esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Visual Acuity and Refractive Error Measurement  
Static visual acuity for distance was measured using the ETDRS chart positioned at a distance of 5 m. Each participant was instructed to read characters on the chart until they could no longer distinguish them. The right eye was assessed first, followed by the left eye, and finally, binocular visual acuity was assessed. For the purposes of this study, good visual acuity was defined as a binocular LogMAR value of 0.00 or better (equivalent to 20/20, lower LogMAR scores indicate superior visual function).  
Refractive error was assessed using a Heine Beta 200 retinoscope (HEINE Optotechnic, Munich, Germany), followed by subjective refraction to ensure the most accurate correction for each player. Subjective refraction began with monocular measurements, followed by bi-ocular balance, and concluded with binocular refinement. Objective retinoscopy served as the starting point for subjective refraction and employed the conventional endpoint of maximum plus for the best visual acuity, followed by Jackson cross-cylinder refinement to determine the axis within 5 degrees and cylinder power within 0.25 D. Spherocylindrical findings, comprising the sphere, cylinder, and axis, were transformed into vector representations using Fourier analysis, as described by [PERSON] et al. [26]. This approach enables a quantitative evaluation of refractive components, including the calculation of vectors J0 (horizontal and vertical astigmatism), J45 (oblique astigmatism), and M (spherical equivalent). Myopia was defined as M \(\leq-0.50\) D, emmetropia as \(-0.50\) D \(<\) M \(<+0.50\) D, and hyperopia as M \(\geq+0.50\) D. Anisometropia was determined by the absolute difference in the M between the two eyes, while anisoastigmatism was evaluated by calculating the absolute differences in the J0 and J45 astigmatic components between the right and left eyes. These metrics provided a detailed assessment of binocular asymmetries in refractive components, which may influence both visual processing and postural stability.
## 4 Multihoming and Interworking
### Gateway Selection and Preservation
#### 4.2.2 Global Address Preservation (GAP)  
When a destination GLI-domain is multihomed, packets in the forward direction of a connection may take a different GLI-gateway than packets in the reverse direction. This may result in different global GLI-addresses at the initial sender and to the violation on the transport layer explained in Section 4.1. When the GAP-bit (see Figure 3) is activated in the global GLI-address of a packet's destination, the GAP-mechanism is triggered at the GLI-gateway of the destination domain to preserve the global destination address of request packets as the global source address of potential response packets. To that end, the GLI-gateway adds an address buffer to the packet storing the currently used global GLI-address of the destination before substituting this address by a local GLI-address. The destination node recognizes the activated GAP-bit of the global GLI-address in the address buffer and stores it. When response packets of the same connection are sent, the GLI-node uses gateway selection for these packets to the respective GLI-gateway. Thereby, the initial sender just sees swapped source and destination addresses and no violations on the transport layer occur.
# _algorithms_
## 4 Experiments and Analysis  
### Chinese Sentiment Analysis  
We chose two public datasets for sentiment analysis, as shown in Table 1. Weibo Dataset had nearly 120,000 samples with two balanced labels. They were collected from Sina Weibo. When it came to ChnSentiCorp-htl-all, it was organized by Songbo Tan, and had 7766 samples with unbalanced distribution of labels. Accuracy and F1 were used as indicators of the model's performance.  
In our experiment, the binary _BOW_ vector was the model's input. Then, positive polarity clauses provided evidence on the presence of a class, while negative polarity clauses provided evidence on the absence of the class. Finally, TM sums scores from all clauses, and the class with the maximum score, were the target output. We used five-fold  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Positive Number** & **Negative Number** \\ \hline Weibo-Senti-100k & 59,993 & 59,995 \\ \hline ChnSentiCorp-htl-all & 5327 & 2439 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of Weibo-Senti-100k and ChnSentiCorp-htl-all.  
Figure 6: Learning process of Tsetlin Machine.  
cross-validation to ensure the credibility of the experimental results. The dataset was randomly divided into 5 parts, four parts used for training and the other for testing). The scores were averaged as our final result.  
We determined the optimal parameters of TM through extensive experiments, and set Clause number to 700, \(s\) to 3, Threshold to 9000. The symbol \"-\" represents those missing results which were not given in the original papers. For Weibo-senti-100k, we referred to previous work as baselines, and the evaluation results are shown in Table 2. For models not pre-trained, we used Sougou pre-trained word vectors to do word embedding, and set the batch size to 64. As shown in Table 2, TM outperformed all previous work in terms of both Accuracy and F1 score, and even outperformed some Bert-based ones.  
For evaluation in ChnSentiCorp-htl-all, we used Sougou pre-trained word vectors to do word embedding as input for models not pre-trained, and set the batch size to 64. For BERT, we used a base version pre-trained from Chinese which had 6-layer transformers, and set the batch size as 32.  
As shown in Table 3, for F1-score, TM outperformed previous work, such as SINM and BAM, by one percentage, but was one percentage lower than the effect of BERT. We think this was because the extra knowledge learned by BERT through the pre-train mechanism was prominent in this scenario.
# Rearrangement of Single Atoms by Solving Assignment Problems via Convolutional Neural Network
## 1 Introduction
First, all vacancies in the target arrays must be filled, and, second, the moves must avoid collision of atoms. It is known that atoms in optical traps have a finite trapping time and can also be lost during transportation with a certain probability given as a function of time and distance. The time is the total computation time of the algorithm and atom transportation time. In the case of a half-filled array, transportation time does not depend much on the initial arrays but changes significantly on choice of algorithm. Thus, the efficiency of atom rearrangement relies mostly on the chosen algorithm. Rearranging atoms (pairing atoms from the trapping sites to the target sites) can be seen as a combinatorial optimization problem, to be more specific, an assignment problem with a carefully designed cost function. However, only a few tailored algorithms have been developed for choosing atoms and their corresponding target sites. Only the Hungarian matching algorithm, a well-known linear programming technique to solve an assignment problem, is frequently mentioned in the literature on rearranging atom formation. When compared to other developed methods for solving an assignment problem, such as greedy algorithm or other metaheuristic problems, the Hungarian method is relatively slow and inefficient, especially when the problem is quite large scale as in the atom rearrangement problem [15]. Therefore, in this paper, we are interested in developing a faster way to solve the atom rearrangement problem. The orientation of the paper starts with the process of designing the cost function for the assignment problem to prevent the collision of atoms, and all vacancies in target array will be filled by the constraints of the assignment problem. The model we developed can provide an optimal solution which is the optimal matching to move the atoms in one single movement to prevent atom losses in multiple atom-moving processes. As solving an assignment problem is slow, especially for a larger-size array, we offer Convolutional Neural Network (CNN) models to predict the optimal solutions of the assignment problems for three grid sizes of \(10\times 10\), \(13\times 13\), and \(21\times 21\). Thisdrastically shortens the time to find the optimal solutions. Finally, we present the accuracy and calculation times for each model.
## 10 Results and Discussion
### Packet Loss Ratio  
The packet loss ratio (PLR) is a network performance metric that measures the proportion of packets that are lost or discarded during data transmission across a network. The packet loss ratio over time for the GBG-RPL and BCPS-RPL mechanisms is shown in Figure 7.  
For both the GBG-RPL and BCPS-RPL processes, Figure 7 shows a declining trajectory of the packet loss ratio with time. The GBG-RPL mechanism consistently maintains a marginally lower packet loss ratio than the BCPS-RPL mechanism at each time interval, demonstrating a higher individual packet transmission efficiency. The packet loss ratio of the GBG-RPL is better than that of BCPS-RPL due to its better detection capability to identify and isolate the blackhole and greyhole nodes in the CPS network. Initially, there is less difference in the PLR of both techniques; however, the GBG-RPL exhibits further improvement in PLR. The high PLR of BCPS-RPL is due to two major factors. Firstly, it is due to the large volume of packet transmission in the device layer, which results in network congestion. Secondly, it is due to delayed detection of the malicious nodes, which causes packet drop. In the case of the GBG-RPL, the improvement is attributed firstly to the shifting of all trust calculations from the device layer (sensor nodes) to the fog layer (fog server), secondly to the careful selection of the significant trust parameters, and thirdly to the trickle timer.
## 3 Experiment and Results Analysis
### Experimental Results and Analysis
#### 3.4.2 Results and Analysis of Experimental Ablation  
Considering the advancements made in this study, the ablation experiment validated the efficacy of both the character-word feature fusion strategy and the incorporation of GHM loss function. The ablation experiment was conducted using RoBERTa-BiGRU-CRF  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Experimental Model**} & \multicolumn{3}{c}{**Audit Datasets**} & \multicolumn{3}{c}{**Resume Datasets**} \\ \cline{2-7}  & **Accuracy** & **Recall** & **F1** & **Accuracy** & **Recall** & **F1** \\ \hline Word2 Vec-BiLSTM-CRF & 87.06 & 94.14 & 90.46 & 93.66 & 93.31 & 93.48 \\ SoftLexicon & 91.25 & 92.17 & 91.71 & 95.30 & 95.77 & 95.53 \\ BERT-BiLSTM-CRF & 91.78 & 94.52 & 93.13 & 95.75 & 95.28 & 95.51 \\ RoBERTa-BiLSTM-CRF & 94.69 & 92.52 & 93.59 & 94.96 & 95.22 & 95.09 \\ RoBERTa-BiGRU-CRF & 95.76 & 92.79 & 94.25 & 95.13 & 95.25 & 95.20 \\ RoBERTa-BiGRU-CRF-FL & 96.05 & 94.19 & 95.11 & 95.43 & 95.27 & 95.35 \\ CW-CBGC & 97.38 & 96.56 & 97.23 & 96.35 & 96.18 & 96.26 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of comparative experiments.  
as the baseline model, denoting the introduction of GHM loss function as +GHM and incorporating character-word feature fusion strategy as +CW. The results of the ablation experiment are presented in Table 6.  
According to Table 6, the incorporation of character-word features fusion strategy in +CW significantly enhances its three major indicators compared to the baseline model. This substantiates that integrating word-level information features effectively improves the accuracy of entity recognition models, further emphasizing their significance for audit text entity recognition tasks. On the other hand, +GHM enhances the loss function compared to the baseline model and exhibits improved recall rate and F1 value. This demonstrates that introducing GHM loss function enhances the model's ability to identify challenging entities, enabling it to recognize more audit entities. However, there is a slight decrease in accuracy primarily influenced by confusion in entity boundary recognition. When both strategies are simultaneously introduced -- incorporating character-word feature fusion strategy and GHM classification loss function--this study's model achieves an increase of 1.11% and 1.87%, respectively, in F1 value compared to models with individual enhancements mentioned earlier. These findings further validate that leveraging word-level information features comprehensively improves model accuracy for identifying difficult-to-separate entities when both strategies are employed together. In conclusion, this paper's proposed enhanced strategies have proven effective for improving the performance of our model.
# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 5 Energy-Balancing Techniques
### Mobility-Based Techniques  
In a typical large-scale WSN, the base station (sink) is static. As such, the data from the sensor nodes are transmitted to the base station through multi-hop communications. Hence, some sensor nodes in the WSN would not only sense and send their data but also act as wireless relays that forward the data of their neighbors toward the sink. Consequently, nodes near the sink experience battery depletion faster, leading to nonuniform energy consumption, which eventually causes the development of an energy hole in the WSN. The energy hole disables the WSN and thus reduces its lifetime regardless of the fact that there are still a number of sensor nodes in the WSN whose batteries are not yet depleted.  
In recent years, contrary to static sink, the mobile sink approach has attracted much research interest because of the increase in its potential WSN applications and its potential to improve network performance, such as energy efficiency and throughput [112]. Mobile sink schemes involve the deployment of a mobile sink or data collection point that moves through the network to gather data from sensor nodes. The rationale behind this approach is to redistribute the energy burden by moving the sink closer to the nodes with lower residual energy, allowing the sensor nodes to transmit data opportunistically when the sink is in proximity. This reduces the need for long-distance transmissions and provides relief to the nodes with higher energy consumption. Thus, the movement of the sink within the network helps to uniformly spread the energy consumption [36]. However, this solution is not very common since the sink in most WSN applications is static.  
Mobile relay schemes involve the use of mobile nodes as relays to assist in data forwarding. These mobile relays move strategically to areas with higher energy consumption or congestion, providing temporary support and alleviating the burden on stationary sensor nodes. The rationale is to dynamically adjust the network topology, enabling efficient data relay and reducing the energy strain on specific nodes.  
As shown in Table 6, mobility-based techniques are highly effective in LWSNs, as they significantly reduce the need for multi-hop communication, which is prevalent due to the constraints imposed by the linear topology.
## 2 Nanodiamonds Synthesis Techniques
### Detonation Synthesis  
In detonation synthesis, the energy of an explosion is used to drive the diamond phase formation [28]. In a closed metallic chamber, explosives with a negative oxygen balance are detonated; usually a mixture of 60% TNT (C\({}_{6}\)H\({}_{2}\)(NO\({}_{2}\))CH\({}_{3}\)) and 40% hexogen (C\({}_{3}\)H\({}_{6}\)N\({}_{6}\)O\({}_{6}\)) is employed (Figure 2) [1]. The carbon atoms that eventually form the NDs are provided either by the molecules of the explosives or by precursor graphite put inside the detonation chamber. The synthesis is called \"dry\" or \"wet\" when the chamber is filled with a gas (N\({}_{2}\), Ar, CO\({}_{2}\)) or with water (ice), respectively, that act as coolants. The cooling media play an important role in the carbon yield, which is usually around 10% of the explosive weight [29]. The process leading to NDs formation is schematized in Figure 2. After detonation, the carbon atoms released during the explosive molecule's dissociation condense and crystallize into nanoclusters [30]. The pressure/temperature increase (Figure 1) reached in the chamber during the detonation leads to crystallization of the carbon nanoclusters into diamond phase. Finally, the formed NDs grow and agglomerate, resulting in NDs of size 4-5 nm. The main drawback of this technique is that the resulting sample, called detonation soot, must be purified to remove contaminants. Indeed, detonation soot is not only made of diamond phase, but also of non-diamond carbon (25-85 wt %) and metals coming from the wall of the chamber (1-8 wt %) [31]. To remove the impurities, cleaning of the detonation soot in a strong acid environment (usually a  
Figure 1: Carbon phase diagram indicating graphite (grey), diamond (white), and liquid (yellow). In addition, pressure-temperature conditions reached with different synthesis technique are indicated. HPHT: high pressure, high temperature. PECVD: Plasma Enhanced Chemical Vapor Deposition.  
mixture of HNO3/H2 SO4/HClO4) is required [28], which is a dangerous and expensive process. Another problem is NDs agglomeration occurring during synthesis, where NDs clusters of several hundreds of nanometers are obtained. To isolate the single 4-5 nm sized NDs, milling with ceramic microbeads (that again introduces contaminants) or ultrasonic disintegration are the standard de-aggregation methods [32].
# Study on Reinforcement Mechanism and Reinforcement Effect of Saturated Soil with a Weak Layer by DC
## 4 Numerical Analysis
### Parametric Analysis
#### 4.2.4 Effect of Groundwater Table  
Influences of the groundwater table on the compaction effect of DC to the grounds were investigated by performing numerical simulations with groundwater table ranging from 2 to 5 m. The right half of each figure presents the foundation with groundwater table of 2 m, while the left half represents the foundation for different groundwater table (cases 11 and 13-15 in Table 3).  
It can be seen from Figures 19 and 20 that, with decreases of the groundwater table, the effective reinforcement range of the embedded weak layer and soil beneath it expanded significantly, and the maximum relative reinforcement degree  
Figure 21 shows comparisons of excess pore pressure ratios in the embedded weak layer in grounds with different levels of groundwater after DC. It is found that when the level of groundwater was above the weak layer, the excess pore pressure in the embedded weak layer increased significantly, and this influence decreased as the groundwater table  
Figure 19: Distributions of \(\mathrm{I_{r}}\) in grounds with different levels of groundwater. (**a**) precipitation depth of 3 m; (**b**) precipitation depth of 4 m; (**c**) precipitation depth of 5 m.  
Figure 20: Cloud lines of \(\mathrm{I_{r}}=7.7\%\) in grounds with different levels of groundwater.  
decreased from h = 2.0 m to h = 3.0 m. While the groundwater table was below the bottom of the weak layer (e.g., h = 4.0 m and h = 5.0 m), the excess pore pressure ratio in the weak layer was 0. The results indicate that lowering of the groundwater table can significantly improve the reinforcement effect of DC to the embedded weak layer and the soil beneath it. It should be mentioned that DC is better carried out until the groundwater is lowered below the designed depth, especially in grounds with shallow weak layers.
# How _Pseudomonas nitroreducens_ Passivates Cadmium to Inhib Plant Uptake
## 3 Results and Discussions
### Biosorption Isotherms  
In order to model biosorption and calculate the biosorption capacity, the Langmuir and Freundlich isotherm models were tested [31; 32]. The Langmuir isotherm [33] supposes monolayer sorption is expressed as follows:  
\[Q_{e}=\frac{Q_{max}bC_{e}}{1+bC_{e}} \tag{1}\]  
\(Q_{e}\) is the equilibrium sorption amount of Cd (mg/g), \(Q_{max}\) represents the maximum biosorption capacity of 11830 at given temperature (mg/g), \(C_{e}\) is equilibrium concentration of Cd in the solution (mg/L) and \(b\) is the biosorption constant.  
The Freundlich isotherm [34] is an empirical equation based on the adsorption on heterogeneous surface and is described as follows:  
\[Q_{e}=KC_{e}\frac{1}{8} \tag{2}\]  
\(K\) and \(n\) are the biosorption constant.  
Biosorption isotherms and biosorption constants evaluated at different temperatures with different initial Cd\({}^{2+}\) concentrations are shown in Figure 2 and Table 1. It can be seen that the Langmuir model can describe the biosorption behavior well (R\({}^{2}\) > 0.95), which indicated that the adsorption was mainly monolayer biosorption. The maximum biosorption capacity was 160.51 mg/g at 30 \({}^{\circ}\)C, and \(b\) = 0.004 was higher than that of 20 \({}^{\circ}\)C (0.001) and 40 \({}^{\circ}\)C (0.001). Higher temperature (40 \({}^{\circ}\)C) and low temperature (20 \({}^{\circ}\)C) are not conducive to the biosorption process. Near the optimum temperature (30 \({}^{\circ}\)C), the lower the temperature is, the more unfavorable to the Cd biosorption. Therefore, the high biosorption capacity of 11830 makes it a promising adsorbent for the removal of Cd, indicating its potential for practical applications in the future.
## 2 Geodetic Data Sets and Processing
### Sub-Pixel Correlation of SPOT Images
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Acquisition** & **Satellite/** & **Cloud** & **Incidence** & **Orient.** & **Sun** & **Sun** \\
**Date** & **Orient.** & **Cover** & **Angle** & **Angle** & **Elevation** & **Azimuth** \\ \hline
29 January 1999 & SPOT2/Right & 10\% & 2.9\({}^{\circ}\) & 9.4\({}^{\circ}\) & 42.9\({}^{\circ}\) & 151.0\({}^{\circ}\) \\
23 November 1999 & SPOT1/Right & 0\% & 2.5\({}^{\circ}\) & 9.4\({}^{\circ}\) & 43.4\({}^{\circ}\) & 159.7\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: SPOT data used in this study, resolution 10 m.  
Figure 5: **(A,B)** East-West and North-South horizontal offsets obtained from the sub-pixel correlation of the two SPOT images (see Table 2) acquired before and after the earthquake (scale in m). Grey areas are where the correlation was too low. (Mean and standard deviation for EW band: \(-\)1.5 m, 1.7 m and for NS band: 1.2 m, 1.9 m).