esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### The WUE of Different LUCC  
#### 3.2.1 WUE of Different LUCC  
We have analyzed the changes in WUE of different LUCC in the YRB during different periods (Figure 5). The results found that the WUE of different LUCC from 1982 to 2018 were forest (1.267 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O), cropland (0.972 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O), grassland (0.805 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O), urban construction land (0.807 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O) and other (0.654 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O), respectively. WUE was highest in forest and grassland under different vegetation types. The forest WUE was 1.270 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O and 1.352 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O in 1982-2000 and 2000-2018, respectively. Forest WUE increased by 0.082 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O after 2000. Compared to the period from 1982 to 2000, the WUE of grasslands and croplands increased between 2000 and 2018. Specifically, the WUE of croplands improved by 0.144 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O, while the WUE of grassland increased by 0.106 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O. However, there has been a decrease in the WUE of urban areas, barren land, and water.
# A Finite Element Method for Nonlinear and Fully Coupled Transient Hydrothermal Transfer
## 5 Illustrative Examples
### Steady 1D Moisture and Heat Transfer with Temperature-Dependent Material Properties  
In this example, moisture and temperature distributions at steady state in a one-dimensional bar is analyzed with the present FEM method. The length of the bar is 20 mm, the material is T300/5208 composite, and its properties are moisture- and temperature-dependent [21].  
\[D_{m}=\frac{D_{0}}{1-\lambda\gamma\times 0.607}exp\left(-\frac{E_{d}}{R_{g}T} \right)\,\left(\mathrm{m^{2}/s}\right) \tag{25a}\] \[D_{h}=10D_{m}\] (25b) \[\lambda=0.122,\ \gamma=2.053\] (25c) \[D_{0}=4.25\times 10^{-5}\,\left(\mathrm{m^{2}/s}\right)\] (25d) \[E_{d}=5.23\times 10^{4}\,\left(\mathrm{kJ/(kg\cdot mol)}\right)\] (25e) \[R_{g}=8.314\,\left(\mathrm{J/(mol\cdot K)}\right) \tag{25f}\]  
Boundary conditions are such that the left side of the bar has a temperature of 293 K and a moisture content of 28 kg/m\({}^{3}\), and the right side of the bar has a temperature of 313 K and a moisture content of 94 kg/m\({}^{3}\).  
Distributions of the moisture content and temperature for this example are shown in Figures 5 and 6. For the curves with constant properties, the material properties are calculated at the initial temperature \(T=293\) K. As expected, if the material properties are constant, the moisture content and temperature profiles are linear. If the temperature-dependent properties are considered, the corresponding temperature and moisture content are considerably higher than those with constant properties. More specifically, at the middle of the bar, moisture content without considering the temperature dependence of the material properties is 61 kg/m\({}^{3}\), while the moisture content with temperature-dependent material properties is 17% higher and is 71.4 kg/m\({}^{3}\).  
Figure 5: Distribution of moisture content at steady state.
###### Abstract  
Heterogeneous cellular networks (HCNs) have emerged as the primary solution for explosive data traffic. However, an increase in the number of base stations (BSs) inevitably leads to an increase in energy consumption. Energy efficiency (EE) has become a focal point in HCNs. In this paper, we apply tools from stochastic geometry to investigate and optimize the energy efficiency (EE) for a two-tier HCN. The average achievable transmission rate and the total power consumption of all the BSs in a two-tier HCN is derived, and then the EE is formulated. In order to maximize EE, a one-dimensional optimization algorithm is used to optimize picocell BS density and transmit power. Based on this, an alternating optimization method aimed at maximizing EE is proposed to jointly optimize transmit power and density of picocell BSs. Simulation results validate the accuracy of the theoretical analysis and demonstrate that the proposed joint optimization method can obviously improve EE.  
energy efficiency; density; transmit power; optimization +
Footnote †: journal:
# A Context-Aware Conversational Agent in the Rehabilitation Domain
## 3 System Architecture
### System Design and Integration  
#### System Conceptualisation  
The system's high-level architecture is divided into three different layers (Figure 1):  
* The communication understanding layer
* The sensor management layer
* The communication analysis layer  
The communication-understanding layer is responsible for receiving any kind of user input and for routing it to the other layers depending on the informational and functionality needs. It also outputs the final system response or action to the user. It comprises the only layer that communicates directly with the user and the central point, where all the information between the user and the system's layers is circulated. It encapsulates modules for transforming any voice input into text and the other way around, and it provides access to data and features that require additional rights to authenticated users (mainly the medical centre staff).  
The sensor-management layer collects and distributes data that are recorded by installing sensors in the room or the user's body. Data stored in this layer concern activities and a person's biometrical indications. It is either retrieved by the communication-understanding layer or sent in the content associated with an alert signal triggered in emergencies when a proactive action is necessary. It is also possible to send commands to this layer to perform a move pertinent to the adaptation of equipment such as the patient's bed, where a need to change its angle may arise at any moment. In addition to the sensor systems, the REA platform will also incorporate an advanced computer vision component, responsible for detecting patient falls and activities.  
Moreover, a communication analysis layer will be integrated, which will grant the REA agent advanced and complex conversational capabilities. The main module of this layer, the Dialogue Management (DM), semantically represents and interlinks the user utterance against the system's domain and dialogue models, whereas at the same time, it processes the entire dialogue to generate content that has not been explicitly fed to the platform. It retrieves relevant content for the system action from either the Knowledge Base (KB) or the web-based question answering service. In most cases, the communication-understanding layer obtains the final system response from this layer, regardless of where the accompanying content is retrieved from (e.g., it may come from the sensors layer). Nevertheless, there are some occasions when the DM intervention is not required and the system action is provided directly from the communication-understanding layer. Regardless, at every dialogue turn, the DM is informed of the performed system action, even if it is not generated by this layer, to keep a complete and updated version of the dialogue history.
# Design and Development of an Omnidirectional Three-Wheeled Industrial Mobile Robot Platform
## 2 Related Work
### Omnidirectional Four and Three-Wheeled Robots  
Omnidirectional mobile robots (OMRs) can be primarily classified into four-wheeled and three-wheeled configurations. Four-wheel designs, typically using mecanum or omnidirectional wheels, offer an even distribution of forces and greater static and dynamic balancing, especially when handling heavy loads. However, they require precise synchronization among all wheels, which increases the complexity of control and demands higher energy consumption [4].  
On the other hand, three-wheeled omnidirectional robots distribute the reaction forces through only three points, offering improved traction efficiency. This design is mechanically simpler, with all wheels maintaining ground contact even on uneven surfaces. The three-wheel configuration also stands out for its reduced number of components, leading to lower manufacturing and maintenance costs [10].  
Although three-wheeled robots may present challenges related to dynamic stability, proper structural design and advanced control strategies can effectively compensate for this limitation. Furthermore, in tasks involving omnidirectional motion on flat, smooth surfaces, three-wheeled platforms can achieve comparable performance to four-wheeled robots [3].  
Recent comparative studies, such as the one by [PERSON] et al. [11], evaluated three- and four-wheeled omnidirectional mobile robots from a kinematic perspective, analyzing their behavior under different motion commands. Their findings suggest that three-wheeled robots can achieve highly accurate omnidirectional movement while benefiting from simpler mechanical construction, particularly advantageous in environments with constrained spaces.  
In addition to academic studies, several commercial omnidirectional platforms have been developed for industrial applications. Table 1 summarizes the key characteristics of some representative designs.  
This comparison highlights that while four-wheeled platforms often dominate in high-payload industrial environments, three-wheeled designs are mechanically simpler and can achieve competitive performance, especially when the payload-to-weight ratio (payload fraction) and maneuverability are critical factors.  
The design proposed in this article adopts a three-wheel configuration, prioritizing mechanical simplicity, reduced energy consumption, and ease of manufacturing, without compromising precision or load-carrying capabilities--essential features for applications in industrial logistics and material transportation.
## 4 Experimental Results
This method considers both the properties of the features and their density. Finally, we draw an ROC curve to compare the performances of the three methods. Figure 10 shows the ROC curves for the three methods. A method in which the AUROC is close to 1.0 detects a PCB fault more precisely. As can be seen, the AUROC of Method 1 is larger than that of Method 2 for 6 of the 10 images. However, the AUROC of Method 1 is less than that of Method 3 for all 10 images. Thus, the performance of Method 1 is not proper for detecting faults. Although Method 2 outperforms Method 1 for certain images, it has a poorer level of performance than Method 3. On the other hand, Method 3 outperforms the other methods, and its AUROC is close to 1.0 for all images. Therefore, Method 3 is appropriate for detecting a scratch fault on a PCB. Figure 10: _Cont._
## 4 Experimental Results  
In this section, the cylindrical roller bearing installed on the test rig is taken as the test object. The test rig is shown in Figure 4. Three NJ208 (TMB) type cylindrical roller element bearing were used for testing. The vibration sensor model was a PCBM621B40 and the sensor had a reference sensitivity of 10 mv/g and the frequency range of measurable signals was 1.6 to 30,000 Hz. The data acquisition instrument model was an INV3018C. The test was carried out under the conditions of setting fault: a through crack fault with width and depth of 0.2 mm was set on the outer race, inner race and ball of the. rolling bearing respectively by using the wire-cut method. The load applied to the testing bearings was about 1 kN. The sampling frequency was 51.2 kHz and the Shaft rotating speed was 1496 r/min. The theoretical fault feature frequencies of the outer race, inner race and ball were 142.8 Hz, 206.2 Hz and 132.6 Hz respectively. And the corresponding periods were 7.00 ms, 4.85 ms, and 7.54 ms, respectively. As in the simulation, we used the discrete Daubechies 5 wavelet with three decomposition levels, and detail coefficients at each level were used for the estimation of parameters \(k_{1}\), \(k_{2}\), \(\sigma_{1}\) and \(\sigma_{2}\) utilizing the EM algorithm. The results for the outer race, inner race and ball are shown in Figures 5-7 respectively.
# EHANet: An Effective Hierarchical Aggregation Network for Face Parsing
## 3 Methodology
### Stage Contextual Attention Mechanism  
Faced with ambiguous semantic categories, model outputs are often misclassified or produce inconsistent parsing results. Depending on the interdependence between feature map channels, we can emphasize interdependent feature maps to improve the feature representation of a particular category. Partly inspired by the work of SeNet [31], we introduce a stage attention based on channel-wise attention. Unlike SeNet only using the current context, we use higher-level features with richer semantics to provide contextual guidance for lower-level ones. Based on the above observations, we extract the high-level context features of stage-(\(n+1\)) to facilitate learning of the low-level features of stage-(\(n\), which is called stage contextual attention mechanism (SCAM). It should be noted that \"stage\" represents different convolutional blocks in Figure 2.  
As illustrated in Figure 3, given a high-level input feature map \(f_{H}^{in}\in\mathbb{R}^{C\times H\times W}\), we first feed it into a global pooling and two \(1\times 1\) shrink and expand convolutions (Add _relu_ and _sigmoid_ layers sequentially) to generate a global feature map \(f_{G}\), where \(f_{G}\in\mathbb{R}^{C\times 1\times 1}\). Then, feature map \(f_{L}^{in}\in\mathbb{R}^{C\times h\times w}\) is obtained from the low-level input feature map followed by another \(1\times 1\) convolution.  
Figure 2: Diagram of our effective hierarchical aggregation network (best viewed in color). “C” stands for channel-wise concatenation. “SCAM” denotes stage contextual attention mechanism. “SGCB” denotes semantic gap compensation block. “BA” denotes boundary-aware module.  
In particular, \(1\times 1\) convolution ensures the consistency of the channel while reducing the number of calculations needed. After that we perform an element-wise matrix multiplication between \(f_{L}^{in}\) and \(f_{G}\), and then \(Add\) with \(f_{H}^{in}\) through bilinear upsampling as \(U(\cdot)\) to get the output feature map \(f_{L}^{out}\in\mathbb{R}^{C\times h\times w}\). Overall, it can be written as:  
\[f_{L}^{out}=f_{L}^{in}\odot f_{G}\ +U(f_{H}^{in}) \tag{1}\]  
where \(\odot\) denotes element-wise multiplication.
# Prediction of Key Parameters of Wheelset Based on LSTM Neural Network
## 3 Wheelset Key Parameter Analysis  
### Standards and Requirements for Key Parameters  
Figure 4 shows the appearance of the bogie and wheelset, and Figure 5 shows the appearance of the wheelset rim. The bogie frame includes the end beam, side beam, cross beam, and other parts, which mainly support the car body and the acceleration and braking of the train [19]. The wheelset is the only part of the vehicle that has contact with the track and is a very key part as the stress point of the train [20]. The wheelbase is 1353 \(\pm\) 2 mm and the wheel diameter value running range is 770 mm to 840 mm.  
The daily inspection of the railway vehicle wheelset is mainly carried out by measuring parameters such as wheel diameter \(D\), flange thickness \(Sw\), flange height \(Sh\), and flange slope \(Qr\) using the trackside measurement system. These parameters are evaluated to assess the operation of the wheelset. When the wheel flange thickness value \(Sw\) decreases and the flange height value \(Sh\) increases, it indicates that the wear on the flange is greater than that on the tread. Conversely, when the flange thickness value \(Sw\) increases and the flange height value \(Sh\) decreases, it indicates that the wear on the tread is greater than that on the flange. The flange parameters are shown in Figure 6.  
Point \(P_{0}\) is defined at a distance of 70 mm from the wheel back at the tread. Point \(P_{1}\) is defined 2 mm below the highest point of the flange. Point \(P_{2}\) is defined at a vertical distance of 10 mm above point \(P_{0}\) (DIN5573 profile). The flange thickness \(Sw\) is the horizontal distance between the wheel back and point \(P_{2}\). The flange height is the vertical distance between the highest point of the flange and point \(P_{0}\). The wheel flange slope \(Qr\) is the horizontal distance between points \(P_{1}\) and \(P_{2}\).  
To ensure the safe operation of railway vehicles, there are strict operational requirements for the dimensions of the wheelset. According to the requirements of a certain line in the Shanghai Metro, the range of the wheel diameter \(D\) is 770 mm \(\leq\) D \(\leq\) 840 mm, the  
Figure 4: Appearance of train bogie.  
Figure 5: Train wheelset appearance diagram.  
range of the flange thickness \(Sw\) is 26 mm \(\leq\) Sw \(\leq\) 33 mm, the range of the flange height \(Sh\) is 27.5 mm \(\leq\) Sh \(\leq\) 34 mm, and the range of the flange slope \(Qr\) is 6.5 \(\leq\) Qr \(\leq\) 13.5. In addition, for coaxial wheelset, the wheel diameter deviation should not exceed 2 mm; for the same bogie, the wheel diameter deviation should not exceed 4 mm; and for the same car, the wheel diameter deviation should not exceed 7 mm.
## 3 Results
### Physiological Assessment of Mental Workload
#### 3.6.3 Ocular Measures  
Recently, there has been a significant increase in the use of eye-tracking technology in the context of HCR [64], largely due to advancements that have made these tools more sophisticated and widely available. Eye tracking goes beyond simply noting where the eye is looking; it allows the detailed examination of where, how long, and in what sequence the eyes focus on different points in the visual field. It is a valuable tool used to analyze visual attention through fixations, saccades, and pupil dilation [40; 45], offering insights \begin{table}
\begin{tabular}{c c c} \hline \hline
**Features** & **Description** & **Employed in** \\ \hline Ultra-Low-Frequency (ULF) & Power spectrum \(\leq\) 0.003 Hz & – \\ \hline Very-Low-Frequency (VLF) & Power spectrum from 0.003–0.04 Hz & – \\ \hline Low-Frequency (LF) & Power spectrum from 0.04 to 0.15 Hz & [34] \\ \hline High-Frequency (HF) & Power spectrum from 0.15 to 0.4 Hz & [34] \\ \hline Sympathetic Modulation Index (SMI) & SMI = LF/(LF + HF) & – \\ \hline Vagal Modulation Index (VMI) & VMI = HF/(LF + HF) & – \\ \hline Symphotovagal Balance Index (SVI) & SVI = LF/HF & [34] \\ \hline \hline \end{tabular} Note. – not employed in any of the reviewed studies.  
\end{table} 
Table 4: Common HRV features in the frequency domain. into how cognitive load affects visual processing and attention, with variations in these signals providing key indicators of mental workload fluctuations (Table 5). Research conducted by [43; 45] underscores the reliability of eye-tracking in long-duration sessions. Their findings indicate a positive relationship between pupil diameter (pupil size) and cognitive processes. Likewise, ref. [52] observed that pupil diameter was reduced in collaborative robot modes (both with and without cues), suggesting a lower mental workload. In the same study, ref. [52] also noted that, in comparison to the manual mode, the number of blinks in the \"unreliable condition\" (participants were informed that the robot could make mistakes and they had to correct them) increased, which suggests an increase in the subject's cognitive load due to the need to supervise and correct the robot's mistakes. In a similar vein, ref. [40] also found that the number of blinks increased more when participants had to do a secondary task (do mathematical tasks aloud alongside the assembly task) in comparison to both the single task (assembly task) and the rest mode. Regarding blink duration, in turn, while the authors found no differences between the dual and single task conditions, in comparison with the rest mode condition, blink duration was longer on both tasks. The authors also found similar results regarding fixation duration as well. As for saccade amplitude, this metric was only employed in [43]. In their study, the authors found that in a collaborative scenario, in comparison with the manual modality, saccade amplitude was higher, indicating a reduction in the subject's cognitive load. In that same study, the authors also analyzed the fixation rate and found that it increased
# The Gyrotrons as Promising Radiation Sources for THz Sensing and Imaging
## 2 Advantages of the Gyrotrons as Powerful Radiation Sources for Sensing and Imaging
### Advanced Spectroscopic Techniques
#### 3.1.1 DNP-NMR and ESR Spectroscopy
A photo of this unique tube is shown in Figure 4.  
Analogously to DNP-NMR, the sensitivity of the ESR spectroscopy increases significantly, advancing toward higher intensities of the magnetic field and correspondingly to higher frequencies. Another possibility for increasing the sensitivity and spectrum acquisition time is the usage of coherent pulses instead of CW radiation. In order to realize this advanced concept, novel instrumentation for pulsed ESR (also referred to as Fourier transform ESR) has been developed recently [72] at FIR UF. In the measuring system, the gyrotron FU VIIA is used as a radiation source and short millimeter-wave pulses with a controllable time delay are generated by a light controlled semiconductor shutter. The used experimental arrangement allows implementing the method of electron spin-echo envelope modulation (ESEEM) in the measurements. An important advantage of ESEEM is that it makes it possible to measure the relaxation times of the electron spins, which is important for the materials science studies on compounds with short relaxation times.
## 4 Experimental Results and Analysis
### Data Collection
By placing it at the front of the vehicle, we obtained a perspective that closely resembles the driver's view, allowing us to capture the road scenes from a realistic standpoint. This camera provided clear and detailed images that were instrumental in analyzing the road conditions and extracting relevant visual features, some examples of which are shown in Figure 4. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Fuel Type** & **Variables** & **Data Size** & **Minimum** & **Maximum** & **Mean** & **STD** \\ \hline \multirow{6}{*}{Diesel} & CO\({}_{2}\) (g/s) & \multirow{6}{*}{28,972 (Rows)} & 6.51 \(\times\) 10\({}^{-7}\) & 16.359 & 3.102 & 3.009 \\ \cline{2-6}  & CO (g/s) & & 3.38 \(\times\) 10\({}^{-10}\) & 0.368 & 1.3 \(\times\) 10\({}^{-3}\) & 0.01 \\ \cline{2-6}  & O\({}_{2}\) (g/s) & & 0.0002 & 17.398 & 3.818 & 2.389 \\ \cline{2-6}  & NO (g/s) & & 2.74 \(\times\) 10\({}^{-8}\) & 0.124 & 0.014 & 0.018 \\ \cline{2-6}  & NO\({}_{2}\) (g/s) & & 2.01 \(\times\) 10\({}^{-8}\) & 0.0198 & 0.0025 & 0.0032 \\ \cline{2-6}  & Vehicle Speed (km/h) & & 0 & 136.899 & 62.876 & 39.623 \\ \cline{2-6}  & Ambient Pressure (kPa) & & 994 & 1025 & 1007.672 & 35.875 \\ \cline{2-6}  & Ambient Humidity (\%) & & 29.2 & 100.7 & 67.917 & 15.910 \\ \cline{2-6}  & Ambient Temperature (K) & & 290.75 & 301.85 & 293.917 & 2.471 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of PEMS data in VAD. Figure 2: PEMS installed in our test vehicle. During the data collection process, we captured images at a frequency of 5 images per second, with a frame rate of 5 frames per second (\(\text{fps}=5\)), with a shape of 1920 \(\times\) 1080. This sampling rate provided a balance between capturing sufficient visual data and minimizing storage requirements. The 5 fps frequency allowed us to capture a series of images representing the temporal progression of the road scene during different driving scenarios. Figure 4: Some examples of our collected images. Figure 3: Intel RealSense camera mounted on the front of the vehicle. By analyzing these images, we were able to detect and classify road infrastructure elements such as lanes, road signs, and traffic lights. Additionally, the images also provided visual clues about traffic flow, congestion, and environmental factors that could affect vehicle emissions. Table 2 provides details regarding our cycles and experiments conducted during the data collection process.