esa-sceva/satcom-chunk-collection huggingface dataset:
# Clinical Applications of Optical Coherence Angiography Imaging in Ocular Vascular Diseases
## 5 Ocular Ischemic Syndrome  
Ocular ischemic syndrome (OIS) is the result of the chronic hypoperfusion of the eye due to atherosclerotic stenosis and occlusion of the common or internal carotid arteries. It is usually unilateral, presents in middle age, and commonly affects men. Carotid artery disease is associated with diabetes, hypertension, hyperlipidemia, and ischemic heart disease [72; 73]. In OIS, there is decreased blood flow in retrobulbar vessels and even reversal of blood flow in the ophthalmic artery due to shunting of blood away from the eye to the low-resistance intracranial circuit [72]. Vascular complications in the anterior segment include iris rubeosis, neovascular glaucoma, and uveitis. Classically, in the posterior segment, mid-peripheral retinal dot-blot hemorrhages are seen. Other signs include narrowed retinal arteries, dilated veins, neovascularization of the disc or retina, cotton wool spots, vitreous hemorrhage, emboli, spontaneous arterial pulsations, and macular edema [73].
###### Abstract  
Imidophosphoric organic esters containing phosphoryl groups are potential polydentate ligands and promising extractants of rare-earth elements. For their preparation, a monophosphate salt [PC]\({}_{3}\)=N\(-\)PC\({}_{3}\)]\({}^{+}\)[PC]\({}_{6}\)]\({}^{-}\) and short phosphazene oligomers of the general formula [Cl-(PC\({}_{2}\)=N)\({}_{n}\)-PC\({}_{3}\)]\({}^{+}\)[PC]\({}_{6}\)]\({}^{-}\), where n = 4-7, were synthesized via living cationic polymerization of Cl\({}_{3}\)P=NSiMe\({}_{3}\) and used as starting compounds. All phosphazenes were reacted with 2-ethylhexanol to obtain the corresponding esters of imidophosphoric acids (EIPAs). The formation of imidophosphoric acids occurs due to the phosphazene-phosphazene rearrangement of -P(OR)\({}_{2}\)=N\(-\) or -P(OH)(OR)=N\(-\) units, where R = 2-ethylhexyl. The prepared EIPAs were characterized by \({}^{1}\)H, \({}^{31}\)P NMR, and MALDI-TOF analyses and their extractive capacity towards lanthanide ions in aqueous solutions of nitric acid was examined. The EIPAs are mixtures of mono-, di-, and trifunctional compounds of the type H\({}_{x}\)A, where x = 1-3, which can form chelate complexes of lanthanide ions [Ln(A)\({}_{x}\)], where z = 3-6, depending on the chain length. The longer chain EIPAs are more suitable for collective rare-earth elements extraction. A comparison of the extraction properties of the EIPAs with the industrially used polyalkylphosphonitrile acid (PAPNA) was drawn.  
Keywords: imidophosphoric acids; phosphazenes; extractants; 2-ethylhexyl esters; rare-earth elements; living cationic polymerization +
Footnote †: journal: Applied Physics Letters
# All-Integer Quantization for Low-Complexity Min-Sum Successive Cancellation Polar Decoder
## 4 All-Integer Quantization for Successive Cancellation Decoding Algorithm
By incorporating this approach, the decoding process preserves numerical stability in hardware implementation. The next section presents experimental results, analyzing the performance and computational complexity of all-integer quantization SC decoding techniques compared to conventional floating-point MS-SC decoding.
## 4 Results and Discussions
### Maximum Contact Load of DACBB  
Figure 8 illustrates the maximum contact loads of the left and the right rows of DACBB under combined loads. The external axial load and axial preload are 1500 N and 300 N, respectively. Figure 8a shows the maximum contact load of the left row of DACBB. It is clearly seen that the maximum contact load increases with the increase of external radial load. Meanwhile, as angular misalignment gradually increases, the maximum contact load tends to be sharply increased. For the maximum contact load of the right row, as described in Figure 8b, the variation of the maximum contact load shows the opposite tendency compared to that of the left row. As the external radial load increases, the maximum contact load tends to be decreased. Similarly, the maximum contact load of the right row also increases with the increase of misalignment angle. Relatively speaking, the effect of angular misalignment on the maximum contact load is more significant than that of radial load.  
Figure 7: Displacements of DACBB under different external loads and axial preloads \(F_{0}\): (**a**) radial displacement versus external radial load; (**b**) axial displacement versus external radial load; (**c**) radial displacement versus external axial load; (**d**) axial displacement versus external axial load.  
Figure 9 describes the dependence of the maximum contact load of the left and the right rows of DACBB on the axial preload and angular misalignment. The external radial and axial loads are both 1500 N. From the results shown in the Figure 9, it can be found that the maximum contact loads of the left and the right rows of DACBB both increase with the increase of the axial preload. The impact of angular misalignment on the maximum contact load of DACBB is the same as that shown in Figure 8. Meanwhile, compared to the axial preload, angular misalignment has a greater influence on the maximum contact load.
The properties assessed through this method are measured on small samples which, however, are not able to represent the full-scale element in an adequate way. In addition to DSC, the T-History (Temperature-History) method is also used in laboratories to assess the bulk properties of PCMs. Although the latter technique allows the problem related to the small size of the sample (which is typical of DSC methods) to be overcome, it still has limitations and drawbacks. In fact, it works well for bulk materials, but it is not suitable for application to components. The PCM is macro encapsulated inside a structure that has a rather complex geometry, which can cause variations in shape/thickness from one point to another and the possible presence of air pockets [11]. For these reasons, a new measurement approach, based on the Dynamic Heat Flow Meter apparatus (DHFM), has recently been introduced and adopted in several research studies to assess the heat storage capacity and to analyze the dynamic behavior of macroencapsulated PCMs [23; 24; 25; 26; 27]. Through the adoption of the DHFM, it is possible to measure the equivalent thermal properties of a full-scale panel containing PCMs, in accordance with the ASTM C1784:2020 standard [28]. This paper presents the results of laboratory and in-field analyses that were performed on a series of macro encapsulated PCMs. The laboratory tests were aimed at assessing the equivalent thermal properties of PCMs with different nominal phase change temperaturesencapsulated in the same envelope. An exhaustive and reliable thermal characterization of this configuration was achieved through these tests. Furthermore, to assess the impact of the application of PCMs to building components, two selected PCM panels were applied to a roof component in a parallel test room system and compared with a reference roof without any PCM. A monitoring campaign was carried out to identify the thermal performance of this system under actual operating conditions and to assess the achievable benefits, in terms of temperature peak shifting and shaving and reduction in the heat gains. Finally, from the laboratory characterization results and from the application monitoring experience, some design guidelines were drawn up to maximize the exploitation of the PCM latent heat storage capacity.
## 4 Discussion
### The Reasons for the Low Accuracy of the Medlyn-PM Model and the Lack of the ANN-PM Model  
The reason for the degraded performance of Medlyn-PM in estimating cropland ET, as compared to ANN-PM, is that the effect of soil evaporation is not considered in the model. ET includes soil evaporation and plant transpiration, as well as part of the contribution of canopy interception. Soil evaporation cannot be ignored in ET. [PERSON] et al. [68] investigated the contribution of soil evaporation to ET of winter wheat under sprinkler irrigation. Their results showed that soil evaporation was an important part of ET, accounting for 20-28% of ET. [PERSON] et al. [69] used a large-scale weighing permeameter and a micro permeameter to measure the daily evaporation and ET in winter wheat fields, and the study showed that soil evaporation accounted for 30% of the ET. [PERSON] et al. [70] also showed that evaporation accounted for 32% of the total ET during the growth of winter wheat and 65% in the early growth period. These indicated a considerable contribution of soil evaporation in ET. Since ANN-PM used ANN to estimate the bulk surface conductance, which accounts for the effect of both stomatal and soil conductance, it has been found to perform better than the Medlyn-PM model.  
The remote sensing information allows ANN-PM to simulate spatiotemporally continuous ET information [71]. However, we did not exhaust all possible RS data in the ANN-PM, which is beyond the scope of this study. In the future, we can evaluate more RS data to improve the accuracy of the ANN-PM model. For example, the development of multi-source RS data and surface parameter inversion products can provide PM models with some basic parameters that promote their application [72], so multi-source remote sensing data and PM models can be combined to estimate cropland ET.
# Classification between Failed Nodes and Left Nodes in Mobile Asset Tracking Systems +
## 5 Performance Evaluation
### Simulation
#### 5.1.5 Elevator Waiting Time  
This section evaluates the impact of the waiting time to get on an elevator. As the emergency room is in a 12-story building with two elevators and the elevators are located outside the emergency room, a patient with a mobile medical asset has to take an elevator to go to another department. Therefore, the waiting time for an elevator makes a mobile node send its value frequently from outside of the monitoring area. In this simulation, the report interval, the moving speed and the missing neighbor rate are set to 10 s, 1.1 m/s and 0%, respectively. The elevator waiting time varies from 0 to 50 s. Figures 5d and 6d show the results. The \"max\" in the figures indicates the maximum window size.  
In Figures 5d and 6d, the graphs (max = 5) clearly show that the success rate decreases when the waiting time is both 40 and 50 s. This result indicates that some left nodes are classified incorrectly as failed nodes. These incorrect decisions occur because \(Q\) and \(R\) have the same data. If the waiting time is long, a mobile node sends many reports at the same location outside of the monitoring area. In this case, \(Q\) and \(R\) can contain the same neighbor data if the node sends the same value more than five times, the maximum length of \(Q\) and \(R\). As these identical values in \(Q\) and \(R\) generate an unchanged trend, the left node is considered as a failed node. In this case, the range of the lengths of \(Q\) and \(R\) is 2\(\sim\)5.  
This problem can be solved if the lengths of \(Q\) and \(R\) are variable. The length of \(Q\) increases until a value greater than the threshold \(T_{nc}\) appears. Then, \(Q\) and \(R\) can contain at least one value reported by a mobile node in the monitoring area. The graphs (max = 7 and max = 8) in the figures show the result using the variable length. The range of the lengths is 2\(\sim\)9 in Figure 5d, and it is 2\(\sim\)7 in Figure 6d. The figures show that the proposed classifier separates all failed nodes from all left nodes correctly.
## 5 Experimental Analysis
### Algorithm Performance
#### 5.2.2 Comparative Analysis of the Impact of Weights  
Figure 10 illustrates the impacts of various weight combinations on the performance of the algorithm. The left sub-figure displays the convergence curves corresponding to the following four distinct weight configurations: one that prioritizes demand coverage, one that emphasizes delivery cost, one that focuses on delivery time, and a balanced weight configuration. It is evident that the configuration prioritizing demand coverage (represented by the blue curve) exhibits the most rapid convergence and the lowest final objective function value. This suggests that, within the context of this study, prioritizing demand coverage may enhance overall performance. Conversely, the configuration that emphasizes delivery time (indicated by the yellow curve) demonstrates a slower convergence rate and a higher final objective function value, implying that an excessive focus on the time dimension may lead the algorithm to converge on a sub-optimal solution.  
Figure 10: Comparative analysis of convergence speed and final outcomes across varying weight parameters.  
Figure 9: A comparative analysis of the convergence processes of various heuristic algorithms.  
The radar chart presented on the right effectively illustrates the final performances of the various weight combinations across three objective dimensions. The configuration that prioritizes demand coverage demonstrates a superior performance in the coverage index; however, it incurs a slight disadvantage regarding delivery costs. Conversely, the configuration that emphasizes delivery costs excels in the cost index, but exhibits a relatively low coverage. The balanced weight configuration attains a more equitable performance across all three dimensions. Depending on the specific requirements of different scenarios, the weight configuration can be adjusted accordingly to achieve a more optimal solution.
## 4 Conclusions  
In this paper an approach was proposed to detect similarity between texts. The approach was based on the text splitting into n-grams and evaluating it using a SOM and similarity measures. The detection of similar texts was made in three steps: (1) text dataset conversion to numerical expression using n-grams; (2) calculation of similarity measures; (3) text dataset visualization using SOM and similarity representation on it. At the first step, the main focus was to create a bag of n-grams of all datasets. The various number of words in n-grams were analyzed. In addition, different filters were applied: numbers and punctuation removing, words frequency, uppercase transform, stemming algorithm, etc. The analysis showed the filters and size of n-grams influenced the final results. For this dataset, the size of the n-grams was selected and equal to three for the experimental investigation. At the second step, the four similarity measures were calculated: cosine, dice, extended Jaccard's, and overlap. Final results showed that the highest percent of similarity was obtained using overlap measures. The other three measure values were always similar and smaller. The usage of SOM showed that SOM helps to see the summarized results of all texts' similarity in visual form quickly. It is very easy to understand which texts are similar to each other or not. In the analyzed dataset case, the SOM helped to detect similarity, and the formed clusters were correlated with the given categorical description of the dataset.  
The experimental investigation showed that the most accurate similarity measure is overlap because this measure detected more near copy texts and gained the highest percent. Sometimes it showed even full texts overlap which can be defined as plagiarism. The SOM helps to summarize the full dataset similarity in visual form, but it is hard to confirm how much texts are similar to each other. The investigations showed that SOM was more useful as an additional tool to decide which texts could be similar and deeper investigation could then be applied. The usage of n-grams and creation of a bag of words showed that it is an effective way to find similarity between texts. Deeper analysis has to be made to detect how all filters, size of n-grams, and other texts' conversation to numerical expression affect the final results for much longer texts' datasets. So it is purposeful to analyze them in more detailed in the future. The proposed approach allowed finding similarity between texts and evaluating results by combining SOM and numerical estimations helped to make a deep analysis.  
Designing and performing measurements, P.S., O.K. and R.S.; Data analysis, P.S., O.K. and R.S.; Scientific discussions, P.S., O.K. and R.S.; Writing the article, P.S., O.K. and R.S.  
This research received no external funding.  
Figure 7: \(9\times 9\) self-organizing map: fifth question (\(Q_{5}\)).
# Comparison of Single-Arm and Dual-Arm Collaborative Robots in Precision Assembly
## 3 Results and Discussion  
### Time of the Assembly Process  
In variant I, a single-arm YuMi robot was used to assemble the computer motherboard. The total simulation time is 20.6 s. During the simulation, the robot performs all necessary tasks related to motherboard assembly, such as picking up components and placing them on the board. The times of individual tasks performed by the robot are presented in Table 2 and in the chart in Figure 11.  
The largest part of the time involved free movements within the workstation, mainly extending the gripper to the place where the part is picked up. These movements accounted for 43% of the time of the entire assembly process. This indicates that the robot performed activities related to the manipulation of elements for 8.7 s. The next longest task was mounting the processor due to the longest distance the robot arm had to move. The operation of pressing the RAMs took 3.6 s, or about 17% of the time of the entire operation, and mounting the RAMs was 10% for each of them.  
In variant II, a dual-arm YuMi robot was used. The total simulation time is 13.6 s. Both robot arms are able to perform tasks simultaneously, which speeds up the motherboard assembly process. During the simulation, one robotic arm can pick up parts while the other places them on the board. All values related to time analysis are presented in Table 3 and charts in Figures 12 and 13. In the time analysis in variant II, the percentage of time of each task on both arms was considered and, additionally, as in variant I, the ratio of the robot's work to free movements.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Task** & **RAM 1** & **RAM 2** & \
\begin{tabular}{c} **Pressing the** \\ **RAMs** \\ \end{tabular} & **Processor** &
\begin{tabular}{c} **Free** \\ **Movements** \\ \end{tabular} & **Total** \\ \hline Time [s] & 2.1 & 2 & 3.6 & 4.2 & 8.7 & 20.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The time needed to complete individual assembly tasks by a single-arm robot.  
Figure 11: Percentage of individual robot assembly tasks for variant I.  
The operating time of the left robot arm in variant II includes a large amount of free movements of over 50%. This arm places the first RAM in its appropriate slot and then reaches for the processor and mounts it in the designated place. These activities take 2.6 s and 3.9 s respectively. For the right arm, the free movements are much higher and amounts to 68%. This arm is responsible for mounting the second RAM and for pressing RAM No. 1 and 2. Both of these tasks took a total of 4.5 s. Taking into account the entire operation performed by both robot arms in variant II, summing up the assembly times of each part, free movements took only 20% of the time of the entire process.
## 5 Simulation
### Performance Evaluation with Source Node Set  
The work in [3] performs simulation by using a fixed source node that sends data to the sink. Using only a fixed source node is not enough to simulate the behaviors of WSNs. In practice, data is always generated by different sensor nodes distributed across an area. It is insufficient to evaluate the PIP with a fixed source node. In fact, our approach can achieve better performance as long as there is secure path from the source node to the sink. Therefore, we evaluate the overall PIP with a collection of source nodes. We select a collection of source nodes and each one is likely to generate data and send it to the sink. Then we evaluate the overall performance for our approach for the collection of source nodes.  
The process of simulation with a collection of source nodes is similar with that of a fixed source node. We evaluate the data collection algorithms for each source node in the collection and record the accumulative result for the collection. Here the number of the source nodes in the collection is 10 (see Table 1). As can be seen in Figure 6(b), the performance of SPDC is better than DRP and NRRP with the same number of compromised nodes. However, the advantage of SPDC is not very obvious against the other algorithms in this situation. The performance of DRP and NRRP is close to SPDC.
# An Efficient Data Retrieval Parallel Reeb Graph Algorithm
## 8 Applications
### Choosing Morse Scalar Functions  
The cutting system and pant decomposition curves that we presented in this section depend on the choice of the scalar function used to define the Reeb graph in the following way. In particular, the choice of these curves affects the final quality of the segmented mesh. For this reason, it is important to choose a scalar function with certain desirable properties. By desirable properties, we mean the following:  
1. The scalar function has a small number of critical vertices.
2. The level sets of the scalar function follow the geometry of the mesh as closely as possible.
3. The scalar function requires minimal input from the user.  
We briefly discuss several scalar functions with the above properties.
# Natural Photosensitizers in Clinical Trials
## 4 Natural Photosensitizers Pending Clinical Trials  
### Furanocoumarins  
Furanocoumarins are a class of natural compounds produced by several plants. They have been used in the medicine of Eastern countries for centuries [83]. Psoralen is the primary compound in the group of linear funcomourins, characterized by photosensitive properties. It initially forms a non-linear bond with DNA. Upon exposure to UV radiation, cycloaddition occurs and a covalent bond is formed between psoralen and the pyrimidine base. Additional interactions between the psoralen monoadduct and the pyrimidine base can occur during subsequent phases of UV exposure, resulting in the formation of cross-links between DNA strands. In addition, psoralens react with other cellular components, such as proteins or lipids [84]. To date, it has not been clearly established whether oxygen-dependent mechanisms are important for the induction of apoptosis in therapy. The involvement of oxygen-dependent reactions in induced apoptosis appears to be mainly related to psoralen photo-oxidation and opening of the mitochondrial permeability transition pore (PTP), rather than direct damage caused by reactive oxygen species. Previous work has shown the potential of using psoralen as a photosensitizer in PDT of melanoma and cutaneous T-cell lymphoma [85; 86; 87].
## 5 Establish the Short-Term Load Forecasting Model
### EN-GMDH Short-Term Load Forecasting  
EN-GMDH uses the elastic net to replace least square estimates to estimate the coefficient of the K-G polynomial. The EN-GMDH algorithm flow is shown in Figure 2.  
Figure 2: Group method of data handling based on elastic net (EN-GMDH) algorithm flow diagram.  
In traditional GMDH, the coefficients of the K-G polynomial are estimated by least square fitting, which has no function of compressing characteristic variables. The characteristic variables with small contributions to load prediction are directly eliminated after the evaluation of external criteria. Although these characteristic variables have little influence on load prediction, their removal will enlarge the prediction error. Elastic net, while estimating the K-G polynomial coefficient, compresses variables with little influence on the output, limiting their contribution to load forecasting rather than eliminating it directly. In this way, the polynomial coefficients can be estimated more accurately.