esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Dual-Depth Head-Up Displays on Vehicle Driver Performance
## 3 Results
### Statistical Methods
#### (c) EEG Discussion  
The brainwave energy changes of EEG points F3, F4, P3, P4, O1, and O2 were measured in this study. It can be seen from Figure 8 that the P3 point of the single-depth HUD is less than that of the dual-depth HUD only from minute 11 to minute 20, and the mental load of the single-depth HUD is greater than that of the dual-depth HUD in the rest of the periods. The mental load of the O2 point in the dual-depth HUD is less than that of te single-depth HUD only at the first to tenth point and is greater than that of the single-depth HUD in other periods. The NASA-TLX questionnaire results show that the subjective workload of the single-depth HUD is statistically significantly higher than that of the dual-depth HUD; please refer to Table 2. According to the above results, P3 is located in the parietal lobe of the brain, which mainly controls the motor nerve center and processes various sensory information. Therefore, it can be confirmed that the single-depth HUD spends more energy in processing complex information and controlling the motor nerve than the dual-depth HUD. O2 is located in the occipital lobe of the brain and mainly processes visual-related  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Mean \(\pm\) SD**} \\ \cline{2-4}  & **Single-Depth** & **Dual-Depth** & \(\mathbf{p}\) \\ \hline F3 & 2.212 \(\pm\) 0.517 & 2.209 \(\pm\) 0.511 & 0.982 \\ F4 & 1.968 \(\pm\) 0.390 & 1.949 \(\pm\) 0.407 & 0.208 \\ P3 & 2.108 \(\pm\) 0.543 & 2.030 \(\pm\) 0.580 & 0.004 ** \\ P4 & 5.499 \(\pm\) 2.180 & 5.621 \(\pm\) 2.241 & 0.253 \\ O1 & 1.988 \(\pm\) 0.372 & 1.982 \(\pm\) 0.438 & 0.511 \\ O2 & 2.084 \(\pm\) 0.483 & 2.160 \(\pm\) 0.474 & 0.000 ** \\ \hline \hline \end{tabular}  
* \(p<0.01\), ** \(p<0.001\).  
\end{table}
Table 1: EEG energy intensity statistics.  
Figure 8: Time activity diagram of the driving process over time.  
information. It can be confirmed from the results that the mental consumption of the dual-depth HUD in processing vision-related information is greater than that of the single-depth HUD. This study is the same as that of [PERSON] et al.'s study on the relationship between the NASA-TLX workload questionnaire and EEG fatigue [30]: when the mental state shifts from alertness to fatigue, the energy of the frontal and parietal lobes will increase significantly, and the driver's workload will increase according to complexity [31]. It can be seen from the results that participants have a higher workload when using the single-depth HUD than the dual-depth HUD most of the time.
## 3 Joint Resource Allocation and Trajectory Planning under Known Users' Location
### UAV's Trajectory Planning  
For given computation offloading variables, the UAV's trajectory optimization problem **P4** can be expressed as  
\[\begin{split}\textbf{P4}:&\max_{\mathbf{q}_{u}[t]} \min_{n\in\mathcal{N}}\sum_{t=1}^{T}\eta g_{n}[t]P_{u}\\ s.&\text{\emph{f.}}&\text{\emph{C1}}, \text{\emph{C2}},\text{\emph{C5}},\text{\emph{C6}}.\end{split} \tag{18}\]  
Due to the objective function of **P4** being non-concave and the constraint C2 being non-convex with respect to \(\mathbf{q}_{u}[t]\), the problem **P4** is non-convex. For this problem, we choose the SCA method to solve.  
By adopting the SCA method, we can obtain  
\[\begin{split}& r_{tr,n}[t]\geq r_{tr,n}^{low}=B\log_{2}\left(1+ \frac{P_{o,n}[t]\beta_{0}}{\sigma^{2}(H^{2}+l_{n}^{j}[t]^{2})}\right)\\ &-\frac{P_{o,n}[t]\beta_{0}log_{2}e(l_{n}[t]^{2}-l_{n}^{j}[t]^{2} )}{(H^{2}+l_{n}^{j}[t]^{2})(\sigma^{2}H^{2}+\sigma^{2}l_{n}^{j}[t]^{2}+P_{o,n} [t]\beta_{0})},\end{split} \tag{19}\]  
where \(l_{n}[t]=\parallel\mathbf{q}_{u}[t]-\mathbf{q}_{u}\parallel\), the \(r_{k}^{low}\) is the lower bound of \(r_{n}[t]\), and the equality holds when \(l_{n}[t]=l_{n}^{j}[t]\).  
Similarly, we can obtain the lower bound \(g_{n}^{low}\) of \(g_{n}[t]\):  
\[g_{n}[t]\geq g_{n}^{low}=\frac{\beta_{0}(H^{2}+2l_{n}^{j}[t]^{2}-l_{n}[t]^{2})}{(H ^{2}+l_{n}^{j}[t]^{2})^{2}}. \tag{20}\]  
According to Formulas (19) and (20), we can transform the problem **P4** to  
\[\textbf{P4.1}: \max_{\textbf{q}_{n}[t]}\sum_{t=1}^{N}\eta g_{n}^{low}P_{u} \tag{21a}\] \[s.t. C2.1: \sum_{t=1}^{T}\frac{f_{n}[t]}{\mathbb{C}_{n}}+\sum_{t=1}^{T-1}r_{tr, n}^{low}\geq R_{n},\forall n\in N,\] (21b) \[C1,C5,C6. \tag{21c}\]  
In problem **P4.1**, we can find that the objective function and the constraint C2.1 are both convex with respect to \(\textbf{q}_{u}[t]\). Thus, the problem **P4.1** is a convex problem. We can use CVX to solve this problem.
###### Abstract  
Stay cables are one of the vital components of a cable-stayed bridge. Due to their flexible nature, stay cables are vulnerable to external excitation and often vibrate with large amplitude under wind action which leads to the fatigue failure of the cables. To suppress such kind of large amplitude vibration by improving the damping ratio of the cable various dampers such as magnetorheological damper, friction damper; oil damper; or high damping rubber (HDR) damper are utilized and gained popularity over time. This paper focuses on improving the damping ratio of stay cables using a combination of two HDR dampers. First, the theoretical model is formulated considering cable bending stiffness to evaluate the damping effect of cable-HDR dampers system. Then, the impact of various design parameters of HDR dampers on cable damping considering the cable stiffness is performed. The comparative analysis of results shows that the considered parameters such as loss factor, spring factor, and installation location of dampers have much effect on the stay cables damping ratio. Finally, the optimal parameters of the two HDR dampers are proposed for damper design.  
two HDR dampers; stay cable; damping ratio; spring factor; loss factor; cable bending stiffness
## 3 Results and Discussion
### Analysis of Physicochemical Characteristics
#### 3.4.3 Thermal Stability Analysis  
The TGA and DTG curves of three chitosan samples are presented in Figure 4. The thermal degradation process can be roughly divided into three stages: before 200 \({}^{\circ}\)C, between 200-450 \({}^{\circ}\)C and after 450 \({}^{\circ}\)C according to the curves, which are respectively attributed to the loss of bound water, main chain fracture and complete carbonization of chitosan [43]. All samples had a slight weight loss before 100 \({}^{\circ}\)C, which was caused by the precipitation of trace water from chitosan. The initial mass loss of CS was 8.3%, while the mass loss of CSC and CSS were 10.1% and 8.7% respectively, indicating that chitosan prepared by high temperature and a high-pressure flash explosion could absorb more water in the form of bound water and unbound water, and could remove a certain amount of water at high temperature. The thermal decomposition mainly occurs at 250-350 \({}^{\circ}\)C, during which the O-bridge of the chitosan main chain and acetyl group broke, and the thermal weight loss was very slow after 450 \({}^{\circ}\)C. There was a strong weight loss peak on the curve at around 300 \({}^{\circ}\)C, where the thermal decomposition rate of chitosan reached the maximum. The maximum weight loss of CS was 59.1% at 308.09 \({}^{\circ}\)C, and that of CSC and CSS was 53.1% and 51.2% at 293.31 \({}^{\circ}\)C and 293.55 \({}^{\circ}\)C, respectively. The different thermal decomposition behaviors of \(\alpha\)-chitosan and \(\beta\)-chitosan may be attributed to the distinct intermolecular forces in their different crystalline structures, especially the loss of hydrogen bonds.  
It can be observed from the endothermic peak in the DSC curve spectrum of the tested samples in Figure 4 that the exothermic peak temperature of CS (129.11 \({}^{\circ}\)C) is the highest, followed by CSC (126.28 \({}^{\circ}\)C) and CSS (119.60 \({}^{\circ}\)C), and the results show the same trend as TGA and DTG. The M\({}_{\rm W}\), DD, source and extraction method are important factors affecting the TS of chitosan.  
The quantitative calculation formula of TS value is as follows: maximum point temperature of weight loss rate \(\times\) thermal degradation temperature range \(\times\) 10\({}^{-4}\). The chitosan TS values are shown in Table 3.  
Figure 3: Water-binding capacity (WBC) and fat-binding capacity (FBC) of chitosan. Different small letters under the data in the same group indicate significantly different (\(p\) < 0.05).
## 1 Introduction  
As a result of the new round of hydrogen energy economy flourish, hydrogen production from coal is receiving increased research focus. Hydrogen is vital to future energy systems. It is clean, carbon free, and an important material for chemical industries [1; 2; 3]. Moreover, H\({}_{2}\) can be used to generate power, drive cars, and synthesize chemicals such as methanol and ethanol, but unfortunately cannot be gained from natural gas reservoirs [4; 5]. Therefore, there is great significance in retrieving efficient hydrogen production from other sources. Compared with other hydrogen production options for China, coal to H\({}_{2}\) has a strategic importance due to the country's large amount of coal reserves.  
Coal to hydrogen technology is based on coal gasification, which originated early last century [6; 7]. The process has been developed over the past 20 years in China in conjunction with several commercialized projects [8; 9]. The basic process involves coal gasification, water gas shift (WGS), and gas separation. Coal gasification is the main unit that decides the whole plant efficiency. Currently, the majority of coal to hydrogen processes employ GE and Shell gasification, which uses oxygen as an oxidant. Further, the cold gas efficiencies are typically lower than 80% [10; 11]. Nevertheless, the low cold gas efficiency and use of oxygen limits coals hydrogen efficiency to less than 60% [12; 13]. In recent years, several alternative coal gasification technologies have been proposed, including Huaneng TPRI (Thermal Power Research Institute) and transport integrated gasification (TRIG). These new technologies have a higher cold gas efficiency and are able to improve coal to hydrogen efficiency [14; 15]. However, most of these technologies still need oxygen for gasification, a process which results in huge power consumption. Another coal to hydrogen process is based on supercritical water gasification [16; 17; 18]. This technology does not require pure oxygen and is expected to be highly efficient. Many previous experiments have been carried out to prove these findings [16; 17; 18]. For example, hydrogen mole fraction for this technology was reported to be higher than 70% [16; 17; 18]. Nevertheless, supercritical water coal gasification operates over 20 MPa and may result in a higher cost for the gasifier. There are some publications relative to the topic of hydrogen production through steam gasification of coal, char, and biomass [19; 20; 21; 22; 23]. Herein, thermodynamic and kinetic analysis are presented.  
In our previous paper [24], a two-step chemical regenerative coal gasification was proposed and a much higher cold gas efficiency was reported. Moreover, in the two-step gasification, gas with high H\({}_{2}\) concentration was produced. In this paper, a coal to hydrogen process based on this gasification is presented. Experiments were carried out in fixed bed reactors and components of syngas, H\({}_{2}\), are reported. Additionally, thermodynamic performance of the whole process is simulated.
## 2 Materials and Methods
### PLM Feature Embedding  
To capture rich contextual information and evolutionary relationships inherent in protein sequences, we utilized pre-trained PLMs such as ESM-1b, ESM2, and ProtBert. These models are all based on the Transformer architecture [23]. PLMs, trained on extensive protein sequence datasets, have shown significant success in capturing complex patterns and evolutionary dependencies essential for understanding protein function [24].  
ESM-1b and ESM2 are large-scale models, each comprising 33 Transformer layers and approximately 650 million parameters [11; 25]. ProtBert, another prominent PLM, is derived from Google's BERT model and pre-trained on a massive dataset of 100 million protein sequences [26]. This pre-training allows ProtBert to learn general representations of protein sequences, effectively capturing the \"language\" of proteins. The depth and capacity of these models (ESM-1b, ESM2, and ProtBert) enable them to effectively learn intricate patterns in protein sequences [27], capturing both local and long-range interactions vital for accurate protein function prediction.  
Figure 2 schematically illustrates the process of extracting and aggregating protein features from these pre-trained language models. Specifically, as the sequence propagates through the Transformer layers, the model generates contextualized embeddings for each amino acid at each layer, capturing both the identity and the contextual information of each amino acid within the sequence [28]. This enables the model to discern the influence of neighboring amino acids on protein function. Hidden states from the last (33 rd) layer of both ESM-1b and ESM2-650M models, and the output of the final layer for ProtBert, were extracted as feature representations for each protein sequence. Specifically, for each protein sequence, the embeddings of all amino acids from the last layer were averaged to produce a single, fixed-length feature vector. Both ESM-1b and ESM2 models yield 1280-dimensional feature vectors (embeddings) for each amino acid, and thus the mean embedding for an entire protein sequence also results in a 1280-dimensional vector. ProtBert, similarly, produces 1024-dimensional feature vectors for each amino acid, resulting in a 1024-dimensional mean embedding for the entire sequence.  
Using these pre-trained PLMs, we extracted highly informative and contextualized representations of protein sequences, capturing intricate patterns and evolutionary dependencies essential for FRP prediction. The substantial parameter size and depth of ESM-1b, ESM2, and ProtBert enable them to directly learn complex representations from the data, outperforming traditional methods reliant on handcrafted features. This approach improves prediction accuracy by leveraging deep learning and large-scale pre-training to model the complex biological properties of protein sequences.
## 5 Simulation Results
### Random Networks
When the random topology is used, however, a node has more neighboring nodes within the average transmission range and some other nodes outsider but close to the average transmission range. Both Type (i) and Type (ii) events are equal-likely to happen, and we see similar improvements for both shadowing channels and near-deterministic channels. There are many factors that affect the performance of a MAC protocol in multi-hop wireless networks, such as topologies, traffic patterns, and channel parameters. Overall our simulation results demonstrate that the proposed location-assisted MAC protocol is effective in achieving considerable throughput and delay improvements over the IEEE 802.11 MAC under various circumstances of log-normal shadowing  
Figure 13: Simulation results for random networks when \(\sigma_{dB}\) is increased from 0.01 dB to 12 dB. (a) Throughput. (b) Delay. channels. The improvements clearly justify the need and the benefits of opportunistically scheduling concurrent transmissions while considering log-normal shadowing channels.
## 3 Materials and Methods
### Method  
#### 3.2.1 Network Structure and Performance Measurements  
The network structure is usually described from three levels: the global level, the meso level, and the individual level. Measurements from the three levels reflect the various features of whole, subgroups, and single nodes in a network, respectively [43, 68]. The global-level metrics reflect the topological characteristics and the whole function, the meso-level metrics indicate the degree of hierarchy within the network, while the individual-level metrics demonstrate the degree of centralization and density, the strength of ties and the  
Figure 1: The framework of research. extent of structural equivalence between different segments of the network [68; 69; 70]. The two dimensions of network structure, the global level and the individual level, are regarded as the fundamentals that determine the network's efficiency; thus, this paper only focuses on the two-level structural metrics' impact. The measurements this paper adopts are listed in Table 1. All the structural metrics are commonly mentioned in the complex network research and used to reflect the network structure comprehensively [68; 71]. Since the metrics of two levels show the network structure in different scales, we take two indicators to reflect the international tourism industrial performance (Table 1): at the global level, the amount of average international tourism arrivals is considered (because the number of participants is dynamic); at the individual level, the international visitors from different regions are employed (to show the tourism industrial performance between each two regions). The previous research foundation in Section 2.1 has shown that different metrics generate different impacts. Considering the mean of each metric and others' relative research results, this paper proposes hypotheses as shown in Figure 2. Firstly, the influences of the global-level structure of GNT on tourism industrial performance are recognized from Hypothesis 1.
## 2 Basic Definitions
### Umda  
Distribution Estimation Algorithms (DEAs), are evolutionary algorithms that use a collection of candidate solutions to perform search paths avoiding local minima [16; 17]. These algorithms use the estimation and simulation of the joint probability distribution as an evolutionary mechanism instead of directly manipulating the individuals that represent solutions to the problem. In a DEA, a population of individuals represents solutions to the problem. Three types of operations are iteratively performed on the population: generating a subset of the best individuals in the population, learning a probability distribution model from the selected individuals, and new individuals are generated by simulating the obtained distribution model. The algorithm stops when a certain number of generations is reached or when the population's performance stops significantly improving.  
To estimate the joint probability distribution at each generation from the selected individuals, the univariate marginal distribution algorithm (UMDA) is used. Thus, the joint probability distribution is factored as the product of independent univariate distributions [16; 18], i.e.,  
\[p_{l}(x)=p(x\mid D_{l-1}^{Se})=\prod_{i=1}^{n}p_{l}(x_{i}). \tag{7}\]  
Each univariate probability distribution is estimated from the marginal frequencies:  
\[p_{l}(x_{i})=\frac{\sum_{j=1}^{n}\delta_{j}(X_{i}=x_{i}\mid D_{l-1}^{Se})}{N} \tag{8}\]  
where  
\[\delta_{j}(X_{i}=x_{i}\mid D_{l-1}^{Se})=\left\{\begin{array}{c}0\ if\ in\ j-th\ D_{l-1}^{Se},X_{i}=x_{i}\\ 1\ in\ other\ case\end{array}\right.. \tag{9}\]  
The definitions in this section are explicitly referenced in the following sections.
# Propagation Attenuation Maps Based on Parabolic Equation Method +
## 5 PAM Accuracy Evaluation versus Angular Resolution of Terrain Profiles
Moreover, the graph shows that the error difference for \(\Delta\alpha=5^{\circ}\) and \(\Delta\alpha=10^{\circ}\) is small. In this case, we can obtain almost unchanged PAM accuracy with a two-fold reduction in the number of terrain profiles and a two-fold reduction in computation time. The accuracy of PAM generation depends not only on the angular resolution of the profiles but also on their length (i.e., \(R_{0}\)), i.e., the PAM matrix dimension. A more detailed analysis of the PAM accuracy will be presented in our next work.
## 4 Lifetime Improvement Mechanisms in Routing
### Mobile Sink Mechanism
#### 4.2.2 Fixed Path/Predictable Sink Mobility  
[PERSON] and [PERSON] [9] proposed a protocol based on fixed path/predictable sink mobility and joint routing in order to enhance the network lifetime. They realized that the maximum lifetime for the wireless sensor network could only be achieved if the sink path covers the periphery of the sensor field. The algorithm uses two patterns for the sink movement while the sensor nodes are distributed in a circular-shape network. The first one is a discrete mobility strategy in which a sink can move and stop periodically. If the protocol employs a time synchronization method among the sensor nodes accompanied with a time scheduling for discrete trajectory, each sensor node knows the position of the sink at a given time and consequently can select the best path for data transmission. The other one is a continuous mobility pattern with the help of location information. In this method, the network topology may change due to the mobility, so the sink node should broadcast a data query time by time in order to update the routing table of the nodes. An online route construction strategy is possible if the sensor nodes and the sink are aware of their location and the sink node announces its position to all nodes.  
Joint routing is shown in Figure 4, where the network is partitioned into two parts: a circle of radius \(R_{m}<R\) and the annulus-shape area between the periphery of the network and the mentioned circle. The sink trajectory is only the circle of radius \(R_{m}\). There are two kinds of routings towards the mobile sink as follows: (1) the sensor nodes that located within the inner circle should transmit the data on shortest path; (2) the packets sent by the other nodes within the annulus area take a round routing around the center \(O\) to reach \(OB\) position. After that, they will be forwarded to the mobile sink through a short path. The direction of round routing should be in such a way that the overall traversed distance will be minimized. The philosophy behind the joint strategy is that through reducing the radius of the network from \(R\) to \(R_{m}\), it can limit the area that employs short path routing and hence achieve a lower network load.  
Although the joint routing mechanism is extremely dependent on the shape of the sensor field, the experimental results [9] show that in comparison with static base station, it can enhance the network lifetime more than 500% by exploiting the potential energy capacity all over the network. However, the packets produced by the nodes in an annulus-shaped area may experience more end-to-end delays due to the circular routing strategy.