esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
With the rapid development of the underwater Internet of Things (IoT), the number of underwater communication nodes is rapidly increasing. The access capacity of a traditional multi-antenna communication system is limited by the number of transmitting antennas, and multi-beam communication systems using non-orthogonal multiple access (NOMA) technology can enhance the access capacity of the system. However, this can lead to serious inter-beam and intra-beam interference. To address the severe issues of inter-beam and intra-beam interference in underwater multi-beam NOMA systems, we propose an adaptive transmit diversity strategy. We design an algorithm for adaptive selection and merging beams based on the degree of interference between beams in space, which merges LED beams with high interference. Diversity technology is used to reduce interference between beams, and spatial multiplexing is still performed between LED groups with low interference. Within the same beam, we use an OFDM-NOMA scheme to match and group the users. Signals from different user groups are sent through different subcarriers to improve resource utilization. This enhances access capacity while reducing NOMA inter-user interference. Simulation results show that the bit error rate (BER) of users with the adaptive transmit diversity strategy satisfies the forward error correction (FEC) limits in the presence of high inter-beam interference and has a better reachable rate and BER performance compared to the multi-beam access system without interference management. We also analyze the system BER performance of the proposed strategy in the multi-user case, and the BER of all 32 access nodes are lower than the FEC threshold at a communication distance of 5 m. This demonstrates that the strategy can effectively reduce the interference of the multi-beam NOMA system.  
multi-beam; inter-beam interference; intra-beam interference; adaptive transmit diversity; OFDM-NOMA +
Footnote †: journal: _Photonics_
## 5 Experiments
### Preparing Multiple Positives and Multiple Negatives  
Contrastive learning usually exploits data-augmentation techniques to construct positive pairs, while negative examples are sampled from other classes' positives in mini batches [65]. In computer vision, data augmentation has been widely done based on image rotation, image sharpening, image corruption, and object deletion [66]. However, similar strategies may not work well in NLP, as changing the order of words in a sentence or deleting words from a sentence may substantially affect its semantic [33; 67].  
According to previous unsupervised works for fine-tuning BERT using contrastive learning, text augmentation techniques including word deletion and word synonym replacement have yielded relatively better results [16; 33]. Therefore, we studied them for generating positive pairs. Additionally, for the first time, we studied paraphrasing for generating positive pairs using the T5 text-to-text model [68] on Semantic Textual Similarity. However, similar to the results of SimCSE [37], word deletion, word synonym replacement and paraphrasing did not perform well in all seven STS tasks and so did not produce a good average on STS tasks. We have provided more details of these three text augmentation techniques in Appendix A.1. Therefore, our main strategies for generating positive and negative samples are as follows:  
**Preparing multiple positives:** No data augmentation is used. We simply copy each anchor several times as its positive samples.  
**Preparing multiple negatives:** We sample negatives for each anchor from the entailments or contradictions of other anchors in the training data (SNLI and MNLI).
## 4 Conclusions  
This study examined the CO\({}_{2}\) absorption mechanisms and regeneration properties of alumina-based sorbents according to K\({}_{2}\)CO\({}_{3}\) loading in the presence of 10 vol% CO\({}_{2}\), 10 vol% H\({}_{2}\)O, N\({}_{2}\) balance at 200 \({}^{\circ}\)C and 20 atm. For the alumina-based sorbent loaded with K\({}_{2}\)CO\({}_{3}\), such as PAI10-40, the CO\({}_{2}\) capture capacities were found to be 32.3, 63.0, 95.4, 124.5 mg CO\({}_{2}\)/g sorbent, respectively, which was about 98% to 101% of the K\({}_{2}\)CO\({}_{3}\) theoretical value. However, the CO\({}_{2}\) capture capacity of PZI30 sorbent was 19.7 mg CO\({}_{2}\)/g sorbent, corresponding to 26% of the K\({}_{2}\)CO\({}_{3}\) theoretical value. The higher CO\({}_{2}\) capture capacity of PAI-\(x\) sorbent relative to the PZI30 sorbent was confirmed by an XRD, FTIR analysis, and TPD experiment. This was attributed to KHCO\({}_{3}\) and KAl(CO\({}_{3}\))(OH)\({}_{2}\) structures. As for the regeneration properties measured in the TPD experiment, the PAI10 and 20 sorbents showed one peak due to the decomposition of KAl(CO\({}_{3}\))(OH)\({}_{2}\) at temperatures of 250 \({}^{\circ}\)C and 350 \({}^{\circ}\)C. The PAI30 and 40 sorbents showed two peaks due to the decomposition of KHCO\({}_{3}\) and KAl(CO\({}_{3}\))(OH)\({}_{2}\) at temperatures of 150 \({}^{\circ}\)C and 400 \({}^{\circ}\)C. In the case of PAI-\(x\) sorbent, the KHCO\({}_{3}\) structure generated during CO\({}_{2}\) sorption was confirmed to have the same decomposition temperature as the KHCO\({}_{3}\) structure generated after CO\({}_{2}\) sorption of PZI30 sorbent. This study demonstrated that the regeneration of an alumina-based sorbent is possible at temperatures lower than 400 \({}^{\circ}\)C. This low regeneration energy is advantageous because the CO\({}_{2}\) capture cost can be reduced under high pressure and moderate temperature conditions.  
Conceptualization, D.-Y.R., S.-C.L.; formal analysis, D.-Y.R., S.J., T.-Y.K.; investigation, J.-H.W., S.-Y.L., J.-H.L., J.-C.K., J.-E.H.; supervision, S.-C.L., and J.-C.K.; writing--original draft, D.-Y.R., S.J.; writing--review and editing, S.J. and S.-C.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Technology Innovation Program (No. 20015460) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea). This research was also supported by National R&D Program through the National Research Foundation of Korea (NRF) funded by Ministry of Science and ICT (No.2021M3I3A1084502).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results
### Tocotriend's Effects on Cerebral Ischaemia Rperfusion Injury  
Table 2 shows how all studies reported that various tootrienol forms, including \(\alpha\)-tocotrienol [18; 19], combined tocotrienols [20], and combined tocotrienols and \(\alpha\)-tocopherol [17; 21], significantly reduced the infarct size in those animals subjected to the MCA occlusion. Reduction in cytotoxic edema was another structural improvement mediated by locotrienol treatment [20]. Other than that, the improvement of collateral circulation was also evident in the same study that utilized a canine model [20]. Furthermore, various parameters were also reported to be significantly changed, including the attenuation of oxidative stress markers (oxidized glutathione (GSSG) to glutathione (GSH) ratio [21], 4-hydroxy-2-nonenal (4-HNE) [19; 21], nitrotyrosine [21], 8-hydroxy-2\({}^{\prime}\)-deoxyguanosine (8-OHdG) [21], the receptor for advanced glycation end products (RAGE) [21], N (omega)-(carboxymethyl) arginine (CMA) [21], N (omega)-(carboxymethyl) lysine (CML) [21]), the regulation of antioxidant-related markers (increased NF-E2 p45-related factor 2 (Nrf2) [21]), reductions in inflammation parameters (tumor necrosis factor \(\alpha\) (TNF-\(\alpha\)) [17], monocyte chemotactic protein 1 (MCP-1), Iba-1, IgG [17], light chain 3 (LC3) II [21]) and a reduction of apoptotic marker (cleaved caspase-3 [21]). In addition, the extracellular matrix was significantly affected, as evidenced by the increase in collagen IV [17] and tissue inhibitor of metalloproteinases 1 (TIMP-1), and the reduction in matrix metalloproteinase (MMP)-9 (not MMP-2) levels [20]. Two transporters that were regulated by locotrienol treatment were multidrug-resistant protein 1 (MRP1) [19; 21] and chloride intracellular channel protein 1 (CLIC1) [20]. Neurobehavioral assessment of the mice demonstrated a significant improvement in Rotarod time but a negligible change in the Bederson score and Corner test [17; 21].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Authors** & **Infact Size** & **LDH** & **MRP1** & \begin{tabular}{c} **Rotarod** \\ **Time** \\ \end{tabular} & \
\begin{tabular}{c} **Bederson** \\ **Score** \\ \end{tabular} &
\begin{tabular}{c} **Corner** \\ **Test** \\ \end{tabular} \\ \hline Jiao [17] & \(\downarrow\) TV & & \(\uparrow\) & NS & NS \\ Shang [21] & \(\downarrow\) TV & \(\uparrow\) & \(\uparrow\) & NS & NS \\ Rink [20] & \(\downarrow\) TE & & & & \\ Park [19] & \(\downarrow\)\(\alpha\)-TC & \(\downarrow\) & \(\uparrow\) & & \\ Mishima [18] & \(\downarrow\)\(\alpha\)-TC, \(\alpha\)-TH, \(\gamma\)-TC & \(\downarrow\) & & & \\ \hline \hline \end{tabular}  
* \(\uparrow\): increased; \(\downarrow\): reduced; \(\alpha\): alpha; \(\gamma\): gamma; \(\delta\): delta; LDH: lactate dehydrogenase; MRP1: multidrug-resistant protein 1; NS: not significant; TC: tocotrienol; TE: tocotrienol enriched; TH: tocoppherd; TV: Tocovid.  
\end{table}
Table 2: Outcomes of the tocotrienol treatment in cerebral ischemia-reperfusion injury.  
Figure 4: Risk of bias assessment for myocardial ischemia-reperfusion injury.
## 2 Coupling Calculation of Temperature and Seepage Field
### Coupling Analysis of Temperature Field and Seepage Field
Figure 1 shows the influence of the seepage field on the temperature field under different permeability coefficients. Figures 2 and 3 mainly reflect the influence of the temperature field on the seepage field under different permeability coefficients. It can be seen from these figures that by comparing the solutions without considering the coupling effect \(T_{0}(x)\), \(H_{0}(x)\) and the solutions considering the coupling effect \(T_{1}(x)\), \(H_{1}(x)\), the latter is more satisfied with the actual engineering situation. With the increase in the permeability coefficient, the influence of the seepage field on the temperature field is greater than that of the temperature field on the seepage field. From the point of view of engineering applications, only the influence of the seepage field on the temperature field is considered, and the effect of the temperature field on the seepage field is neglected, which can meet the requirements of engineering accuracy and simplify engineering problems. Figure 1: Temperature \(T_{0}(x)\) and temperature \(T_{1}(x)\) under different permeability \(K\).
## 4 Discussion
DCM is characterized by clinico-radiological discordance, where DCM patients with substantial neurological dysfunction may have minimal cord compression [39, 40, 41]. This can contribute to delays in diagnosis and difficulties with the prognostic of the post-surgical recovery of functions. In these patients, the quantification of spinal cord biomechanics may potentially support an earlier diagnosis and improve the accuracy of predicting post-surgical function. Spinal cord biomechanics can also assist with the diagnosis of certain forms of cervical myelopathy, such as [PERSON]'s disease [42], where dynamic spinal cord compression is the primary contributor to neurological dysfunction. The quantification of patient-specific spinal cord biomechanics can also be used for surgical planning. In those patients with high intramedullary stress and strain during neck flexion, motion-preserving surgeries such as cervical laminectomy or laminoplasty are unlikely to provide durable benefits compared to fusion surgeries that will reduce adverse spinal cord tension during neck motion. Our previous work has demonstrated that patient-specific FEMs can be successfully developed for the post-surgical spine, enabling longitudinal assessments of spinal cord biomechanics [15, 43, 44]. Assessing the link between post-surgical spinal cord biomechanics and function is a goal of future studies. The limitations of this study include its small sample size, which restricts generalizability and reduces statistical power; a larger sample size would address these issues. Our FEMs also did not differentiate between gray and white matter, as separate gray and white matter properties for the human spinal cord are currently not available. In future studies, we will include patient-specific axial rotation, as well as lateral bending, for a more comprehensive understanding of spinal cord mechanics. Our study is also limited by the lack of patient-specific spinal cord tissue properties in FEMs. Deriving patient-specific tissue properties of the spinal cord is not feasible given the risk of profound neurological damage, and cadaver spinal cord specimens may not reflect true in vivo tissue properties. One technique is the use of advanced microstructural MRI, such as diffusion tensor imaging (DTI). DTI can provide an estimate of spinal cord microstructure, and in our ongoing studies, we are evaluating the link between spinal cord DTI and tissue biomechanics. These studies may create a pathway for non-invasively estimating patient-specific tissue properties to improve the accuracy of patient-specific FE modeling. Despite these limitations, this is the largest study to perform patient-specific cervical spinal cord FEM, and the first to correlate FEM outputs to the patient-reported measures of neurological function.
## 3 Results
### Test-Retest Reliability  
Results from the test-retest reliability are shown in Table 3. MVC, RFD\({}_{0-250}\) and Impulse\({}_{0-250}\) maintained a high reliability among sessions for both raw and fitted data (CV \(\leq\) 9.2% ICC \(\geq\) 0.904). However, RFD\({}_{0-150}\) and Impulse\({}_{0-150}\) were highly reliable in fitted data (CV \(\leq\) 10.8% ICC \(\geq\) 0.879). Likewise, RFD\({}_{0-50}\) and Impulse\({}_{0-50}\) were only deemed reliable in fitted data and particularly in the dominant leg (CV \(\leq\) 13.3% ICC \(\geq\) 0.898). RFD\({}_{\max}\) was best in the dominant leg, with similar results in fitted and raw data (CV-17% ICC \(\geq\) 0.799). Dominant limb records were not importantly altered by the familiarization (ES \(<\) 0.13) except for the RFD\({}_{0-250}\). In opposite, non-dominant limbs showed large variations in the majority of the variables but the RFD\({}_{0-250}\).
# A Novel Method for Wheat Spike Phenotyping Based on Instance Segmentation and Classification
## 3 Results and Discussion
### Segmentation Results of Wheat Spike
The YOLOv8-seg model could extract complementary image features from the three varieties of wheat spikes, which was conducive to spikelet segmentation. Therefore, the overall instance segmentation performance was optimal for the YOLOv8-seg model trained by three varieties of wheat spikes, as shown in Figure 9. The average segmentation time of the model was 40 ms, which indicated that the YOLOv8-seg model provided technical support for fast and accurate wheat spike segmentation. For the Z618, Y19, and S8 datasets, the wheat spike images were classed into four categories, including the background, wheat spikes at the early filling period, wheat spikes at the late filling period, and wheat spikes at the mature period. For the TV datasets, the wheat spike images were classed into the background and nine categories of spikes, including \(\text{Z618}_{\text{sfs}}\), \(\text{Z618}_{\text{lfs}}\), \(\text{Z618}_{\text{ms}}\), \(\text{Y19}_{\text{efs}}\), \(\text{Y19}_{\text{lfs}}\), \(\text{Y19}_{\text{ms}}\), \(\text{S8}_{\text{efs}}\), \(\text{S8}_{\text{ffs}}\), and \(\text{S8}_{\text{ms}}\). The category of the wheat spike was determined by the mode of the predicted categories of all the corresponding spikelets using the YOLOv8-seg model. As shown in Table 1, the Z618, Y19, S8, and TV datasets achieved very high precision for predicting categories of wheat spikes, and all the A values reached 100%. Such excellent classification performance was caused by  
Figure 8: Example of instance segmentation result (S8 wheat spike). **(a)** Original image; **(b)** segmentation result; **(c)** ground truth. The part pointed by the arrow is the unsegmented part of the spikelet. Figure 9: Instance segmentation results in wheat spikes at different growth periods. the following reasons. The main reason was that this study used the mode of the predicted categories of all the spikelets to represent the category of the corresponding wheat spike, which could effectively eliminate a small part of the wrong predicted categories of the spikelets and greatly improve the precision. As for the Z618, Y19, and S8 datasets, there were clear differences in appearance for the wheat spikes, including color, size, and texture. The wheat spikes had characteristic bright green and golden yellow bodies at the early filling and mature periods, respectively. The size of the wheat spikes reached the maximum at the late filling period. The spike textures also changed significantly along with the advance of the wheat growth period, such as regularity, contrast, coarseness, directionality, etc. As for the TV dataset, the YOLOv8-seg correctly predicted the categories of most spikelets in one single wheat spike due to some differences in appearance for the three varieties of wheat. Therefore, the increase in the number of categories did not reduce the precision of the TV dataset.
## 5 Results and Discussion
### Engine Performance  
Figure 9a shows the effect of biodiesel blends on the specific fuel consumption and thermal efficiency of the engine with respect to the engine's mode of operation. In general, the increase in the percentage of biodiesel causes an increase in specific fuel consumption. It was observed that on average, the PB10 SB5, PB5 SB5, PB10, and PB5 show an increase in specific fuel consumption of 13.28%, 11.48%, 7.64%, and 3.8% compared to diesel, respectively. The cause of this behavior is mainly due to the lower calorific value of biodiesel blends. Therefore, a greater amount of fuel is needed to reach the same amount of power. Additionally, it is observed that biodiesel blends have a higher density and viscosity than diesel, so the amount of fuel injected into the combustion chamber is greater [43,44].  
In addition, it was observed that an increase in engine load causes a reduction in the BSFC (Brake-specific fuel consumption) value because the combustion process is more efficient with higher loads. Similar results were reported in the investigations of [PERSON] et al. [45], and [PERSON] [46].  
The thermal efficiency of the brake (BTE) is a parameter that measures the combustion capacity of the fuel to transform its chemical energy into mechanical energy. The BTE against the different engine loads is shown in Figure 9b. The addition of biodiesel is reflected in the efficiency of the thermal brake. It was observed that biodiesel blends show a reduction in the thermal efficiency of the brake. For the different modes analyzed, it is shown that the BTE varies from 26.4-31.8%, 26.1-31.2%, 25.56-30.6%,  
Figure 8: In-cylinder temperature in mode D.  
Figure 9: Diagram of **(a)** specific fuel consumption, and **(b)** brake thermal efficiency.  
25.26-30% and 24.96-29.4% for diesel, PB5, PB10, PB5 SB5, and PB10 SB5, respectively. This efficiency reduction can be explained by the higher fuel consumption and the lower calorific value of biodiesel blends, as shown in Table 4.
# Medical Devices for Tremor Suppression: Current Status and Future Directions
## 6 Other Devices  
### Gyroscopic Stabilization  
The GyroGlove (GyroGear, London, UK) is developed with a plurality of gyroscopes, mounted to a fabric glove on the dorsal surface of the hand, that function to counteract tremors [132]. Each gyroscope includes a rotatable disc that is capable of rotating about an axis to resist involuntary motions. This allows its angular momentum to be conserved when a rotational displacement is encountered, with opposing force inputs from any direction. Unlike previous gyroscopic devices using a single gyroscope [133; 134], the use of multiple gyroscopes in the GyroGlove allows it to suppress involuntary movements in multiple planar directions. This device is currently in the advanced stage of development, and data is needed to inform its efficacy.
## 4 Technologies to Support Contextualization
### Sampling and Integrating Process Data  
When discussing data sampling and integration, two types of data have to be considered: static and dynamic data. Static data do not change during the production process (e.g., type of machine), whereas dynamic data change more or less rapidly (e.g., temperatures, machine speed, presence of chocolate bars on a conveyor belt). To connect static and dynamic data, semantic descriptions are essential. It should be noted that these descriptions do not suddenly enable an integration of data that has been impossible to date. In fact, data have been integrated with older technologies for decades. However, this had to be done manually for each machine or plant, and thus required immense programming effort. Modern technologies can automate this process, making it feasible to flexibly provide the information needed in a given context. Thus, it becomes realistic to use the potentials of data integration for operator support.  
Figure 2: Schematic representation of connected ontologies for faults, process, and machines. Oval shapes represent resources (i.e., concepts), rectangles represent literals (i.e., text strings).
## 2 Materials and Methods
### Application Layer
#### 2.4.3 Facility Management  
The inspection and maintenance work are required either due to the failure of the utility system or according to the routine schedule such as weekly or monthly. However, during the inspection or maintenance, information about the utility is not readily available, making the system ineffective and causing time delays and cost overruns. On the basis of the proposed framework, the BIM-GIS integrated model provides ready-to-use information about the underground utilities in 3D that can be used by the inspection and maintenance team to depict the location, type, and material of each pipe segment. Furthermore, each pipe is attributed to the semantic information needed for the inspection.