esa-sceva/satcom-chunk-collection huggingface dataset:
# Temperature Prediction of Chinese Cities Based on GCN-BiLSTM
## 4 Results and Discussion
### Discussion
The GCN-BiLSTM model has the optimal performance for temperature prediction in Guangzhou, with an RMSE of 1.485 \({}^{\circ}\)C, which is 22.09% lower than the average. The worst performance for Jinan is 2.337 \({}^{\circ}\)C, which is 22.61% higher than the average. As shown in Figure 10, in terms of MAE, the GCN-BiLSTM model performs best for temperature prediction in Guangzhou. In Jinan and Harbin, the MAE is high, and the GCN-BiLSTM model performs well. Figure 11 depicts the temperature trends in the four cities of Harbin, Jinan, Hangzhou, and Guangzhou during the five-year period from 1 May 2014 to 1 May 2019. As shown in Figure 11, the temperature difference in Guangzhou is small throughout the year, about 24 \({}^{\circ}\)C to 26 \({}^{\circ}\)C due to its low latitude, its proximity to the South China Sea, and its subtropical maritime climate. Therefore, the prediction accuracy in Guangzhou is better than in other cities. However, in Harbin and Jinan, the temperature fluctuates throughout the year, with a temperature difference of about 35 \({}^{\circ}\)C. The prediction accuracy of the model is lower than in other cities, with more slight fluctuations. Figure 8: Comparison of MAE of temperature prediction for the next 14 days. study area and with fewer neighbor nodes (including first-order and second-order nodes) have higher prediction errors, such as Suqian and Xuzhou. Cities with more neighbor nodes have smaller prediction errors, such as Yangzhou and Taizhou. GCN is employed to extract the spatial features of the city's topological maps in our model. If there are more neighbor nodes in the target region, the more features used to extract spatial correlation, the more accurate the prediction will be. However, when the number of neighbor nodes exceeds a certain threshold, the prediction effect does not improve significantly, such as in Yancheng. We consider that when there are too many neighbor nodes, it will bring additional noisy data to affect feature extraction. In summary, the GCN-BiLSTM model shows the best performance in temperature prediction among the above four models. The model has significant spatial performance differences and is influenced by the meteorological factors of the surrounding areas. The The GCN-BiLSTM model has higher accuracy in cities with stable temperature fluctuations and slight temperature differences than in cities with sharp temperature fluctuations and significant temperature differences.
# Webcam Eye Tracking for Monitoring Visual Attention in Hypothetical Online Shopping Tasks
## 3 Materials and Methods
### Software and Hardware  
The cloud-based software EYEVIDO Lab was employed to set up and run the experiment. The software enables collaborative online creation and analysis of screen-based eye tracking studies and offers additional features such as embedding questionnaire elements. The software's cloud architecture also allows simultaneous online data collection from multiple participants. The typical workflow of a cloud eye tracking study with EYEVIDO Lab is outlined in the following. First, a study is created in the web portal, i.e., the stimuli and accompanying questions, if any, are entered, and the areas of interest (AOIs) are set according to the hypotheses. Subsequently, the study is launched. The participants take part in the study via the recording tool (tester software), which must be downloaded to the participants' computer beforehand. The tester software identifies the eye movements of the participants, stores all inputs, records the screen content, and transmits all data encrypted to the server of the EYEVIDO GmbH via the internet. This means that participation can take place from any location, as long as an Internet connection is available. The investigator can view and analyse the results at any time in the web portal and export reports in CSV format. Reports can be generated for each individual participant as well as across participants. The reports contain numerical information based on fixations within the defined AOIs, such as number and percentage of fixations, fixation duration or number of visits within an AOI. The across-participant report additionally shows the number of participants who had at least one fixation within an AOI. Visualisations (heatmaps, gazeplots) can also be generated and exported [31].  
The software can be used in conjunction with both infrared eye trackers and web-cams [31]. Since our goal was to address the question of whether webcam eye tracking could be suitable for analysing users' visual attention while visiting online shops, we used only the webcam function in this study for tracking the participants' gaze behaviour. All participants used their own equipment, i.e., their own computers and webcams, for participating in the study.
## 3 Modeling of a Transmission Tower with a PTMD  
### Structural Model  
A SZ21-type transmission tower practical engineering example in Northeast China was selected as the research object. Figure 2 shows the practical graph of the transmission tower. The height of the tower is 53.9 m, and its weight is 20.23 tons. The tower size is shown in Figure 3. The main member and diagonal members of the transmission tower are made of Q235 and Q345 angle steels with elastic moduli of 206 GPa. A three-dimensional finite element model of the power transmission tower was established by using ABAQUS (Dassault Simulia Company, Providence, RI, USA), as shown in Figure 4. The \(X\), \(Y\), and \(Z\) directions of the model are expressed as the longitudinal, transverse, and vertical directions of the structure, respectively. The members of the transmission tower are simulated by B31 elements, and the base nodes of the transmission tower are fixed at the ground. Based on the analysis of the dynamic characteristics, the frequencies in the \(Y\) and \(X\) directions of the transmission tower are analyzed. The first three natural frequencies in the \(Y\) direction are 1.768, 4.870, and 8.909 Hz, while the first natural frequencies in the \(X\) direction are 1.797, 4.954, and 9.774 Hz. The vibration modes that shape the transmission tower are shown in Figure 5.  
Figure 4: Three-dimensional finite element model of the transmission tower.  
Figure 3: Tower size (m).  
Figure 2: Practical graph of the transmission tower.
## 3 Methodology
### Extraction Layer
#### 3.2.2 Semantic Feature Extraction  
Attention mechanism is a common way to capture the interactions between the aspect and context words. However, the attention mechanism is easily disturbed by noise (other irrelevant aspects of words), and as clues, misjudge the sentiment polarity of the related aspects. Therefore, we use aspect-aware attention to learn local semantic information for a specific aspect, while using self-attention to learn global semantic information for sentences. After that, we fuse local attention with global attention to learn semantic correlation.  
1. Local attention: To enhance the attention of specific aspects to local contextual sentiment information, we use aspect-aware attention to prevent disturbance with other aspects of word information. The aspect-aware attention mechanism utilizes the aspect term as query conditions to calculate the attention feature information of related aspects, \[A_{local}^{i}=\text{tanh}\bigg{(}H_{a}W^{a}*\left(KW^{K}\right)^{T}+b\bigg{)}\] (5) where \(K\) is equal to the output \(H\) of the input layer, and \(W^{a}\) and \(W^{K}\) are learnable weights. We perform mean pool operation on output \(H\) and copy the processed output n times as \(H_{a}\).
2. Global attention: The attention mechanism captures the semantic correlation between any two words in a sentence. This is useful for grasping all of the semantic information in a sentence. Therefore, we use the multi-head attention mechanism [27] to construct the global semantic score matrix \(A_{global}^{i}\) of the sentence. The calculation process is as follows, \[A_{global}^{i}=soft(\frac{QW^{Q}*\left(KW^{K}\right)^{T}}{\sqrt{d}})\] (6) where \(W^{Q}\) and \(W^{K}\) are learnable weights Then, we combine the local attention score with the global score to obtain semantic matrix \(A_{sem}\): \[A_{sem}=A_{global}+A_{local}\] (7)
## 4 Results
### Input Feature Importance Evaluation  
The average feature importance of the top 15 features from 2009 to 2016 is given in Figure 10, and the feature importance values of each year are shown in Figures S4 and S5. Overall, the results indicate that \(\rm NIR_{V}\) was the most important feature to predict rice yield among eight factors, demonstrating that \(\rm NIR_{V}\) was superior to other vegetation indices in predicting crop yield. Moreover, Srad and Tmax had higher feature importance values than other climatic variables. In addition, intervals 6 and 12 had high values of feature importance among the 12 intervals in each year.
## 4 Discussion
### Representativeness of Sites and Regions  
This study utilized carbon flux data from a total of 280 flux tower sites worldwide to validate the data. These sites cover all the seven climate zones and various land cover types in all continents except Antarctica (Table 2). However, it should be noted that the distribution of these sites is not uniform across regions. The number and density of sites are much higher in North America and Europe than in other parts of the world. As a result, site-level validation results are expected to be more accurate and representative within these respective continents. In terms of climate zones, the continental climate zone has the highest number of sites, while the remaining climate zones consist of a similar number of sites ranging from 19 to 33. Concerning land cover types, the majority of sites are located in grassland, savannah, and forests, while only a few sites are located in unvegetated land and cropland. Therefore, it is necessary to gather additional in situ carbon flux data by establishing more flux tower sites in regions where site density is sparse in the future.  
Figure 11: Interannual graphs of global carbon fluxes for each product: (**a**) GPP, (**b**) RECO, and (**c**) NEE.
# Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications
## 3 Use Cases and Applications of 6G
While uHSLLC provides ultra-high speeds and very low latency, uHDD meets the criteria for data density and excellent dependability. Additionally, upcoming uMUB, uHDD services, and uHSLLC need end-to-end co-design of communication, sensing, and computing capabilities. In order to enhance 5G use cases, three new use cases are upgraded, as 5.5G, to the main three 5G use cases, which are real-time broadband communication (RTBC), uplink centric broadband communication (UCBC), and harmonized communication and sensing (HCS) [41]. As Huawei's vision and mission, these three cases will lead beyond the Internet of everything. (IoE), enabling intelligent IoE. The comparison of the key scenarios of 5G, 5.5G, and 6G is shown in Figure 1. Sixth generation is expected to inherit some of the features and technologies of 5G, evolve some of the current transmission technologies based on future demands, and revolutionize some technologies based on the demanded use cases. Figure 1: Comparisons of use cases for 5G and 6G systems. * Note 1: Ref. [40]. To realize the above applications on a large scale, a network with extremely high data rate, reliability, and power efficiency is required. The next section considers the 6G requirements in detail.
## 2 Theory and Methods  
In order to analyze the influence of floods on GNSS MP and CNR signals, the theory of the relationship between floods and multipaths, and the relationship between multipaths and the CNR signal is given first. In addition, the pseudo-range multipath combination model of the GPS satellite is presented.
## 3 Results and Analysis
### Nonlinear Finite Element Analysis
#### 3.4.3 Comparative Analysis of Load-Displacement Curve Results  
The comparison of the load-displacement curves obtained from the finite element analysis of the three specimens and those obtained from the tests are shown in Figures 18-20, respectively. From the analysis of the figure, it can be seen that from the load-displacement curve as a whole, the two trends were generally consistent. Due to the discrete nature of the material itself and the different test methods, as well as the error during the test and the different values of the parameters during the finite element analysis, there are still some differences between the experimental results and the finite element analysis results. However, the overall trend can still be consistent with the experimental results, it indicates that the finite element simulation analysis can be used to better guide the test in the pre-test of future frames.  
Figure 17: The diagram of KJ-3 result analysis. (**a**) Stress contour of frame under the ultimate load. (**b**) The X displacement contour of frame under the ultimate load. (**c**) Cracks of frame under the ultimate load.  
Figure 18: The comparison between the KJ-1 curve and the curve of the finite element analysis.
# An Improved Multi-Sensor Fusion Navigation Algorithm Based on the Factor Graph
## 4 Information Fusion Method Based on the Factor Graph
### Factor Graph Formulations  
The probability graph model is a kind of graph model which can express the joint probability distribution of random variables. The factor graph is a bipartite graph representing the factorization structure of a multivariate function into a product of functions (factors), each involving only a subset of the variables [26]. There are two kinds of nodes in the graph. The variable node \(X\) represents the variable of the global multivariate function, and the factor node \(F\) represents the factor in the factorization. The variable node is connected to a factor node by an edge \(E\)[27]. The factor graph is defined as  
\[G=(F,X,E)\]where \(X_{i}\subseteq\{x_{1},x_{2},\cdots,x_{m}\}\) is the subset of the variable nodes \(x_{j}(j=1,2,...,m)\), \(m\) is the number of the variable node, \(F=\{f_{1}(X_{1}),f_{2}(X_{2})...,f_{n}(X_{n})\}\) represents a set of the factor nodes \(f_{i}(X_{i})(i=1,2,...,n)\), and \(n\) is the number of the factor node. Edges \(E=\left\{e_{ij}\right\}\) denotes the set of links of the factor graph, which can exist only between factor nodes and variable nodes. The factor graph \(G\) defines one factorization of the function as  
\[g(X)=\prod_{i}f_{i}(X_{i}) \tag{10}\]  
A factor graph can be used as a probabilistic graphical model which represents a joint probability mass function of random variables. Each variable node represents a set of the random variables. Each factor represents the joint probability function of a subset of random variables, which is shown as a box or a circle. For example, Figure 2 can be factored as  
\[g(u,w,x,y,z)=f_{1}(u,w,x)f_{2}(x,y,z)f_{3}(z) \tag{11}\]  
The factor vertex \(f_{1},f_{2},f_{3}\) are the local functions. Due to the variable vertex \(u\), \(w\), \(x\) are connected to \(f_{1}\), the factor vertex \(f_{1}\) involves the independent variables \(u\), \(w\), \(x\). In a similar way, the factor vertex \(f_{2}\) involves the independent variables \(x\), \(y\), \(z\). The factor vertex \(f_{3}\) involves the independent variable \(z\).
## 4 Experiments and Analysis  
Based on the ARDU denoising model in Figure 1, the basic experimental flow framework is shown in Figure 5. The experimental process mainly included three steps:  
Figure 5: Basic experimental flow framework.  
(1) Data preprocessing. The data were preprocessed with cropping, correction, translation, inversion, filtering, and signal and noise synthesis, and a training set, validation set, and test set were created.  
(2) Model training, validation, and testing. The training set was used to train the model, learn the features of the seismic signal and noise, and fit the model parameters. The validation set was used to validate the model, adjust the hyperparameters of the model, and preliminarily evaluate the denoising performance of the model. The test set was used to test the denoising performance of the ARDU model and verify the feasibility of the denoising model.  
(3) Comparison of denoising effects of different algorithms. The test set data were used to compare the denoising effect of the ARDU model and other denoising methods, and the SNR, \(r\), and RMSE were calculated to evaluate the denoising performance of the model.
## 3 Results
XRD data from a range of experimental temperatures spanning from 100 \({}^{\circ}\)C to 200 \({}^{\circ}\)C are shown in Figure 4. At lower temperatures, a variety of side products can be seen in the XRD data, including LuF3 and LiLuO2. Unreacted or undissolved LiF can also be seen in many of the XRD patterns. A stable, single phase of Yb:LLF is not reached after 24 h unless the standard reaction temperature of 180 \({}^{\circ}\)C or above is used. Additionally, the size of the formed crystals also increases with reaction temperature, which can be seen in SEM images (Figure S6). Reaction time also plays a factor in the generation of a single, stable Yb:LLF phase. At lower time points, the XRD data shows mostly unreacted LiF and little to no evidence of the Yb:LLF crystals. After 14 h, a significant Yb:LLF phase can be seen, and only traces of LiF remain (Figure S7). SEM also shows that increasing the length of the synthesis causes the crystal facets and morphology to be more well-defined and fully formed (Figure S8). Figure 2: Characterization of Yb:LLF crystals. Scanning electron microscope images of faceted Yb:LLF particles synthesized with NH4 HF2- (**a**) and PEG-assisted (**b**) hydrothermal syntheses. Scale bar = 5 \(\upmu\)m. Types of facets are indicated in parentheses. (**c**) Schematic of a Scheelite crystal structure of Yb:LLF with \(I4_{1}/a\) space group symmetry. (**d**) Powder XRD pattern of Yb:LLF crystals following NH4 HF2- (blue) and PEG-assisted (red) hydrothermal synthesis, indicating a Scheelite crystal structure. Inset: stereographic projections of crystal facets related to the most intensive XRD peaks.
## 4 Optimized NGSA-III Algorithm  
Multi-objective genetic algorithms typically exhibit a superior ability to handle high-dimensional multi-objective problems compared to traditional mathematical optimization algorithms [16]. [PERSON] [17] first proposed NSGA-I for multi-objective optimization; [PERSON] [18] subsequently used the crowding distance to improve this algorithm, establishing NSGA-II; finally, [PERSON] [19] used the reference point instead of crowding degree selection to propose NSGA-III. Although the flow of NSGA-III is generally similar to that of NSGA-II; the former overcomes the problems of the latter in solving high-dimensional multi-objective optimization problems, including those associated with the difficulty of visualizing the multi-dimensional frontiers, the uneven distribution of the non-dominated layer, and the readiness to fall into a local optimum, by selecting individuals based on the reference points.  
As a type of multi-objective genetic algorithm, NSGA-III evenly expands the individuals in the Pareto optimal solution front to the entire Pareto domain by setting reference points and conducting adaptive standardization of the population as well as correlation and individual retention operations to ensure population diversity [20].
# Arsenic Removal from Water by Adsorption onto Iron Oxide/Nano-Porous Carbon Magnetic Composite
## 3 Results and Discussion
### Effect of pH  
Relative distribution of As, as well as ionization of functional groups on the adsorbent surface, depends on the pH of the solution [42]. Effect of pH on the percentage of adsorption of As (III) by the composite at different pH values is presented in Figure 2.  
It was observed that a sharp increase in the As removal occurred when the pH value of the solutions changed from 2 to 8. From pH 8 onwards, a decrease in the adsorption, of As ions, was achieved. The maximum adsorption, of As ions, was obtained at pH 8. Therefore, pH 8 was considered as optimum pH for As (III) adsorption onto the composite. This adsorption behavior of As (III) at different pH might be due to the existence of As (III) in different oxyanions forms. In a highly acidic medium, As (III) mainly exists in the form of neutral H\({}_{3}\)AsO\({}_{3}\) species, and the adsorbent surfaces are highly protonated so adsorption of As (III) is not favorable. This is probably due to the weak interaction that occurs between the composite surface and H\({}_{3}\)AsO\({}_{3}\). At near neutral pH values of 7 to 9, H\({}_{3}\)AsO\({}_{3}\) could be deprotonated to form anionic species (H\({}_{3}\)AsO\({}_{3}\)- and HAsO\({}_{3}\)-). This neutral H\({}_{3}\)AsO\({}_{3}\) and negatively charged H\({}_{2}\)AsO\({}_{3}\)- and HAsO\({}_{3}\)\({}^{2-}\) were attracted to the positively charged adsorbent surface. It resulted in high As (III) uptake by the adsorbent in this pH range. The adsorption of As (III) took place by substitution of the hydroxyl ions or water molecules contained in the coordination spheres of loaded metal ion from the adsorbent surface. The decrease in As (III) adsorption at pH 10 might be due to the electrostatic repulsions between H\({}_{2}\)AsO\({}_{3}\)- and HAsO\({}_{3}\)\({}^{2-}\) and the negatively charged composite surface. Thus, the combined effects of enhanced electrostatic repulsion and the hydroxyl ion competition were responsible for the decrease in arsenic As (III) adsorption at high pH. It reduced As (III) uptake by the composite. A similar observation has been reported for the removal of arsenic from aqueous solution using iron (III)-loaded sugarcane bagasse [43].
## 3 Experimental Verification
### Experimental Principles and Platform Construction
#### 3.2.2 Excitation Signal Generation Function  
In the actual working process of the power transformer, the common excitation signals are the sinusoidal signal, square wave signal, PWM signal, etc. In order to simulate the actual service process of the transformer, it is necessary to consider the generation of the above three signals. The sine signal can be generated directly by the LabVIEW sinusoidal signal control, as a square wave for each cycle of the duty cycle of a certain PWM wave. This paper focuses on the study of PWM signal excitation with regard to grain-oriented silicon steel specimens and the magnetic properties of the change rule. Therefore, this section only introduces the generation of PWM excitation in the measurement platform.  
PWM is a very effective technique to achieve control of analog circuits by using the digital output of microprocessors, which is explained in detail in [29]. The carrier ratio is the ratio of the carrier signal frequency \(f_{v}\) to the modulated signal frequency \(f_{s}\), and the mathematical expression is shown in Equation (11). Generally speaking, the frequency of the carrier signal in PWM technology is much higher than the modulated signal frequency; this paper, for the convenience of research, uses a fixed carrier ratio of 20.  
\[N=f_{v}/f_{s} \tag{11}\]  
Another important parameter in PWM technology is the modulation index (also known as the amplitude modulation ratio), defined as the ratio of the modulated waveform amplitude \(V_{v}\), with the mathematical expression as shown in Equation (12). In practical engineering applications, \(K>1\) is over-modulation and \(K<1\) is under-modulation.  
\[K=V_{s}/V_{v} \tag{12}\]  
PWM wave can be divided into unipolar PWM and bipolar PWM; the different modulation methods will lead to differences between the two. In this paper, we use the simple and efficient modulation principle of bipolar PWM, which can be generated by inputting the modulated wave and carrier wave into the comparison function. When the  
Figure 11: Demagnetization signal output waveforms.  
modulation index changes, the generation process is as shown in Figure 12. The PWM excitation signal is generated by the host computer.