esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this study, a novel phosphonic acid functionalized cellulose adsorbent (PVKAP) with a simple preparation route is used for the highly efficient and selective adsorption of U(VI). The effects of different temperatures, contact times, pH values, ionic strengths, and competitive adsorptions of various metal ions were studied to explore the adsorption mechanism of the prepared adsorption materials to U(VI). The adsorption behavior conforms to the Langmuir adsorption model and the pseudo-second-order kinetics model. Owing to the strong coordination between phosphonic acid groups and U(VI), the adsorption performance of PVKAP is obviously higher than that of PVK. The maximum adsorption capacities (pH = 5, \(T\) = 293 K) of PVK and PVKAP based on Langmuir  
Figure 9: XPS spectra of P 2p for PVKAP before (**a**) and after (**b**) uranyl entrapment, O 1s for PVKAP before (**c**) and after (**d**) uranyl entrapment.  
isotherm model are 57.2 and 714.3 mg g\({}^{-1}\), and the adsorption equilibrium reaches in 240 and 35 min, indicating that PVKAP has great potential value to capture U (VI). Moreover, PVKAP has high adsorption selectivity for U(VI) (sorption selectivity is 70.36%) and good recyclability. The results of batch adsorption experiments and spectral analysis reveal that the efficient capture of U(VI) by PVKAP is mainly regulated by the inner layer complexation. Given the environmental friendliness of PVKAP, its ease of preparation, and low cost, it has good application potential value for U(VI) enrichment in aqueous media related to nuclear waste.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/16/5455/s1](http://www.mdpi.com/2076-3417/10/16/5455/s1)]([http://www.mdpi.com/2076-3417/10/16/5455/s1](http://www.mdpi.com/2076-3417/10/16/5455/s1)), Figure S1: Schematic of the synthetic route of PVKAP, Figure S2: Original data for XPS experiments.  
Conceptualization, Z.H.; Funding acquisition, Z.H., H.Z. and J.L.; Investigation, Z.H., S.Z. and J.Y.; Project administration, Z.H. and J.L.; Supervision, H.Z.; Validation, S.Z. and J.Y.; Writing--original draft, Z.H., S.Z. and J.Y.; Writing--review and editing, Z.H. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The work was financially supported by the National Natural Science Foundation of China (Nos. 21677146, 21777165, 21876178), and the Anhui Provincial Natural Science Foundation (No. 1708085 MB31).  
The authors declare no conflict of interest.
## 1 Introduction
Further, an error propagation analysis was carried out, and the convergence conditions are given in [32], which showed that the localization and tracking accuracy only depends on the expectation of the measurement precision. In this paper, the multi-robot localization and target tracking problem with a general nonlinear process and various measurement models is studied, and a UT-based CLAT scheme is proposed with consideration of the communication and memory limitations. The main contributions of this paper are summarized as follows: First, the proposed UT-based CLAT is recursive, and it does not store measurements; each robot only keeps the latest estimates of its own and the target, so the storage requirement is significantly reduced. Furthermore, communication is limited to the two robots to obtain a cooperative relative measurement, and no communication with other robots is needed. Meanwhile, to guarantee estimation consistency, inter-robot correlations are approximated in a distributed fashion on the basis of the covariance split method, and the robot-target correlation is discarded using the conservative CI method. Finally, the overall system is modeled on the basis of general nonlinear models and is characterized on the basis of the UT approach rather than the EKF method. Thus, the computation of a Jacobian is avoided. Simulations were carried out, and they indicate that the proposed UT-CLAT method is able to realize stable state estimates of both local robots and targets. More importantly, a hardware platform containing three quadrotors was implemented to verify the effectiveness of the proposed UT-CLAT method. Specifically, three types of measurements (absolute measurement, relative cooperative, and uncooperative measurement) from, respectively, the navigation system, ultra-wide bandwidth (UWB) transmitters, and onboard cameras were utilized to effectively estimate the states of the local robots and targets. The rest of this paper is organized as follows: Section 2 formulates the CLAT problem, and Section 3 describes the proposed CLAT method. Sections 4 and 5, respectively, provide the simulation results, which are based on synthetic data, and experimental results, which are based on hardware platforms. Section 6 concludes the paper.
## 7 Experimental Results
As shown in the first row of the figure, the AC efficiency can be kept nearly constant above 97% and does not much depend on the phase difference \(\varphi_{21}\). However, the DC efficiency shows a dependence on the phase difference. As shown in the second row of the figure, the DC efficiency of \(\varphi_{21}=-96^{\circ}\) is better than the other two data sets with a phase difference of \(\varphi_{21}=-90^{\circ}\) and \(\varphi_{21}=-102^{\circ}\). In the \(\varphi_{21}=-90^{\circ}\) case, the reactive power at  
Figure 16: Some typical operating points in the CC and CV modes. (**a.1**) CC mode, \(U_{\text{2 DC}}=40\) V. (**a.2**) CC mode, \(U_{\text{2 DC}}=60\) V. (**a.3**) CC mode, \(U_{\text{2 DC}}=70\) V. (**a.4**) \(U_{\text{1 DC2 DC}}=90\) V, charging power about 1.2 kW. (From **b.1** to **b.4**) CV mode operating points, with charging current from \(I_{\text{bat}}=9.3\) A to \(I_{\text{bat}}=6.2\) A.  
Figure 12:0 Experimental results concerning the charging power for different operating points. the input and output of the analog WPT path is nearly zero, so the zero-voltage-switching (ZVS) cannot be reached. By decreasing the phase difference to \(-96^{\circ}\), the ZVS conditions can be met. By further reducing the phase difference, the third quadrant current in the GaN-MOSFET during deadtime increases. It is worth mentioning that the GaN-MOSFETs have no intrinsic p-n junction body diode; the third quadrant current builds up a reversed \(V_{\text{GS}}\) to enable the third quadrant current flows from the source to the drain in saturation. Therefore, by further reducing the phase difference, higher losses due to the third currents in GaN-MOSFETs can be produced. Unlike with conventional semiconductors (e.g., SiC), a reverse conduction voltage of up to \(-8\) V due to off-state third quadrant current was obtained with the GaN-MOSFETs. Parallel diodes can be considered to reduce the losses in GaN-MOSFETs due to the third current at the cost of extra output capacitance.
# Visual Selective Attention Analysis for Elderly Friendly Fresh E-Commerce Product Interfaces
## 4 Results
### Design Strategies  
Based on the visual selective attention mechanisms of elderly users and the conclusions drawn from the visual element selection and eye-tracking experiments, the following five design strategies are proposed for optimizing fresh food e-commerce interfaces for elderly users:  
* Categorized Information Cards to Improve Visual Selection Efficiency:  
Implementing information card categorization in internet product design establishes a clear information framework, facilitating a smooth and efficient interaction experience for elderly users. Clearly categorized information and ample white space are more likely to attract the visual attention of elderly users and reduce their cognitive load. In age-friendly design, similar information can be integrated into card formats, making it easier for elderly users to locate target information and enhance selection efficiency;  
* \"Top-Down\" Layout Structure to Align with Reading Habits and Optimize Interaction Experience:  
Elderly users prefer vertically arranged information lists and dislike crowded and complex page layouts. Given that the way elderly users hold and operate their phones differs from that of younger users [55], adopting a top-down layered single-column list layout with whitespace and right-side hot zone design is more effective in capturing their visual attention and optimizing the interaction experience;  
* Emotional Implications of Color to Guide User Behavior:  
The color choices of elderly users are influenced by individual emotions and memories. Design should consider the consistency of colors with their emotional experiences to ensure that color expression aligns with user perceptions. Interface elements can leverage the emotional expression function of color, such as using warm tones to highlight important information, thereby guiding the attention of elderly users;  
* Clear and Appropriate Text Information:  
Elderly users prefer sans-serif fonts of moderate thickness, with 18 px being a commonly favored choice. Fonts should be simple and easy to read, using language that is understandable to elderly users. Additionally, maintaining font consistency is crucial, avoiding the use of too many different font styles on the same interface;  
* Focus Styles to Guide Elderly Users Toward Targets:  
Elderly users concentrate more on task-related information [56]. Interactive interfaces should use prominent visual guidance to help them effectively execute tasks. The design should employ clear visual guidance methods, such as background color changes, highlighting, and enlargement, to assist elderly users in efficiently completing tasks, building trust, and directing visual attention.
## 2 Materials and Methods
### SEM Experiment  
SEM images were acquired using a Cold Field Emission Scanning Electron Microscope. To obtain high-resolution images of coal surface morphology and avoid the charging effect of insulators, all coal samples underwent conductive treatment. Firstly, coal blocks were selected and prepared into small pieces of approximately 5 \(\times\) 5 \(\times\) 3 mm\({}^{3}\) using a precision cutting machine (with the cutting direction parallel or perpendicular to the bedding plane). Subsequently, the sample surfaces were treated with argon ion polishing (at a low angle \(<\) 5\({}^{\circ}\) and low voltage \(<\) 5 kV) to minimize mechanical damage and obtain a flat, fresh observation surface, thereby exposing the true pore structure. SEM observations were conducted in high vacuum mode. The key parameter settings were: accelerating voltage of 3-10 kV (typically 5 kV), working distance of 5-10 mm, and the use of a low electron beam current (pA level) to minimize sample damage. The secondary electron detector (SE) was primarily used to acquire morphological information. The imaging process involved locating representative areas (such as vitrinite-rich zones) at low magnification and systematically acquiring high-resolution images of the selected fields of view at magnifications ranging from 500\(\times\) to 50,000\(\times\) (at least 3-5 fields of view per sample). The obtained SEM images were primarily used for qualitative analysis of the macro/micro morphological characteristics of coal samples with different volatile contents (such as groove undulations, bedding development, and surface compactness), as well as pore morphology, size distribution, abundance, connectivity, and fracture characteristics. These results were cross-verified with AFM analysis findings.
## 3 Method  
### Experimental Data Description  
The following paragraph describes the AMIGOS dataset in a condensed form. In this paper, the newly released dataset AMIGOS are used to validate the model, not only because it is widely used in the recent literature on physiological signal-based emotion elicitation, but also because they use low-cost physiological signal acquisition devices for data collection and the non-invasive nature of the whole process. AMIGOS applied a 14-channel Emotiv Epoc wireless headset to acquire EEG signals, peripheral physiological signals (ECG Right, ECG Left and, GSR physiological data pre-processed at a sampling frequency of 128 Hz) recorded with non-invasive devices such as the ECG Shimmer 2R5, as well as frontal video (RGB), stimulus material from the MAHNOB-HCI [41] dataset as emotionally stimulating material. The dataset used both individually and GrOups scenarios, the first with 40 participants watching 16 short videos (<250 s in length); the second, with 17 people in an individual setting and 5 groups of 4 people each, where participants watched long videos (>14 min in length). Each trial first contained a 5 s baseline signal, with the signal depending on the duration of the video. After viewing the video, participants rated self-assessments of arousal, potency, liking, and dominance on a scale of 1 to 9 in the self-assessment of potency (SAM) [42]. A total of 12,580 video clips were annotated in this way (340 clips from 37 participants in both short and long-video experiments). The arousal and valence scales used for these annotations are continuous, ranging from 1 (low arousal or potency) to 9 (high arousal or potency), and there is a high degree of agreement between annotators. There were 800 records in the dataset and 7 subjects (ID numbers 33, 24, 23, 22, 21, 12, 9) had missed data and were considered invalid.  
The dataset can be divided into four classes: low arousal low valence (LALV), high arousal low valence (HALV), low arousal high valence (LAHV), and high arousal high valence (HAHV), and the threshold values of the valence and arousal dichotomy is 5. The kmeans algorithm are applied to cluster the distribution of the data, and Figure 1 shows the distribution of the sentiment classes in AMIGOS, purple represents LALV, blue represents HALV, green represents LAHV, yellow represents HAHV in the Figure 1.
## 5 Applications of PCMs in Buildings
### PCM Panels and Wallboards
#### 5.1.3 Ceiling Applications  
The development of ceiling solutions based on PCM technology has also been a field of research. There are many approaches for PCM ceiling applications, such as the incorporation of PCM ceiling panels into the roof construction [106; 107], the development of ceiling panels with capillary network for PCM circulation [108], and the coupling of PCM technology with other technologies [109; 110]. Mariana Velasco-Carrasco et al. [106] investigated the thermal performance of three different PCM blister panels for ceiling tiles application. The PCM used was INERTEK 23. The PCM was incorporated in three blister panels, two of which were enhanced with the addition of aluminum and steel wool particles, respectively. The panels were embedded between the roof slabs. The results obtained showed that the overall thermal performance of the aluminum- and steel-wood-particle-enhanced panels was improved, especially in the case of steel wool particles. Shilei Lu et al. [109] developed an innovative building cooling system of PCM ceiling coupled with earth-air heat exchanger (EAHE). The evaluation of experimental methods and data analysis indicated that indoor room temperature with this system could reduce peak temperature by 2.1 \({}^{\circ}\)C under 8-h timed cold storage experiment and 2.7 \({}^{\circ}\)C under 12-h timed cold storage experiment. Hansol Lim et al. [110] proposed a PCM integrated thermoelectric radiant cooling panel, which consists of thermoelectric modules, heat sinks, insulation, and PCM layer between two aluminum panels. The PCM layer provides passive cooling by freezing the PCM during the operation period for shifting the electrical load to the off-peak period demand periods.
# LoRaWAN Communication Protocols: A Comprehensive Survey under an Energy Efficiency Perspective
## 2 Background Knowledge
### Device Operation Classes in LoRaWAN  
According to the LoRaWAN specification [29], three classes of end-devices are defined, namely class A, B, and C. In class A, the end-devices are in sleep mode, except when transmitting data. The uplink transmissions are based on ALOHA scheme, followed by two downlink slots called receiving windows in order to allow end-devices to receive potential ACKs and/or commands from the NS. These slots are not prescheduled, so class A devices are not capable of communicating constantly with NS unless they transmit data. LoRaWAN may include confirmed message transmission in order to improve transmission reliability by using ACK to guarantee data reception. Confirmed message transmission imposes limitations to network capacity, making scalability an important need for LoRaWAN. After the receive window, the end-devices are set back to sleep mode in order to conserve energy.  
Class A is the end-device category that yields the lowest power consumption [34] and all LoRaWAN end-devices should support Class A operation. Generally, these devices are battery-powered, have long intervals and downlink latency; thus, making them suitable to monitor environmental conditions, track animals, detect fires, etc. In Class B, the end-devices operate in a similar to Class A manner, but an additional receive window is introduced at pre-scheduled intervals; thus, increasing the device's power consumption [35]. Finally, in Class C, the end-devices are continuously set to active mode; hence, being able to accept messages from the server at any time, except for the time periods that they transmit data. In Class C, end-devices increase further the induced power consumption compared to Class A and B [36].
# Control of a Mobile Line-Following Robot Using Neural Networks
## 3 Control Strategies
### Pid  
For the classical methods, the PID (Proportional-Integral-Derivative) controller was selected. The PID is the most common controller in industry and is widely used in all control applications. It also represents an essential element of more sophisticated controllers [23; 24; 25; 26]. Figure 5 presents a block diagram of a PID-controlled system [27; 28].  
The PID controller can be described as  
\[u(t)=K_{p}\bigg{(}e(t)+\frac{1}{T_{i}}\int_{0}^{t}e(\tau)\,d\tau+T_{d}\frac{de( t)}{dt}\bigg{)} \tag{1}\]  
where \(u(t)\) represents the controller action output and \(e(t)\) is a control error input, which is the difference between the desired reference value and the current value of the variable to be controlled. The controller output is thus the sum of three terms: the P-term (which is proportional to the error), the I-term (which is proportional to the integral of the error), and the D-term (which is proportional to the derivative of the error). The parameters \(K_{p}\), \(T_{i}\), and \(T_{d}\) are, respectively, the proportional gain, the integral time constant, and the derivative time constant [29].  
The discrete PID controller can be obtained as  
\[u[n]=K_{p}\bigg{(}e[n]+\frac{T}{T_{i}}\sum_{k=0}^{n}e[k]+\frac{T_{d}}{T}(e[n] -e[n-1])\bigg{)} \tag{2}\]  
where \(T\) is the sampling period and \(n\) is the sample instant. Since \(T\) is constant (\(T=1/80\) s), the following simplified equation was used:  
\[u[n]=K_{p}e[n]+K_{i}\sum_{k=0}^{n}e(k)+K_{d}(e[n]-e[n-1]) \tag{3}\]  
where \(K_{i}=\frac{K_{p}T}{T_{i}}\) is the integral gain and \(K_{d}=\frac{K_{p}T_{d}}{T}\) the derivative gain.
# Short-Term and Long-Term Travel Time Prediction Using Transformer-Based Techniques
## 3 Proposed Methodology  
### Problem Formulation  
The aim of this study is to forecast future travel times over extended periods for specific road segments by leveraging their respective historical traffic data. The predictive challenge can be formulated as follows:  
Given the historical traffic data \(\mathbf{X}^{\tau}\) spanning a duration of \(T\) time steps of a road segment \(r\), our goal is to develop a model \(f(\cdot)\) to estimate the travel time for the upcoming \(T^{\prime}\) time steps, starting from \(\Delta\) steps after the present time \(\tau\), denoted as \(\mathbf{Y}^{\tau,\Delta}\). The model \(f(\cdot)\) is thus defined as follows:  
\[[X_{\tau-T+1},\dots,X_{\tau-1},X_{\tau}]\xrightarrow{f^{\Delta}}[Y_{\tau+ \Delta+1},Y_{\tau+\Delta+2},\dots,Y_{\tau+\Delta+T^{\prime}}] \tag{1}\]  
Note: To maintain clarity and focus in the subsequent sections of this paper, the superscripts of \(\tau\) and \(\Delta\) in \(\mathbf{X}\) and \(\mathbf{Y}\) will be omitted.
## 3 Results and Discussion  
### Feasibility Analysis of the Model  
#### 3.1.1 Analysis of Grid Independence  
The calculation domain grid is refined to assess grid independence and determine the optimal grid size. The maximum grid spacing in the calculation domain is set to 3.6 mm, while the minimum grid size and the porous medium refinement size are set to 0.21 mm and 0.06 mm, respectively. Consequently, the number of grids increases from 114,660 to 902,560. Figure 2 illustrates the impact of grid density on pressure drop. As the number of grids increases, the difference in pressure drop between adjacent operating conditions gradually diminishes. Notably, the calculated pressure drop results for minimum grid sizes of 0.06 mm and 0.07 mm are nearly identical. Therefore, a minimum grid size of 0.07 mm can reduce computational cost while maintaining the accuracy of the simulation results.
## 2 Methodologies
### Feed Forward, LayerNorm and Dropout  
Our model incorporates several techniques to enhance its performance. These include adding two feed-forward neural networks (FFN) in the text convolutional layer, attention layer, and feature fusion representation layer, as well as utilizing LayerNorm, Dropout, and residual connections. In the feed-forward neural networks, we employ the gelu [49] non-linear activation function, which introduces non-linearity and alters the output space of features, thus improving the model's performance. Specifically, for the input of the FFN, we first map it to a higher-dimensional space using a fully connected layer, apply the gelu activation function, and then map it back to the original dimensionality using another fully connected layer, as shown in Equation (8). This allows the model to better learn non-linear features and enhance its ability to handle complex data.  
\[FFN(x)=gelu(xW_{1}+b_{1})W_{2}+b_{2} \tag{8}\]  
In addition to \(FFN\), we also introduce LayerNorm and Dropout to enhance the robustness and generalization ability of our model. LayerNorm enables the normalization of features for each sample, ensuring similar mean and variance distributions across dimensions, thereby reducing the problem of internal covariate shift. _Dropout_ randomly sets a portion of neuron outputs to zero, preventing the model from overfitting to specific input data. Lastly, we employ residual connections, where the original input is added to the features processed by the \(FFN\). This can expedite the convergence of the model and mitigate the issue of gradient vanishing. Therefore, the output for each component is given as follows:  
\[LayerNorm(x+Dropout(Sublayer(x))) \tag{9}\]  
With the series of operations described above, our model is able to obtain richer feature representations, thereby enhancing its performance in handling complex tasks. These techniques not only expedite the convergence speed during training but also effectively prevent overfitting and gradient vanishing issues, thereby strengthening the network's sensitivity to data.