeable planning path, which makes it more feasible and flexible in practical application [19]. It is assumed that there are \(n\) dwell points along the planned path on the to-be-polished surface; the surface is divided into \(m\) control points; and correction of the surface is simplified to reduce the form error at these control points. When the TIF dwells at a dwell point, the material removal at all control points within a unit of time can be written in the form of \(m\times 1\), and then, the TIF removal matrix \(A_{m\times n}\) for the \(n\) dwell points can be obtained, which is usually a sparse matrix with \(m\) rows and \(n\) columns. The form error to be removed at the \(m\) control points is represented as a column \(b_{m}\times 1\). Similarly, the dwell time of each dwell point to be calculated is also written as a column vector form, i.e., \(x_{n}\times 1\). In the linear model solution, the dwell time corresponding to each dwell point can be estimated by solving the equation \(A_{m\times n}x_{n\times 1}=b_{m\times 1}\). However, because it is an indefinite equation and the large condition number means that rounding error and other errors will seriously affect the solution of the problem, the regularization method using the smart damping factor is used here to ensure stability of the solution, as shown in Equation (2) [20].  
\[\left\{\begin{array}{l}\left(\begin{array}{c}A_{m\times n}\\ \gamma\times E_{m-n}\end{array}\right)x_{n\times 1}=\left(\begin{array}{c}b_{m \times 1}\\ 0\times I_{(m-n)\times 1}\end{array}\right)\\ \gamma=3\times TRP\end{array}\right. \tag{2}\]  
where \(\gamma\) represents the regularized damping coefficient, \(E_{m-n}\) represents a unit matrix of \((m-n)\) order and \(I_{(m-n)\times 1}\) represents a column vector containing \((m-n)\) elements. \(TRP\) represents the maximum removal depth of the TIF per unit time, in \(\lambda/\)min (\(\lambda\) = 632.8 nm).  
By using the least squares method and sparse matrix method to solve the objective function of Equation (3), the stable and reliable solution of dwell time can be obtained.  
\[\min\left\{\left\|Ax-b\right\|^{2}+\left\|\gamma x\right\|^{2}\right\},x\geq 0 \tag{3}\]
# Influence of Solvent Selection in the Electrospraying Process of Polycaprolactone
## 3 Selection of Solvent Systems Used in PCL Electrospraying
### Solution Properties for Cone-Jet in PCL Electrospraying  
The physico-chemical properties of the electrospraying solution depend on the properties of the solvent system, i.e., (i) electrical conductivity, (ii) surface tension, (iii) viscosity, (iv) vapor pressure, and (v) dielectric constant. The control of these characteristics allows the formation of the Taylor cone, which leads to the production of particles having a low mean diameter and narrow particle size distribution.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & \(\delta_{D}\) & \(\delta_{P}\) & \(\delta_{H}\) & **Distance to PCL a** & **Solubility b** & **Reference** \\ \hline Styrene & 18.6 & 1.0 & 4.1 & 6.4 & NT & [108] \\ Anisole & 17.8 & 4.1 & 6.7 & 2.4 & good & [99] \\
1,4-Dioxane & 19.0 & 1.8 & 7.4 & 5.1 & good & [111] \\ Methyl salicate & 18.1 & 8.0 & 13.9 & 6.4 & good & [114] \\ Methyl formate & 15.3 & 8.4 & 10.2 & 5.8 & good & [115] \\
2-Chloroethanol & 16.9 & 8.8 & 17.2 & 9.9 & good & [115] \\ _o_-Xylene & 17.8 & 1.0 & 3.1 & 7.3 & good & [108] \\ Benzaldehyde & 19.4 & 7.4 & 5.3 & 4.4 & good & [108] \\
1,2-Dichlorobenzene & 19.2 & 6.3 & 3.3 & 5.4 & good & [112] \\ Acetyl chloride & 15.8 & 10.6 & 3.9 & 7.0 & good & [116] \\ Furan & 17.8 & 1.8 & 5.3 & 5.1 & good & [111] \\ Isopropanol & 15.8 & 6.1 & 16.4 & 9.4 & bad & [117] \\ Hexafluorooisopropanol & 14.17 & 2.41 & 13.89 & 10.0 & good & [118] \\ Trifluoroethanol & 15.9 & 9.7 & 17.8 & 11.2 & good & [119] \\ \hline \hline \end{tabular}  
* The distance from solvent to PCL is calculated taking in account the [PERSON]’s solubility parameters of PCL \(\delta_{D}=17.7\), \(\delta_{P}=6.2\) and \(\delta_{H}=7.8\) (temperature: 25 \({}^{\circ}\)C). b As for the solubility to PCL, good means the solvent can dissolve PCL completely; partial means the solvent can only dissolve PCL at lower concentration or lower molecular weight; bad means the solvent cannot solubilize PCL at any concentration and molecular weight; NT represents “not tested” or uncertain.  
\end{table}
Table 2: \(Cont.\)
# Storytelling with Image Data: A Systematic Review and Comparative Analysis of Methods and Tools
## 5 Task 3: Intelligent Narrative Discovery
### Image Collection Summarization
The first technique, termed image summarization through clustering, employs One-Class Support Vector Machine (SVM) clustering accompanied by outlier identification in the process of extracting representative images. The second technique, dubbed image summarization using an auto-encoder, employs an auto-encoder to assign a representative value to each picture in the collection and then creates a summary using photos with varying values. **Multimodal Image Collection. ** The phrase \"multi-modal image collection\" refers to a collection of photos that include other modalities such as video, text, and hyperlinks [109]. [PERSON] et al. [106] approached extractive multimodal summarization as a classification problem and proposed a text-image classification technique based on a multimodal RNN model. Their strategy encrypts words or phrases using hierarchical RNNs frameworks and the ordered image set using a CNN model and an RNN model, further determining the probability of sentences being selected and the likelihood of sentences being aligned using a logistic classifier with characteristics for text coverage, text duplication, photo set coverage, and photo set duplication. By merging the essential scores of words and the hidden sentence-image arrangement, two techniques are provided for computing the image set redundant feature. For event summarization, [PERSON] et al. [107] argued that it had been a contentious topic of how to make sense of this deluge of data in order to discover and forecast breaking events. Additionally, they highlighted that while the majority of existing systems focus on event recognition, event location estimate, and text-based summary, a tiny number of works have concentrated on event summarizing. Then, they designed a framework for event summarizing based on social media, consisting of three stages: (1) A coarse-to-fine filtering technique is used to reduce superfluous data. (2) A unique technique called User-Text-Image Co-Clustering (UTICC) is suggested for simultaneously discovering subevents from blogs that contain several media types--user, text, and image. (3) A multimedia event summarizing approach is used to find representative words and pictures, which are then combined to provide a holistic, visually represented summary of the incidents. [PERSON] et al. [108] investigated the idea of context-aware picture quality evaluation, in which the photo context is determined using clustering, and statistics from both the extracted context and the complete photo collection are utilized to assist the detection of low-quality photographs. They selected to show their concept by utilizing sharpness as the primary criterion for image quality. As such, it is proposed that a photograph be eliminated if its sharpness is insufficient in the scope of the collection. They also believed that, simultaneously, sharpness needs vary significantly according to content type and user purpose, necessitating context-aware customization.
# Routing Selection Algorithm for Mobile Ad Hoc Networks Based on Neighbor Node Density
## 3 Node-Density-Based Routing Method
### The Issues with ND-AODV  
ND-AODV still has limitations because it lacks information about the location of neighbor nodes, which can result in two types of network wastage.  
Figure 4: Flowchart depicting the route discovery process in the ND-AODV protocol.  
Two types of nodes are defined here, the receiving node as receiver and the sending node as sender.  
Scenario 1: In certain circumstances, when a sender is surrounded by an isolated receiver, meaning there are fewer common neighbor nodes between the sender and receiver, and the number of neighbor nodes around the receiver exceeds the threshold \"\(k\)\", and this isolated node is associated with the routing to the destination node, it may have an adverse effect on the discovery of the destination node, leading to an increase in network overhead, as illustrated in Figure 5. In this context, the red node represents the receiver, S represents the sender, the dashed lines represent the range of node transmission, and D represents the destination node. When the sender transmits an REEQ to the receiver, if there is an isolated receiver, and the number of neighbor nodes around this receiver exceeds the threshold \"\(k\)\", according to the conditions of the ND-AODV protocol, as shown by the red receiver in Figure 5, there is a certain probability of not forwarding the REEQ, making it challenging to discover the destination node D, and consequently further increasing network overhead. Therefore, this node is referred to as an isolated node.  
Scenario 2: In another scenario, there is an over-density of nodes in the vicinity, as shown in Figure 6, where the red, blue, and green nodes are all neighbor nodes of the sender S. If there are excessive receivers around the sender S (meaning there are many common neighboring nodes), based on the observation from the figure, the over-dense blue receiver and the sender S share many common neighbor nodes. Leveraging the characteristic of the ND-AODV protocol, which sacrifices the best route, the over-dense blue receiver can choose not to forward the REEQ control information, as indicated by the blue node in Figure 6. Specifically, when the sender transmits an REEQ to the receiver, if there is an over-dense receiver, meaning there are more neighbor nodes around both the sender and receiver than the threshold \"\(k\)\", these over-dense receivers may still forward the RREQ with a certain probability. This can lead to the redundant propagation of control information, resulting in the wastage of network resources. Therefore, this node is referred to as an over-dense node.  
Figure 5: Illustration of isolated receiver in ad hoc networks, isolated node.
# Schumann Resonances and the Human Body: Questions About Interactions, Problems and Prospects
###### Abstract  
(1) Background: The interaction between the human body and the Earth's magnetic field at Schumann resonances (SRs) is one of the important fundamental questions of science that continues to be studied. The aim of this study was to create a working theoretical foundation for the interaction of the human body with SRs. (2) Methods: Extensive research was carried out in the literature with the aim of comprehensively presenting data on SRs and creating a working concept of their interaction with the human body. (3) Results: General data on SRs, a theoretical basis for justifying their interaction with the human body, possible mechanisms of influence and research results on the functioning of human internal organs were presented. (4) An interaction of the human body with SRs exists and has been scientifically proven. This interaction has been studied most significantly between SRs and the human nervous system. SRs affect functional indicators of the cardiovascular system: heart rate and blood pressure. Studying the influence of SRs on the course and pathogenesis of non-communicable diseases is a promising direction. Low-frequency SRs decrease the risk of developing acute myocardial infarction, and there is a tendency for them to promote cases of chronic kidney disease. SRs are an important external natural factor influencing the human body.  
non-communicable diseases; Earth's magnetic field; Schumann resonance; magnetoelectrochemical theory of metabolism; concept of synchronization 1
## 3 Results and Discussion
### Statistical Data and Correlation between Examined Assays  
The results showed that at all tested assays differences among treatments were not considered to be significant. The total phenolic content indicated a strong correlation with antioxidant capacity of chamomile and sage aqueous extracts prepared at all temperatures, as described by [PERSON]'s correlation coefficient (_r_ = 0.90). The correlation is presented in Figure 2. In addition, there was a statistically significant relationship between the two assays, considering that the _p_-value was less than 0.05.  
Figure 1: Concentration of chamomile and sage aqueous extracts that causes 50% of _V. fischeri_ luminescence inhibition, presented as medians.  
Reportedly, there is a relationship between total polyphenols concentration and DPPH radical-scavenging activity in chamomile and sage teas (correlation factor 0.950) [27]. This correlation is also in line with [PERSON] et al., who studied antioxidant capacity and total phenolic content of chamomile and sage as infusions [28]. Other studies on herbal aqueous extracts also indicated that antioxidant activity was positively correlated with total phenolic content [5; 13; 18; 29; 30; 31; 32; 33]. However, according to [PERSON] et al., the total content of antioxidants of an herbal extract, including aqueous extracts of chamomile and sage, is not bi-univocally related to its antioxidant capacity, because the distinct components of the extracts can have very different antioxidant capacities [16]. Moreover, total antioxidant activity of tea herbs is linked not only to the presence of phenols, but also to the presence of other free radical scavenging compounds [34].  
In contrast, acute toxicity of herbal aqueous extracts is not correlated to their total phenolic content and antioxidant capacity. Similar results for herbal infusions previously indicated no association of bioluminescence inhibition either with total phenolic content or with antioxidant activity [5].
## 4 Results and Discussion
### Evaluation of CMIP5 Global Climate Models Used in the Analysis
#### 4.1.2 Maximum and Minimum Temperature  
An analytical exploration into the diverse climate models for maximum and minimum temperature offers an intriguing narrative, as summarized in Tables 3 and 4. These models, evaluated against several statistical measures, display a range of outcomes that echo the multifaceted reality of precipitation forecasting.  
the spectrum, HadCM3, with 2.982, stands close to the HADG-EM2-ES model, reflecting a higher degree of absolute error.  
For RMSE, the values range from 7.659 (INM-CM4 model) to 8.022 (HADG-EM2-ES model), indicating diverse degrees of prediction accuracy. In this context, CNRM-CM5 and MRI-ESM1 models present values of 7.768 and 7.791, respectively, situating themselves closely to the most accurate INM-CM4 model. In terms of NSE, the INM-CM4 model excels, with 0.253, while the HADG-EM2-ES model lags, with 0.181. Additionally, the MRI-ESM1 model's value stands at 0.227, which is close to the MPI-ESM-P model's performance, suggesting that the former model's prediction accuracy is commendable, relative to the mean of the observed data.  
Finally, the correlation coefficients showcase the varying degrees of linear relationship between the predicted and observed values. The INM-CM4 model exhibits the strongest correlation (0.515), contrasting with the relatively lower correlation (0.45) by the HADG-EM2-ES model. Similarly, the NorESM1_M model exhibits a correlation of 0.481, placing it midway in the spectrum of correlation coefficients among the models.  
This analysis underscores the inherent diversity of performance across climate models when forecasting maximum temperature, emphasizing the multifaceted nature of climate modeling and prediction. Through this detailed comparison of climate models, we further appreciate the spectrum of performance characteristics across models, all of which contribute to our overall understanding of climate prediction mechanisms.
# A Microservice-Based Smart Agriculture System to Detect Animal Intrusion at the Edge
## 4 Proposed System
### Proposed Algorithm  
We propose and deploy an algorithm (as shown in Algorithm 1) on the fog server to predict the future location of intrusive animals based on the previous readings returned by the sensors. Whenever a sensor detects an animal, it sends the data back to the container running the algorithm on the fog server in the format of \"#side-#sensor-timestamp\". The data received by the container are combined with the previous record to make a prediction. The data here may have two scenarios: one is the data are read back from a non-overlapping coverage area; the other is the animal is in the overlapping area covered by multiple sensors. In this case, there will be multiple sensors with similar timestamps to send back data and the server needs to make the final prediction after receiving data from all these sensors. We use a \"tolerance time difference\" to define this similar timestamp. In addition, we need to define another threshold as the minimum time interval between animal intrusions, i.e., if the server does not receive new data within the amount of time, the next data received are considered to be a new animal intrusion. It should be determined by the number of animals within the vicinity and their appearance frequency around the field. We define a mapping of sensor numbers and position coordinates in the algorithm. The server first converts the sensor number in the received data into a coordinate and combines the previous set of coordinates to calculate the distance and direction, and then to calculate the average speed of the animal's movement with the timestamp difference. With the direction and speed, the next position of the animal can be predicted under the assumption that the animal will move in the same direction with the same speed for a short period of time. This period is the sum of the time it takes for the sensor to return data, the time it takes for the algorithm to make the prediction, the time it takes for the instruction to be passed from the server to the Raspberry Pi, and the time it takes for the camera to rotate to point to the predicted position. The direction and speed of animal movement are not stable, but the constant detection and updating of position information by the sensors, the fast transmission of LoRa, and the high-speed calculation of the system can make the predicted deviation be calibrated quickly and continuously. The field of view of the camera can also provide a certain degree of tolerance. Taken together, our proposed algorithm is expected to effectively and accurately locate and predict the location of animals. Next, we evaluate and verify the adequacy of the algorithm through experiments.