esa-sceva/satcom-chunk-collection huggingface dataset:
# A High Thermal Conductivity of MgO-H\({}_{2}\)O Nanofluid Prepared by Two-Step Technique
## 2 Experimental Details
### Measurements of Thermophysical Properties  
With varying volume concentrations of 0.05, 0.1, 0.15, 0.2, and 0.25 vol. percent, the thermal conductivity and dynamic viscosity of MgO-DW nanofluid were measured. They were also measured with various ultrasonication treatment times of 45, 90, 135, and 180 min. The thermal conductivity was tested using a Sweden company device (Hot Disk TPS 500 S, KJITALEY, Johanneberg Science Park, Goteborg, Sweden), which is a type of thermal constants analyzer that uses the standard isotropic method. The dynamic viscosity measurements were carried out using a viscometer, Model HTD13145 Six-Speed Rotational Viscometer (Qingdao Haidongda Special Instrument Co., Ltd., Qingdao, China). The thermal conductivity and dynamic viscosity measurements were both achieved at room temperature. The enhancement ratio of thermal conductivity values of MgO-DW nanofluid can be estimated using the following relationship [71]:  
\[\text{TCE\%}=\frac{\text{K}_{\text{nf}}-\text{K}_{\text{bf}}}{\text{K}_{ \text{bf}}}*100 \tag{3}\]  
where \(\text{K}_{\text{nf}}\) and \(\text{K}_{\text{bf}}\) are the thermal conductivity of the nanofluid and the base fluid, respectively.
# Smart Agriculture Applications Using Deep Learning Technologies: A Survey
## 4 Applications of Deep Learning in Agriculture
### Crop Identification/Classification
In [47], the faster region-based convolutional neural network (FRCNN) framework was designed to produce a plant detection model and estimate plant densities across a UAV orthomosaic. The results show that accurate two-dimensional maps of plant density can be constructed from UAV imagery highly correlated to essential yield components.
## 3 Results and Discussion
### Principal Component Analysis (PCA)  
Two principal components were identified, which explain 95.5% of the variability. The first component (PC1) explains 63.19%, including negatively correlated content of anthocyanins, color parameters a* and b* and positively--lightness L*. While the second component (PC2) explains 32.31% of the variation. PC2 was negatively correlated with phenol content and pH value (Figure 4). The PCA score plot indicates the beneficial effect of the by-product addition to both dried green tea leaves and hibiscus flowers on the content of phenolic compounds (Figure 5). The highest content of the tested bioactive compounds was obtained in infusions of green tea leaves with the addition of chokeberry pomace.  
Figure 4: PCA loading plot.  
Figure 3: Influence of dried pomace addition to green tea leaves or hibiscus flowers on the total phenolic compound content in infusions (the different letters above the bars indicate a significant difference at \(p<0.05\) for the two-way ANOVA and Tukey post hoc test. GTL—dried green tea leaves (0.5 g); WHF—dried white hibiscus flowers (0.5 g); RHF—dried red hibiscus flowers (0.5 g); CP—dried chokeberry pomace; 2.0, 2.5 and 3.0—indicate pomace addition in g.
## 5 Cybersecurity in MGs
### Security Vulnerabilities in MGs  
Cybersecurity vulnerabilities in MGs are similar to those found in large-scale energy grids. However, due to the unique characteristics of MGs, they require different attention. MGs are mainly based on RESs. Therefore, the devices used for energy generation, storage, and distribution are manufactured by different vendors and are not compatible with each other [196; 197]. This can lead to cybersecurity vulnerabilities in MGs as cyberattackers may exploit the weaknesses of these devices.  
The vulnerabilities of MGs are generally caused by factors such as inadequate security measures, lack of software and hardware updates, weak authentication, incorrect configuration, faulty software coding, and improperly separated networks [198]. Cybersecurity vulnerabilities allow a cyberattacker to take control of devices, gain unauthorized access to systems, install malware, and monitor network traffic. This can cause interruptions in the energy production and distribution processes of MGs and create security risks for individuals [199]. Table 9 provides a brief description of the categories and explanations of significant vulnerabilities encountered in MGs.
# Effect of Soil Washing with Ferric Chloride on Cadmium Removal and Soil Structure
## 2 Materials and Methods
### Measurements  
The measure method of soil particle size distribution was formulated form [PERSON]'s research [17]. The air-dried soil was sifted through a sieve of 0.60 mm. To obtain a homogeneous soil sample of specific amount, the quartering method was used. From the prepared fine soil, 0.5 g was weighted and added to 10 mL of 1 mol\(\cdot\)L\({}^{-1}\) sodium hexametaphosphate. Dispersing time was 24 h. Before the measurement, the samples were exposed to ultrasound for 10 min. The prepared sample was analyzed in a laser particle size analyzer (Mastersizer 2000, Malvern, UK).  
The concentrations of total Cd and Al were determined via ICP-MS (Thermo iCAP RQ, Waltham, MA, USA) [18]. The dissolved Fe(II) concentration in the solution was determined using a UV/visible spectrophotometer (Agilent Cary 60, Santa Clara, CA, USA) at 510 nm [19].  
The speciation of Cd was analyzed using the Tessier sequential extraction procedure [20]. For this, five defined fractions were sequentially extracted, i.e., exchangeable Cd (Exch), Cd bound to carbonates (Carb), Cd bound to iron and manganese (Fe-Mn) oxides, Cd bound to organic matter (Org), and residual Cd (Re).  
The TOC and DOC were determined using a TOC analyzer (Multi N/C 3100, Jena, Germany) [21, 22], while the X-ray diffraction (XRD) patterns of the soil samples were obtained using Rigaku Smartlab with a Cu K\(\alpha\) radioactive source (1.54059 A). Continuous scans from 10\({}^{\circ}\) to 80\({}^{\circ}\) 20 were collected at a scanning rate of 5\({}^{\circ}\cdot\)min\({}^{-1}\) following [PERSON]'s study [23].
# An Enhanced Fusion Strategy for Reliable Attitude Measurement Utilizing Vision and Inertial Sensors
## 5 Conclusions  
An enhanced data fusion strategy based on RBF and CKF has been developed for attitude estimation in a vision and inertial integrated system with consideration of sampling frequency discrepancy and divergence. By integrating the technique of RBF into the normal filtering framework of CKF, the proposed reliable attitude measurement not only can reduce the problem of divergence inherent in the intersampling of slow vision data when calculated by only inertial data but also can ensure the stability of the high frequency output of attitude angles. More importantly, in contrast to the existing compensation techniques that only rely on the past information of single information, the present scheme makes full use of the past information of inertial and vision information. Comparisons and extensive experiments under different scenarios illustrate that reliable attitude estimation performance and enhanced convergence capability are achieved by the proposed approach.  
Methodology X.G.; Software, H.Z., C.S. and X.G.; Validation, C.S.; Writing--original draft, H.Z., X.C. and C.S.; Writing--review & editing, H.C., D.Z., H.H. and X.G.  
This work was supported in part by the National Natural Science Foundation of China (61603353, 51705477, 51705021, U1764261), and the Pre-research Field Foundation (6140518010201) and the Scientific and Technology Innovation Programs of Higher Education Institutions in Shanxi (201802084), the Shanxi Province Science Foundation for Youths (201601D021067), the Program for the Top Young Academic Leaders of Higher Learning Institutions of Shanxi, the Young Academic Leaders Foundation in North University of China, Science Foundation of North University of China (XJ)201822), and the Fund for Shanxi \"1331 Project\" Key Subjects Construction, National Natural Science Foundation of China (61703098), and Natural Science Foundation of Jiangsu Province (BK20160699).  
[PERSON] and [PERSON] have the equal contribution to this work.  
The authors declare no conflict of interest.
## 5 Machine Learning (ML) Techniques
### Multilayer Perceptrons (MLPs)  
MLPs are an artificial neural network architecture composed of multiple layers of neurons, which are basic processing units. These networks are designed to solve complex classification and regression problems and are known for their ability to handle nonlinear data [47].  
The main characteristic of MLPs is the presence of one or more hidden layers, situated between the input layer and the output layer. Each layer consists of interconnected neurons, where the connections between neurons have associated weights. These weights are adjusted during the network training process to optimize performance and enhance accuracy [47].  
MLPs are capable of learning from examples provided during training. Data propagation occurs directly from the input layer to the output layer through a weighted linear combination of input values and synaptic weights. Subsequently, the output undergoes a nonlinear activation function, introducing nonlinearity into the network and enabling it to learn complex relationships among the data.  
During the training process, synaptic weights are adjusted using learning algorithms such as back-propagation, which calculate the error between the network outputs and expected values, and iteratively update the weights to minimize this error. Once the network has been trained, it can be used to make predictions or classify new input examples. Through the process of forward propagation, the network performs a series of mathematical operations and neuron activations, processing the input data and generating a final output [47].  
MLPs are widely used in areas such as pattern recognition, natural language processing, computer vision, and many other applications involving analysis and learning from complex data. Their ability to learn and generalize from examples makes them powerful models for classification and regression problems.  
Many tests were conducted considering different ANN topologies to determine the best network. The test considered neuron numbers from 1 to 30 in the hidden layer, and for each neuron in the hidden layer the test was repeated for 100 different random seeds. Finally, the topology with one hidden layer with 24 neurons yielded the best accuracy, as seen in Figure 5.  
Figure 6 represents the MLP neural network modeled for this research, in which the input layer has three neurons, which represent the distance between the transmitter (Tx) and the receiver (Rx) in meters (m), the heights of the Tx, 6, 12, and 18, in meters (m) and the co-polarizations of the transmitting and receiving antennas (VV and HH), with sigmoid function in the hidden layer and linear activation function for the output layer.
# Image Degradation Modeling for Real-World Super Resolution via Conditional Normalizing Flow
## 3 Materials and Methods  
### Image Degradation Formulation  
Given an HR image \(\mathbf{I}_{HR}\), the corresponding LR observation \(\mathbf{I}_{LR}\) is generally assumed to follow the image degradation as follows:  
\[\mathbf{I}_{LR}=(\mathbf{I}_{HR}*\mathbf{k})\downarrow_{s}+\mathbf{n}, \tag{1}\]  
where \(\downarrow_{s}\) denotes a down-sampling operation, \(*\) denotes a convolution operation, and \(\mathbf{k}\) and \(\mathbf{n}\) denote blur kernel and random noise, respectively. However, most existing SR methods assume a simple and uniform degradation, which utilizes a fixed blur kernel (e.g., Bicubic kernel) to generate HR-LR images for SR model training. In real-world scenarios, it is a challenge to manually model the image degradation process, which is usually susceptible to a combination of sensor noise and post-processing artifacts. Thus, the degradation process can be expressed as  
\[\mathbf{y}_{\mathbf{n}}=\mathbf{y}+\mathbf{n}=f_{b}(\mathbf{x})+\mathbf{n}, \tag{2}\]where \(\mathbf{y_{n}}\) and \(\mathbf{y}\) denote the corrupted noisy LR image and its clean version, \(\mathbf{x}\) denote the HR image, and \(f_{b}\) denotes the blur degradation function.  
Considering the stochasticity of image degradation, we take IDM as the problem of learning a conditional probability distribution of degraded LR images given the HR input. We propose a conditional normalizing flow based method named IDFlow to model image degradation and learn the distribution \(p_{y_{\mathbf{n}|x}}(\mathbf{y_{n}|x})\) from existing real-world SR datasets. As shown in Figure 1, we decompose the IDM process into two steps: blur degradation modeling (BDM) and real-world noise modeling (RNM). Blur degradation modeling aims to model the degradation process from \(\mathbf{x}\) to \(\mathbf{y}\), while real-world noise modeling aims to model the noise addition process.
# Secure Cognitive Radio Vehicular Ad Hoc Networks Using Blockchain Technology in Smart Cities
## 4 Performance Evaluation
The latter [3] considers cognitive technology in a vehicular environment to deliver a packet from a source node to a destination node only if a channel is available between the two. Figure 5 shows the delivery time as a function of the number of miners. We can see that increasing the number of miners in the network can reduce the time it takes to deliver a message to the destination. There are two reasons for this. One is the increase in connectivity in such a highly dynamic network, where more miners can deliver it more quickly. The second factor is that the larger the number of miners, the less time it takes to validate message authenticity, which results in faster message delivery. Since the reference blockchain scheme only considers a safe route for any customer using an app to reach the destination, it takes a longer time to deliver the message when miners are added. However, for the reference cognitive scheme, miners are just relays that take longer to deliver messages due to sensing the channel at every link. The same pattern can be seen in Figure 6, where the overhead for our scheme is low compared to the other two schemes. For the evaluation of overhead, we considered a reference blockchain scheme [20] as the one that provides a secure route by collecting information from all vehicles and providing it to a fog node. With the increase in nodes in the network, the exchange of messages increases, which increases the network overhead. However, our scheme outperforms the two because we consider RSU to be responsible for authenticating the nodes and PU to be responsible for providing channels. Once these two entities complete their task, the nodes are free to start communication and deliver data as soon as possible to receive their rewards. Figures 7 and 8 depict the delivery time and overhead as a function of the number of miners, respectively, when all vehicles are moving at random speeds between 10 m/s and 15 m/s. Our pattern is nearly identical to the one we produced in Figures 5 and 6. It can be concluded that vehicles moving at constant speed take longer to reach their relevant destination, as evidenced by a slight decrease in delivery time. However, when vehicles move at a random speed, a high-speed miner is able to deliver packets quickly, leading to a shorter delivery time. There has also been a slight decrease in overhead due to the same reasons. The longer it takes to transmit the message, the more overhead the network experiences due to the exchange of more control packets to reach the destination. To summarize, our proposed scheme exhibits a remarkable balance between the resource-intensive nature of blockchain technology and the rigorous efficiency requirements of vehicular networks. Figure 5: Performance comparison of delivery time in terms of number of miners. Figure 8: Performance comparison of overhead when speed is random. Figure 6: Performance comparison of overhead in terms of number of miners. Figure 7: Performance comparison of delivery time when speed is random.
# Prediction of Belt Drive Faults in Case of Predictive Maintenance in Industry 4.0 Platform
## 3 Research Area
### Results  
In this study, the performance of the algorithm was verified on data separate from the training set, i.e., data that the algorithm did not process in the learning process. Test data samples were provided for the efficient belt transmission, as well as for the individual failures investigated in this experiment. First, data from a sensor placed horizontally with respect to the belt transmission were input to the proposed algorithm and the learning process of the algorithm was performed to match the data from the undamaged transmission.  
Based on the results obtained, the density distribution of the mapping error of the undamaged values was determined (Figure 7). The largest error value obtained was 1.51%. In the next step, data from the damaged devices were input to the learned autoencoder. The minimum value of the mapping error was determined, which was 1.75%, and the distribution of the mapping error density for the damaged values (Figure 7). Determination of the maximum error of the correct values allowed us to draw a cutoff curve of the undamaged class from the damaged class (red line Figure 7). The mapping error value above which the autoencoder algorithm considered the input signal as anomalous was 1.48%. Finally, the F1 value for the classification test was determined to be 98.89%.  
In the second step, the steps were repeated for a sensor placed vertically to the belt transmission. The maximum value of the mapping error in the \"undamaged\" class was determined, which was 1.8%, and the minimum value of the mapping error, regardless of the type of damage for the damaged object class was 1.85%. The error value separating the damaged and undamaged classes was 1.79% (Figure 8). The F1 value for the classification test was determined to be 99.33%.  
During the design stage, several autoencoder structures were analyzed, modifying the number of neurons at the input and in the hidden layers. Increasing the number of neurons did not increase the accuracy of the proposed solution, reducing the number of neurons by half, i.e., creating a structure of 8-4-2-2-4-8, the classification efficiency decreased by 4 percentage points. Based on the obtained results, it should be concluded that the proposed approach detects introduced anomalies into the tested device with very high efficiency. As  
Figure 7: Distribution of autoencoder mapping errors for each damage-related class for horizontally positioned sensor.  
it was proven on the basis of conducting tests, the classification accuracy is not less than 98%. Table 1 provides a summary of the results obtained from the research conducted.
# QoE Optimization in a Live Cellular Network through RLC Parameter Tuning
## 2 Radio Link Control
### Unacknowledged Mode  
As mentioned above, the functionalities of RLC are executed by entities. For the communication to be established, each RLC entity at the transmitter must have a peer at the receiver. When the RLC layer is in the UM operation mode, these entities are called the transmitting UM RLC entity and receiving UM RLC entity, respectively; see Figure 1.  
The transmitting UM RLC entity has a transmission buffer in which the RLC SDUs that it receives from the PDCP layer are stored. When the MAC layer observes a transmission opportunity, the transmitting UM RLC entity performs the segmentation and reassembly of the RLC SDUs stored in the transmission buffer, obtaining UM data (UMD) PDUs with an appropriate size to be sent by the MAC layer. Before sending these PDUs, the transmitting UM RLC entity adds an RLC header to the UMD PDUs. The header is used by the receiving UM RLC entity to reorder the UMD PDUs and for error detection tasks.  
The receiving UM RLC entity reorders and performs the segmentation and reassembly of the UMD PDU that it receives from the MAC layer; then, it sends the resulting RLC SDUs to the PDCP layer. The UMD PDUs that could not be reordered due to some error detected in the RLC SDUs are discarded. To control how often the reordering and delivery of the packets to the PDCP layer are performed, the receiving RLC UM entity has a timer called t-Reordering. When this timer expires, the receiving RLC UM entity updates its status variables, removes the UMD PDU headers, reassembles the UMD PDUs to obtain RLC SDUs, and delivers them in an orderly manner towards the PDCP layer.
## 3 Results and Discussion
### Microstructural Investigations
#### 3.2.3 Topography of the Cavitated Surfaces  
In Figure 10, SEM images are shown of the surface topography of specimens that underwent stress-relief annealing and were then subjected to ultrasonic cavitation for 15 min, 60 min, and 165 min. From these images, it can be seen that at short cavitation exposure (15 min, Figure 10a), initiation of small pits occurs along the boundaries between the crystalline grains, where the fragile intermetallic phases are located. As erosion progresses, the attack continues along the twin boundaries and the grain boundaries of the \(\gamma\) solid solution matrix (Figure 10b). Due to their brittleness, these phases are less capable of absorbing the deformation energy caused by the stresses induced in the material by cavitation impact waves and, consequently, deteriorate more rapidly than the \(\gamma\) solid solution grains with an FCC crystal lattice.  
This image demonstrates that the material-removal mechanism is governed by plastic deformation, accompanied by the formation of slip steps characteristic of the \(\gamma\)-matrix.  
By the end of the cavitation test (165 min, Figure 10c,d), the fractured surface exhibits ductile features, typical of face-centered cubic (FCC) materials. Some zonal variations in cavity depth are observed (exceeding 18 \(\upmu\)m), and evidence of local fatigue loading is highlighted by the formation of characteristic deformation lines (Figure 10d). These observations are in agreement with studies of the degradation mechanisms in austenitic metallic alloys [4; 5; 6; 12; 19].  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Element** & **Series** & \begin{tabular}{c} **Mass C.** \\ **(wt.\%)** \\ \end{tabular} & \begin{tabular}{c} **Norm. C.** \\ **(wt.\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Atom C.** \\ **(at.\%)** \\ \end{tabular} &
\begin{tabular}{c} **Error (1 Sigma)** \\ **(wt.\%)** \\ \end{tabular} \\ \hline Al & K series & 0.20 & 0.21 & 0.29 & 0.05 \\ \hline Si & K series & 0.14 & 0.15 & 0.23 & 0.04 \\ \hline Ti & K series & 0.29 & 0.31 & 0.40 & 0.04 \\ \hline Cr & K series & 19.09 & 19.22 & 22.59 & 0.57 \\ \hline Mn & K series & 0.12 & 0.12 & 0.22 & 0.04 \\ \hline Fe & K series & 4.26 & 4.47 & 4.96 & 0.15 \\ \hline Co & K series & 0.08 & 0.09 & 0.13 & 0.06 \\ \hline Ni & K series & 60.26 & 62.14 & 64.26 & 1.47 \\ \hline Nb & K series & 2.14 & 3.62 & 1.26 & 0.11 \\ \hline Mo & K series & 6.93 & 7.28 & 4.68 & 0.47 \\ \hline Ta & L series & 1.01 & 2.39 & 0.98 & 0.10 \\ \hline  & Total & 97.98 & 100 & 100 \\ \hline \end{tabular}
\end{table}
Table 4: Mass and atomic concentrations of the elements in the chemical composition.  
Figure 10: _Cont._