esa-sceva/satcom-chunk-collection huggingface dataset:
## Abstract  
Motion Freeze (MF), which integrates 100% of the signal of each respiratory phase in four-dimensional positron emission tomography (4D-PET) images and creates the MF-PET, is capable of eliminate the influences induced by respiratory motion and dispersing from three-dimensional PET (3D-PET) and 4D-PET images. In this study, the effectiveness of respiratory gated radiotherapy applying MF-PET (MF-Plan) in lung cancer patient was investigated and compared with three-dimensional intensity modulated radiotherapy (3D-Plan) and routine respiratory gated radiotherapy (4D-Plan) on the impact of target volume and dosimetry. Thirteen lung cancer patients were enrolled. The internal target volumes were generated with 40% of maximum standardized uptake value. The 3D-Plan, 4D-Plan, and MF-Plan were created for each patient to study the radiation to the targets and organs at risk. MF-Plans were associated with significant reductions in lung, heart, and spinal cord doses. The median reductions in lung V20, lung mean, heart mean doses, and spinal cord maximum dose compared with 3D-Plans were improved. When compared with 4D-Plans, the median reductions in lung V20, lung mean dose, heart mean dose, and spinal cord maximum dose were improved. Our results indicate that the MF-Plan may improve critical organ sparing in the lung, heart, and spinal cord, while maintaining high target coverage.  
lung cancer; 4D PET/CT; respiratory gated radiotherapy; motion freeze +
Footnote †: journal: Applied Sciences
# Precedence Table Construction Algorithm for CFGs Regardless of Being OPGs
## 7 Conclusions  
Previous work has addressed the application of OPGs to solve problems related to the parsing of various forms of languages; however, not much emphasis has been placed on the definition of the Left and Right sets nor on the generation of an algorithm to find the operator precedence table.  
In this work, we have redefined the sets Right and Left, previously exposed by other authors, and introduced a novel set called Leftmost. These sets form the basis for an algorithm that constructs the operator precedence table. Each set is constructed from an algorithm that facilitates its obtaining, adding a new dimension to the existing research.  
The proposed algorithm for constructing the precedence table, while breaking (with some exceptions) the basic definition of an OPG, opens up new possibilities. It allows for the construction of an operator precedence table from a non-OPG CFG, paving the way for a bottom-up parsing algorithm to recognize a string generated by the CFG.  
Three examples of CFGs are shown: one OPG and two non-OPGs. The proposed algorithms are systematically applied to these examples, step by step, until the operator precedence tables are obtained, regardless of the CFG's nature. Additionally, the bottom-up parsing algorithm is applied to specific strings for each grammar, providing verification that they generated them.  
It is important to note that while the algorithm and the definition of the new sets of terminal symbols are significant advancements, they do have limitations. It is not always possible to solve all cases where two or more adjacent terminal symbols are on the right-hand side. There are exceptions, and we must be aware of them.  
Conceptualization, L.L. and J.M.; methodology, J.M.; software, L.L.; validation, L.L. and J.M.; formal analysis, L.L., E.A., and J.M.; investigation, L.L. and J.M.; resources, J.M.; data curation, E.A.; writing--original draft preparation, E.A. and J.M.; writing--review and editing, E.A. and J.M.; visualization, E.A.; supervision, J.M.; project administration, J.M.; funding acquisition, J.M.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding and the APC was funded by Universidad del Norte.  
Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
# Bayesian Compressive Sensing Based Optimized Node Selection Scheme in Underwater Sensor Networks
## 1 Introduction  
Compared to the traditional sensor networks [1; 2], the underwater sensor network is more restricted to the resource. The study of underwater sensor networks has become a hot topic and has attracted more attention in recent years [3; 4; 5; 6]. For example, routing protocols and secure communication for underwater sensor networks are investigated in [7; 8], respectively. However, none of them focus on the full use of node energy resources. However, the energy of each sensor node is fixed and limited in underwater sensor networks. A traditional information acquisition scheme needs to collect the information of all the sensor nodes so that it costs too much energy. Hence, some works are provided for decreasing the price of sensor networks [9; 10]. In addition, it is difficult for the sink node to distinguish the signals from different sensor nodes. An efficient and affordable scheme needs to be proposed for superior performance. A compressed sensing method can be used to solve it as long as the original signal is sparse [11]. In the underwater sensor network, many kinds of signals received by sink nodes can be represented sparsely on some basis such as temperature and salinity. Therefore, we can collect a part of the total sensor nodes and then reconstruct the original signal. The compressed sensing scheme can not only decrease the cost but also improve the efficiency. The state of network and the correlation of nodes are not considered due to the randomness of nodeselection in a conventional compressed sensing scheme. Hence, the performance of the compressed sensing scheme can be further improved by considering them.
# Accumulative Roll Bonding--A Review
## 3 Parameters
### Rolling Parameters
#### 3.3.4 Rolling Friction  
Most of ARB researches have been performed without lubricants. Shown in Figure 6a [PERSON] and [PERSON] [32] performed ARB at different friction coefficients with lubricant, and in dry and rough surface conditions where the average peel strength increased when the friction coefficient increased  
Figure 5: (a) Variation in diffusion layer width with rolling temperature for ARBed Al/Ti sheets [45], (b) Influence of both rolling temperature and roll speed on bond toughness of Al strips [42], (c) Variation of ambient tensile properties of 8011 alloy with increasing the equivalent strain in RT-ARB and HT-ARB samples [46], (d) Ultimate bending strength of the ARBed Al/AZ31 at different temperatures [47].  
between the strips and rolls probably due to the increased mean contact pressure between the outer layers and the rolls.  
The effect of lubricant on mechanical properties of ARBed Cu was studied by [PERSON] et al. [50] where they concluded that, if the surface friction is reduced by lubricant, the deformation is treated as a plane strain compression, of which equivalent strain is about 0.82; nonetheless, higher frictions usually exert larger strains adjacent to the surface and hence enhance microstructural evolution close to the surface being described as an additional shear strain. The additional shear strain is mostly created on the surface, so the corresponding effect is more pronounced at this region. Of course, it should be noted that additional shear strain still exists inside the ARBed materials as the central points were at surface points before the last roll bonding steps; finally, grain refinement was accelerated in case no lubricant was used due to the additional shear strain [50].  
As shown in Figure 6b, at earlier ARB cycles, hardness value increases to ~140 Hv at surface layers when no lubrication is consumed; the hardness of the other samples reaches to ~120 Hv. The hardness at the surface of ARBed sample without lubrication seems to be constant, whereas for the other sections (center and quarter) increases gradually with increasing cycle number and then saturate. The reason why the hardness at surface layers is almost always higher than those of inner regions is due to the additional grain refinement caused by the friction between the sheets and rolls.
# Design of a Land Area Measuring Instrument Based on an STM32 and a BeiDou Positioning Chip
## 4 Embedded Programming
### System-Level Integration of Dynamic Memory Management  
Conventional approaches store collected data using static memory allocation. However, unpredictable land area dimensions and geometries preclude prior knowledge of required sampling points. Excessive pre-allocation wastes storage resources and constrains auxiliary functions, whereas insufficient allocation risks memory overflow and system failure. Dynamic memory management thus enhances flexibility and stability [26; 27], aligning resource utilization with operational demands.
## 3 Results
### Analysis Using Zeta Potential and Stretchable Gas Barrier Film Fabrication  
Using a zeta potential analyzer (ELSZ-1000, Otsuka Electronics), the surface charges of the solutions used for the LbL deposition were determined. As shown in Figure 2, the GO solution exhibited a zeta potential value of \(-33.6\) before mixing with PDDA, but after mixing with PDDA at a ratio of 1:2, the PDDA (GO) solution showed a positive charge of 34.95. The MMT solution exhibited a zeta potential value of \(-0.54\) before mixing with PVA, which changed to a negative charge of \(-8.4\) after mixing at a ratio of 3:1 with PVA. When MMT was mixed with PVA, the PVA permeated between MMT and bonded with the MMT to increase the diffusion path of water. As confirmed by the zeta potential analysis, the PDDA (GO) solution exhibited a positive charge, and the PVA (MMT) solution was negatively charged, meaning each layer can be stacked alternately by electrostatic interactions. Herein, 30 alternating layers of PDDA (GO) and PVA (MMT) were stacked on the stretchable substrate. The thickness of the fabricated layer by the LbL process is about 150 nm. The LbL process, the solution process used in this paper, has the advantage of being able to be utilized in a large-area lamination because it stacks each layer by electrostatic forces. In addition, after drop casting, it is then rinsed with DI water and then laminated to a thickness on the nano-scale. The drop casting method is more suitable than spin coating for the fabrication of a uniform multilayer film while maintaining pre-strain on the substrate.  
Figure 1: Light transmittance of the prepared stretchable substrates.
# Evolutionary Game Analysis of Power Generation Groups Considering Energy Price Fluctuation
### Main Work and Innovations
It can be solved to its local equilibrium point in space \(N(\{p,q\};0\leq p,q\leq 1)\). Then, there are five equilibrium points in the system composed of evolutionary game models, which is \(E_{1}(1,1)\), \(E_{2}(1,0)\), \(E_{3}(0,1)\), \(E_{4}(1,1)\), \(E_{5}(p_{0},q_{0})\). Coordinates \(p_{0}\) and \(q_{0}\) are calculated according to Equations (9) and (10):  
\[p_{0} =(v_{4}-v_{3})/(v_{1}-v_{2}-v_{3}+v_{4}) \tag{9}\] \[q_{0} =(u_{4}-u_{2})/(u_{1}-u_{3}-u_{2}+u_{4}) \tag{10}\]  
For a population dynamic described by a system of differential equations, the stability of its equilibrium point is obtained by the local stability analysis of the Jacobi matrix. The Jacobi matrix of the above evolutionary game system is described in Equation (11):  
\[J= \begin{pmatrix}\frac{\partial\dot{p}}{\partial p}&\frac{\partial\dot{p}}{ \partial q}\\ \frac{\partial q}{\partial p}&\frac{\partial q}{\partial q}\end{pmatrix}  \tag{11}\]  
where in, the calculation formula of each element in the Jacobian matrix is shown in Equation (12). \[\left\{ \begin{array}{l}\frac{\partial\dot{p}}{\partial p}=(1-2p)[(u_{1}-u_{ 2}-u_{3}+u_{4})q+u_{2}-u_{4}]\\ \frac{\partial\dot{p}}{\partial q}=p(1-p)(u_{1}-u_{2}-u_{3}+u_{4})\\ \frac{\partial\dot{q}}{\partial p}=q(1-q)(v_{1}-v_{2}-v_{3}+v_{4})\\ \frac{\partial q}{\partial q}=(1-2q)[(v_{1}-v_{2}-v_{3}+v_{4})p+v_{3}-v_{4}] \end{array} \right. \tag{12}\]  
For an evolutionary game with two agents and two strategies, judging whether an equilibrium point is stable requires calculating the determinant and trace of its Jacobi matrix. If the determinant of an equilibrium point is greater than zero and the trace is less than zero, the equilibrium point is asymptotically stable and is a stable equilibrium point (SEP); If both the determinant and the trace are greater than zero, the equilibrium point is unstable and is an unstable equilibrium point (UEP); If the determinant is less than zero, the local equilibrium point is a saddle point (SP), that is, the system is in a critical evolutionary state at this point, and is still in an unstable equilibrium state. The next section will analyze the stability of each strategic equilibrium point of coal-fired and gas-fired power generation groups based on the fluctuation of primary energy prices and the influence of different market supply and demand.
## 3 UAV Platform for Multi-Sensor Photogrammetry Aerial Mapping
### Remote Sensing Methods for Automatic Detection and Mapping of Weak Structural Parts and Components
#### 3.2.2 Automatic Methods for Building Damage Detection and Mapping  
The photorealistic 3D models and products (e.g., from orthophoto) can be used for the detection and mapping of building damage. The developed methods are based on a combination of unsupervised and supervised image classification methods. Furthermore, the methods can be technologically developed on object-based machine learning algorithms [87; 88; 89; 90]. Such an approach allows rapid damage detection on buildings, which is important because of the large amount of high-resolution spatial data. Preliminary research related to the topic of developing automatic methods for the classification has been made [91]. Newly developed methods for the detection and mapping of building damage can use all bands of the multi-sensor system, but also spectral indices. Spectral indices allow further highlighting of certain phenomena on earth or objects [91; 92; 93]. The use of spectral indices has been shown to increase the accuracy of classifications. In some cases, spectral indices allow features to be highlighted on an object that cannot be detected on any single band in the visible or out of the visible part of the spectrum. In addition to the spectral indices, the newly developed methods also use the Haralick texture features (Gray Level Co-occurrence Matrix) to detect and emphasize the edge between damaged and undamaged parts on objects [94; 95; 96; 97; 98]. Using an object-based approach enables rapid and accurate mapping of damage. After the mapping, the system immediately assigns statistical indicators such as area, depth, etc. (Figure 3). The development of a new automatic method for structural damage  
Figure 2: Photogrammetric 3D building modeling methods. From (**a**) photogrammetric creation of SfM (Structure-from-motion) point clouds to the (**b**) high-resolution photorealistic 3D building model.  
detection and mapping is a major scientific contribution in the field of photogrammetry and remote sensing applications in civil engineering.
## 3 Case Study
### Casting Mold Liquid Surface Segmentation Experiment and Result Analysis
#### 3.2.3 Ablation Experiment  
In order to evaluate the efficacy of the proposed enhanced DeepLabV3+ technique for segmenting the liquid surface in casting molds, this experiment conducted ablation investigations that specifically examined feature extraction networks, attention mechanisms, and transfer learning. The segmentation performance was tested using the original backbone networks Xception, ResNet50, and lightweight network MobilenetV2 to demonstrate the balance between model accuracy and inference time. Figure 11 displays the detection results using different feature networks as backbone networks. Based on the figure, it is clear that utilizing MobilenetV2 as the underlying network results in the highest inference speed (70.77 FPS) but the lowest _MIoU_ (87.88%). On the other hand, when ResNet50 is used as the main network, DeepLabV3+ achieves the best level of _MIoU_ (90.4%), with an inference speed (54.84 FPS) second only to MobileNetV2. ResNet50 is a suitable choice for the backbone feature network because of its ability to achieve high segmentation accuracy and meet real-time needs, resulting in overall improved model performance.  
Additionally, Table 3 reveals that the introduction of the CBAM attention module resulted in a 0.6 percentage point increase in _MIoU_ and a 0.85 percentage point increase in _Liquid-IoU_. Using the CBAM module improves the model's capacity to acquire complex details of the liquid surface.  
Figure 11: Detection comparison chart using different feature networks as backbone networks.  
Figure 10: Transfer learning method strategy.  
Furthermore, the effectiveness of transfer learning in enhancing the performance of the improved DeepLabV3+ algorithm was validated. As shown in Table 3, the results of the ablation experiments show that training with transfer learning led to a 2.27 percentage point increase in _Liquid-IoU_ and a 1.42 percentage point increase in _MIoU_. This demonstrates that transfer learning effectively improves the model's prediction accuracy.
# Multi-Object Tracking with Predictive Information Fusion and Adaptive Measurement Noise
## 3 Results
### Comparison Results
#### 3.2.2 Comparison on MOT20  
As shown in Table 2, we compared our proposed method with FairMOT [7], TransMOT [39], ByteTrack [6], and OC-SORT [10] on the MOT20 dataset. The results were directly obtained from the official evaluation server of MOT Challenge.  
Our method achieved the best performance in HOTA, IDF1, and AssA, while also achieving near-optimal results in MOTA and DetA. The improvement on the MOT20 dataset is relatively limited compared to the DanceTrack dataset. We believe there are two main reasons for this. First, the scale of the MOT20 dataset is much smaller than that of the DanceTrack dataset, which may not fully assess the performance of the method. Second, the MOT20 dataset primarily comes from surveillance scenarios, where the tracked objects are usually pedestrians whose behavior patterns are closer to simple linear motion, causing the method's performance to become saturated. In contrast, the DanceTrack dataset consists of videos of people dancing, where the behavior patterns of the objects are complex and nonlinear, allowing our method to better demonstrate its advantages.  
We found that on the MOT20 dataset, our method achieved the best performance in HOTA, IDF1, and AssA, indicating significant improvements in object association. However, MOTA and DetA show slight declines compared to the baseline model ByteTrack, suggesting that our method performs slightly worse in object localization. We believe this may be due to the fact that we introduced more historical information, and the high density of people in the MOT20 dataset led to more localization errors during the detection stage. As the historical information accumulates, these errors may gradually propagate over time, ultimately affecting the accuracy of object localization.
## 5 Evaluation
### FIO Micro-Benchmark
Therefore, QZRAM needs a strong ability to handle requests in parallel. Figure 9 shows the performance of QZRAM and ZRAM under different workloads. When the workload is 1, although the throughput of both ZRAM and QZRAM is around 670 MiB/s, the CPU usage of QZRAM is 36.6% lower than that of ZRAM. Both the throughput and CPU usage of ZRAM and QZRAM increase as the workload increases. With a workload of 16, the QZRAM CPU usage is reduced by up to 49.2% compared to ZRAM. This means that  
Figure 8: Performance of different IO patterns. Figure 7: Write throughput and latency with different block sizes. QZRAM saves a lot of CPU resources without decreasing the throughput. At the same time, we can see that the throughput of either ZRAM or QZRAM does not increase with the increase in the workload because it increases the competition for resources within the system. In addition to resource competition, we suspect that the QZRAM throughput did not increase due to the limited number of compression instances per QAT accelerator card (18 compression instances), which is obviously not enough to be used by 88 workers at the same time. One solution is to insert more QAT accelerator cards into the machine because if the physical machine has a large number of cores, using a few more QAT cards can maximize the potential of QZRAM. In other words, QZRAM has a stronger parallel processing ability compared to ZRAM. Figure 10 shows the read and write request latency of ZRAM and QZRAM under different workloads. In the graph, it can be seen that the average read delay of QZRAM increases by 54 microseconds compared to ZRAM, which is consistent with the throughput difference between the two in the second experiment. At the same time, the write delay of QZRAM is essentially the same as that of ZRAM. This result is partly due to the additional delay introduced by the PCIE data transmission between the QAT and CPU in the compression process. On the other hand, it is because the GZIP algorithm implemented by the software in the QAT hardware uninstallation module adds a header and footer to the compressed data packets to be compatible with the compressed data. Figure 10: Performance of different workloads. Figure 9: Performance of different workloads.
# Revisiting the Analysis of Radiative Mid-Range Wireless Link for Pacemakers
## 3 Results
### Evaluation of Gain Reduction Factor  
A useful representation of the reduced antenna gain in the radiative near-field region is the gain reduction factor. The effects of the significant decrease in the near-field power transmission indicate that the reduction in antenna gain exists in a proximity distance. The near-field distribution of a patch antenna inside the human tissue is presented to examine the gain reduction effect of the biomedical devices. The radiation characteristics are investigated for the patch antenna immersed with different depths of the human tissue, whose specification was presented in [10]. The size of the implanted patch antenna is 20 cm \(\times\) 20 cm, which is located between two 3.4 mm thick substrates with \(\varepsilon_{r}=10.2\). The size of the reader patch is 40 cm \(\times\) 40 cm, which is printed on 3.4 mm thick substrates with \(\varepsilon_{r}=2.2\). As shown in Figure 3, the radiation pattern obtained from the full-wave simulation is varied depending on different distances \(R\) between the implanted antenna and the measured points. The radiation patterns, changed from the flat radiative pattern to the directive far-field radiation pattern, are described. The radiation patterns are obtained with different thicknesses of the human tissue, which are set as 4 mm and 8 mm. Since the thickness of the human tissue is comparable to the distance \(R\) for the case of small \(R\), the distance \(R\) is measured as the wavelength in the air, \(\lambda_{0}\) and the one in skin tissue, \(\lambda_{\mathrm{e}}\). The broadside gains at the different positions are used to derive the curve of the gain reduction factor. The gain reduction factor is obtained using the asymptotic formula presented in [29]. The modified antenna gain is applied to increase the accuracy of the formula in [29]. The modified gains for the reader patch antenna and other patch antennas immersed with different depths of skin are obtained through using the switching function \(F(G)\). It is observed that the gain reduction for the case of the reader patch antenna is greater than the other case of the implanted antenna. It is attributed to the fact that the gain reduction factor with the higher far-field gain typically produces the larger reduction in the antenna gain at the same distance \(R\).  
A comparison between calculated and simulated gain reduction factors is provided in Figure 4, which shows a good agreement between those two results except for a very close distance. For a very close distance which is less than 0.5\(\lambda\), the gain reduction factor rapidly decreases due to the failure of the quadratic form of gain reduction factor in the close proximity. The power transmission based on the gain reduction factor is investigated in the following subsection.