esa-sceva/satcom-chunk-collection huggingface dataset:
# The Value of Fractal Analysis in Ultrasound Imaging: Exploring Intricate Patterns
## 3 Results
### Bone Texture Analysis  
Some studies have utilized fractal analysis on radiographic images to evaluate trabecular bone pattern and density, which are crucial for assessing bone health and osteoporosis risk [9; 10]. For instance, dental radiographs, including panoramic and cone-beam computed tomography (CBCT) images, have been extensively analyzed using fractal dimension to distinguish between healthy and osteoporortic patients. These analyses revealed that higher fractal dimension values are associated with denser and more complex trabecular patterns, typical of healthier bone structures. Systematic reviews and meta-analyses have consolidated data from various studies [10; 11; 12; 13] to establish the reliability of fractal dimension in diagnosing osteoporosis. These studies often involved comparing fractal dimension values from radiographic images of healthy controls and osteoporortic patients. The findings consistently show significant differences in fractal dimension values, supporting the use of fractal analysis as a noninvasive diagnostic tool [10]. Combining fractal analysis with other diagnostic measures, such as ultrasound broadband attenuation T-scores, enhances the overall assessment of bone health. This combination allows for a more detailed evaluation of bone microarchitecture and structural integrity, providing a comprehensive diagnostic approach [9]. This integrated method not only improves the detection of osteoporortic changes but also helps monitor the progression of disease and evaluate the effectiveness of treatment interventions [9] (Figure 2).  
Figure 2: Schematic representation of main organs and systems evaluated by ultrasound imaging using fractal analysis.
# The Potential of Visible and Far-Red to Near-Infrared Light in Glaucoma Neuroprotection
## 3 Pathophysiology of Glaucoma: Increased IOP, Mitochondrial Dysfunction, and Altered Ion Fluxes
Animal studies have specifically shown that optimum treatment conditions using red light for a short period of time (16.5 watts/m\({}^{2}\), 3000 lux, 625-635 nm) can attenuate an insult of raised IOP to the rat retina and retinal dysfunctions [9], with no detectable harmful effects on the lens, cornea, and retina, thus dispelling previous doubts about its therapeutic safety [45; 46]. The beneficial effects of PBM on the neural and vascular elements of the retina have also been supported by a study from [PERSON] et al., which showed that a 670 nm light 9 J/cm\({}^{2}\) used once daily over 5 days reduces the Muller cell-mediated retinal inflammation of an animal model both in vivo and in vitro [47; 48], offering a potential cellular mechanism for 670 nm light therapy in regulating inflammation associated with retinal degenerations. In fact, both the expression of COX5a and the mitochondrial membrane potential (\(\Delta\)Ym) were increased, and the microglia/macrophage recruitment into the outer retina was reduced. This is of importance, as non-neuronal components of the retina and glial-derived Muller cells, are the supporting cells of the retina that play a key role in maintaining retinal homeostasis and responding to retinal stress through the release of chemokines to recruit microglia and macrophages into the damaged retina, where gliosis is a feature of eye disease progression. This highlights the value of low-level laser therapy (LLLT) as a novel paradigm to treat visual failure, and it supports the opinion that neuronal energy metabolism could constitute a major target for neurotherapeutics of both the brain and the eye [15]. Interestingly, calcium channels are modulated by red light, as demonstrated by recent works. In fact, a study reported that an LED light at 630 nm (26 and 39 J/cm, 4 times every 3 h within 12 h) could promote TRPV4 expression in human synovial fibroblasts [49]. These observations suggest the utility of further studies investigating the effects of PBM on TRPV4 in glaucoma. A schematic representation of putative protective effects of PBM on cell metabolism is shown in Figure 2. In glaucoma, mitochondrial dysfunction is a component of a complex multifaceted disease, together with other signs of derangement such as increased IOP and defective ion fluxes. Restoring mitochondrial activity could affect RGC survival differently, depending on the severity of the other pathological components; however, the potentiated energy production should at least be a beneficial, if not a resolutive, approach. Based on these considerations, photobiomodulation could stimulate several defective components and could have a stronger beneficial effect than the pharmacological single-target treatments.
## 3 A Generalized Approach for Multiple Event Types
### Protocol and Algorithm
In other words, we ensure that _Event_ packets for which the sink is willing to pay a higher price has higher accumulated survival probability (\(P_{i}k_{i}\)) and the ratio of the cumulative survival probability follows the ratio of the prices. If two classes of packets traverse through similar network conditions, the ratio of throughput of these two types of packets should be similar to the ratio of the prices. Note that network condition includes both network link quality and the probability of being dropped in a node along the path to the sink. If the prices of two classes of packets are the same, we would like the probability of packets received at the sink to be the same. If all the prices are equal, the optimization problem becomes the same as Section 2.. Since all _Non-Event_ packets have the same price and we selectively drop _Non-Event_ packets, Equation 14 becomes \[\text{maximize}_{\{\vec{K_{N}}\}} \frac{(\sum P_{i}\times k_{i})^{2}}{N_{N}\times\sum(P_{i}\times k_{i})^{2}}\] (17) Note that if \(R=0\), then \(k_{i}=0\). 3. \(Q_{max}\leq N\leq Q\): After dropping all _Non-Event_ packets, begin dropping _Event_ packets since the buffer is highly utilized. The dropping strategy follows the optimization problem given by, \[\text{maximize}_{\{K_{E}^{\prime}\}} \frac{(\sum_{j\in N_{E}}\frac{P_{j}\times k_{j}}{R_{j}})^{2}}{N_{E} \times\sum_{j\in N_{E}}(\frac{P_{j}\times k_{j}}{R_{j}})^{2}}\] (18) such that \[\sum_{j\in N_{E}}k_
# Mixed Reality Enhanced User Interactive Path Planning for Omnidirectional Mobile Robot
## 6 Experimental Results and Analysis
### Path Planning with Virtual Obstacles  
This part of the experiment showed the path planning effect of the mobile robot after adding virtual obstacles to the map with HoloLens. We used the same map as the previous experiment and set a starting point and a target point. The experimental operation process was as follows: When the mobile robot moved to a certain position, two virtual obstacles were added to the map through gesture control of HoloLens. Then, the path planning algorithm took these two virtual obstacles into account and re-planed the path. The scenes in HoloLens before and after the operation of adding obstacles are shown in Figure 10a,b. The actual path to avoid virtual obstacles in RViz is shown in Figure 10c,d. The trajectory comparison of before and after the addition of virtual obstacles is shown in Figure 11a. It can be seen that in the process of motion, when the mobile robot detected the suddenly added obstacle, it would immediately change the previously planned trajectory, and it then re-planed the trajectory according to the new map and moved according to the new trajectory.  
The changes of velocity and angular velocity are shown in Figure 11b,c. The limitation of the velocity was also to protect the mobile robot. After adding obstacles, the two curves would change obviously due to the sudden detection of obstacles. When adding virtual obstacles, its position could not be too close to the moving mobile robot, otherwise, obstacle avoidance would be confused.  
Figure 12a,b respectively show the performances of the traditional VFH* path planning algorithm and the DWA algorithm in the scene where virtual obstacles were suddenly added. It can be seen that neither of the two methods could complete the experiment well. When using the DWA method, it was difficult to update the path in time, so after adding virtual obstacles to the map, the mobile robot did not correct the path to avoid the obstacles, but still moved according to the original path, failing to reach the target point. Compared with the traditional VFH* method, the main reason why the improved VFH* has a better effect is that the new cost function fully considers the characteristics and requirements of the system. Since the platform used was OMR, the unaffected part of the original function was removed, and the coefficient of the function was modified to make the algorithm more sensitive to the virtual obstacles suddenly added in the distance and to adjust the path in time.  
Figure 10: The trajectory in HoloLens and RViz before and after adding virtual obstacles. (**a**) The scene in HoloLens before adding virtual obstacles. (**b**) The scene in HoloLens after adding virtual obstacles. (**c**) The scene in RViz before adding virtual obstacles. (**d**) The scene in RViz after adding virtual obstacles.
## 2 Materials and Methods
### Synthesis and Characterization of Poly-d,l-lactide Alkyne Via Amide Bond Formation  
Propargylamine was conjugated to PLA of various lengths through amide formation in the presence of EDC and NHS. The general procedure was as follows: PLA-COOH (1 eq) was dissolved in DMSO (250 mg/mL); then, EDC and NHS (2 eq, 0.3 mmol/mL) were added under stirring at room temperature. Propargylamine (2 eq) was then added, and the reaction mixture stirred for another 24 h. The crude product was obtained by precipitation in double distilled water, and it was purified by exhaustive washings with the same solvent. A white powder was recovered by freeze-drying. Yield 89 \(\pm\) 1%. \({}^{1}\)H NMR (400 MHz, DMSO-\(d_{6}\)) & 8.48 (m, 1H), 5.17 (tq, \(J\) = 19.6, 6.1 Hz, 1H\({}^{*}\)PD), 4.20 (d, \(J\) = 6.6 Hz, 1H), 3.87 (dt, \(J\) = 5.7, 2.8 Hz, 2H), 2.15 (s, 1H), and 1.67-0.83 (m, 3H\({}^{*}\)PD). ATR FT-IR (cm\({}^{-1}\)) 3507, 3411,2995, 2945, 2884, 2638, 1748, 1634, 1526, 1452, 1381, 1364, 1318, 1269, 1185, 1129, 1083, 1052, 956, 864, 748, and 693.
## 5 Simulation and Results
### Simulation Settings  
We assume that a time segment, i.e., a beam hopping cycle, has 256 time slots and the length of each time slot is 10 ms. Service requests generated by terrestrial cells obey Poisson distribution. In order to avoid congestion caused by excessive traffic, the traffic waiting for more than 50 time slots will be discarded, that is \(T_{ttl}=50T_{slot}\). The main simulation parameters are listed in Table 3.  
The following performance metrics are presented to evaluate the performance of the LEO beam hopping communication system.  
* Throughput: The system throughput is defined as the average throughput of 12 beams over the entire BH cycle.
* Access Success Rate: The access success rate is defined as the ratio of the number of packets served of all cells in the BH cycle to the demand generated.
* Average Delay: The average delay is defined as the average queuing time for all served packets.
* Fairness: The fairness between cells is defined as the variance of the average delay of the packets served by each cell.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of cells \(N\) & 195 \\ Number of hopping beams \(K\) & 12 \\ Frequency of downlink \(f\) & 30 GHz \\ Total transmission power \(P_{tot}\) & 360 W \\ Total available bandwidth \(B_{tot}\) & 250 MHz \\ Number of sub-bands \(m\) & 10 \\ Interference threshold to GEO system \(I_{th}\) & \(-\)132.5 dBW \\ Packet size \(ps\) & 1200 bit \\ Packet Poisson arrival rate \(\rho\) & 2 \(T_{slot}\) \\ Number of time slots in a BH cycle \(k\) & 256 \\ Length of time slot \(T_{slot}\) & 10 ms \\ Maximum queuing time \(T_{ttl}\) & 50 \(T_{slot}\) \\ Weight of traffic amount \(\omega_{1}\) & 0.5 \\ Weight of traffic emergency \(\omega_{2}\) & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: System parameters.  
In this paper, we compare our proposed algorithm with three different BH schemes, and a brief description of the comparison algorithms is as follows.  
(1) The polling beam hopping (Polling-BH): divide all cells into 12 uniform blocks, and each beam will serve the cells in a block in turn in a fixed order. Due to the inherent geographic isolation, each beam can use the full bandwidth.  
(2) Random beam hopping (Random-BH): the random hopping method randomly selects K cells from N cells for service at each time slot. Moreover, the bandwidth of each beam is also chosen randomly from \(m(m+1)/2=55\) available bandwidth allocation schemes at each time slot.  
(3) Genetic algorithm without interference cluster (GAWIC-BH): the weighted greedy strategy is still used to determine the lighting cells of each time slot, but we do not perform the interference clustering in the resource allocation stage. All 12 illuminated cells adopt a genetic algorithm to search the joint bandwidth and power assignment. The iteration times for each slot is set as 200, which is four times that in our proposed improved algorithm.
## 3 Proposed Method
### Peso
#### 3.3.1 Semantic Location Vector  
It is insufficient to consider only the previous ten track points before the current timestamp as spatial correlation. In our model, we consider the spatial correlation between the future trajectory points and the observable historical trajectory points of a vessel, which can better simulate the navigation process and obtain satisfactory prediction results. Figure 3 shows the workflow of obtaining the Semantic Location Vector. Technically, we mesh the areas involved in the trajectory points of the vessel in the training set, with 0.1 latitude and 0.1 longitude as a grid. We segment the trajectory according to the mechanism of sliding windows into several groups. Each group consists of 5 trajectory points (\(p_{1}\), \(p_{2}\), \(p_{3}\), \(p_{4}\), \(p_{5}\)), and we calculate the corresponding grid serial number (\(g_{1}\), \(g_{2}\), \(g_{3}\), \(g_{4}\), \(g_{5}\)) for each group. CBOW is an algorithm converting words into vectors in natural language processing, which predicts the central word from surrounding words. PESO mainly utilizes the CBOW model to map the grids to 8-dimensional vectors with spatial location semantics. The CBOW model consists of an embedding layer \(Embedding\) and two linear layers \(Linear_{cbow1}\) and \(Linear_{cbow2}\). The embedding layer aims to encode the grid number into an 8-dimensional vector, and the loss function is the cross-entropy function. The training process can be summarized as follows:  
\[\begin{split} inp_{cbow}&=(g_{1};g_{2};g_{4};g_{5}) \\ emb_{cbow}&=Embedding(inp_{cbow})\\ fea_{1}&=Relu(Linear_{cbow}^{1}(emb_{cbow}))\\ out_{cbow}&=LogSoftmax((Linear_{cbow}^{2}(fea_{1})) \end{split} \tag{2}\]  
where \(out_{cbow}\) is the output of CBOW, and \(emb_{cbow}\) is the semantic vector of the grid with number of \(g_{3}\). \(Relu\) and \(LogSoftmax\) denote nonlinear activation functions, and the objective function is:  
\[objective_{cbow}=CrossEntropy(out_{cbow},g_{3}) \tag{3}\]  
Figure 3: Workflow for obtaining the Semantic Location Vector. First, we divide the sea areas of our dataset with 0.1 latitude and 0.1 longitude as a grid. Then, we use sliding window to conduct a training set of a CBOW model. After training, this model can map every grid to an 8-dimensional semantic vector. Finally, after collecting all the grids corresponding to a vessel, we average the semantic vectors of these grids to obtain the Semantic Location Vector (SLV).  
When the training process of the CBOW model is completed, we will obtain the semantic vectors corresponding to each grid on the map. Precisely, for each ship in the training set, we will obtain the semantic vectors corresponding to all the track points. Furthermore, we average the semantic vectors of all the track points of a ship to obtain the Semantic Location Vector of the ship. Take a vessel of ID \(i\) as an example, and suppose there are \(k\) track points in total associated with the vessel. The formula can be computed as:  
\[SLV_{i}=Avg(slv_{1}^{i},slv_{2}^{i},slv_{3}^{i},\ldots slv_{k}^{i}) \tag{4}\]
# Finding the Least Motion-Blurred Image by Reusing Early Features of Object Detection Network
## 2 Materials and Methods
### Evaluation of the Methods Used for Determination of the Less Motion-Blurred Image  
Evaluation of the proposed and standard methods for finding less motion-blurred (better quality) images was performed using testing data. A description of testing dataset preparation is presented in Section 2.3.  
At the beginning of the photogrammetry pipeline, we want to select only necessary and of the highest quality images for the object reconstruction (see Figure 1). Picking the highest quality image (which, in our case, we defined as having the least motion blur distortions) from a set of images means sorting these images according to their quality and choosing the best one. The test set is constructed so that the method should pick an image from a pair of images. The method assigns some ranking scores to the images, and the appropriate image is chosen. Our current method gives a higher score for less motion-blurred images; the sharpness-based method also gives a higher score for less motion-blurred images. Blind image quality evaluation methods BRISQUE, NIQE, and PIQE provide a smaller score as indicators of better perceptual quality (less motion blur).  
The test set is constructed from original images I1 and their augmented versions I2, I3, I4, and I5. All augmented versions are gradually increasingly affected by motion blur. For the evaluation of the methods, all possible combinations of I1-I5 images were generated. Images that are closer by distortion parameters are harder to differentiate into better and worse according to image quality. The harder pairs are I1-I2, I2-I3, I3-I4, and I4-I5. The easier pairs are I1-I3, I1-I4, I1-I5, I2-I4, I2-I5, and I3-I5.
## 2 Materials and Methods
### Analysis
We also applied Wilcoxon signed rank (i.e., one-sample test) on \(R^{2}\) values of these models (for both polynomial degrees 0 and 4) to determine how significantly better or worse (in the case of \(R^{2}<0\)) than the use of averaged pre-touch distance they performed. For each round, we also calculated the effect size \(r=\frac{W}{\sqrt{N}}\)[30] with \(N\) denoting the sample size (\(N=100\), i.e., RMSE and \(R^{2}\) associated with each of the 1000 random sampling without replacement rounds) and \(W\) denoting the Wilcoxon test-statistics. We Bonferroni-corrected all \(p\)-values by multiplying them with \(N\), given the use of non-parametric tests. We reported the average of these 1000 simulation rounds. To test whether the differences between these models can also be verified in their respective learning process, we further used the full facial pre-touch distance data and trained these models. We then used their estimated pre-touch distances that was based on the full dataset and computed the difference between these estimates (per model) and the actual pre-touch distances and applied [PERSON]'s test on these differences to test for any significant difference. For [PERSON]'s test, we reported the effect size \(r=\sqrt{\frac{\chi^{2}_{\
u}}{N}}\)[31] with \(N\) denoting the sample size and \(\chi^{2}\) is the [PERSON]'s test-statistics. We also computed the RMSE and \(R^{2}\) values of these models on full data. We used Python 2.7 for models' training and testing. All analyses were carried out in MATLAB 2016a. We used MATLAB and the Python version of Raincloud plots [32] for data visualization. We applied Bonferroni-correction to all our tests. Specifically, our analyses were between four settings: \"GP\", \"SVR\", \"Lasso\", and \"Fixed\" pre-touch distance boundary. As a result, the Bonferroni-corrected \(p\)-value for significance level \(p=0.05\) was \(\frac{0.05}{4}=0.0125\).
## 2 Background and Related Work
### Dynamic Matching  
The dynamic matching process's conduction is based on the system users' (or nodes) preferences for the conditions or attribute compatibility. The \"kidney-exchange process\" [17] is the most closely related scenario to our study about dynamic supply-and-demand matching on a time-varying network. In general, we encode a kidney exchange instance as a directed compatibility graph \(G=(V,E)\) and construct one vertex for each patient-donor pair in the pool. Then, an edge \(e\) is created from vertex \(v_{i}\) to vertex \(v_{j}\) if the patient in \(v_{j}\) wants and is compatible with the donor kidney of \(v_{i}\). A paired donor is willing to give her kidney if and only if the patient in her vertex \(v_{i}\) receives a kidney. We also encode our TimeBank instance as a bi-directional directed compatibility graph and construct one vertex for each SP and each SR in the market. Clearly, in our system model, SP plays a similar role as a donor and SR as a patient. And an edge is created from vertex \(v_{i}\) to vertex \(v_{j}\) if the SR in \(v_{j}\) is compatible with the SP's matching criteria in \(v_{i}\) and vice versa. Since the edge is bi-directional, we can simplify it as an undirected one.  
Another topic related to our research is the Stable Marriage Problem (SMP). In mathematics, economics, and computer science, SMP is the problem of finding a stable matching between two equally sized sets of elements given each element's ordering preferences. In 1962, [PERSON] and [PERSON] proved the Gale-Shapley algorithm [18] to solve this problem. There are also related papers discussing the SMP issue in different scenarios [19]. The most significant difference between SMP and our research is that the SMP problem is a preference-based matching issue, and our study focuses on the compatibility-based matching issue. In the future, our system may also integrate with the preference-based matching mechanism.  
[PERSON] [20] considered a model where compatibilities are based on a random graph model. Researches indicated that waiting for the thickening of the market size will yield significant gains if the planner knows the departure time. There are other studies on Imbalanced Markets [21; 22], in which the exchanges of markets are conducted by easy-to-match and hard-to-match agents.  
[PERSON] [23] and [PERSON] [24] studied the housing market's overlapping generation models. In their models, agents have a deterministic schedule in arrivals and departures. Besides, the market's housing side is infinitely durable and static, and houses do not have preferences over agents.  
As pre-described, this work applies the strategy of market size thickening to a blockchain-based TimeBank system's operations. To simplify the analyses, we treat balancing supply-and-demand in a TimeBank as finding a match in a \"two-sided market\" [25], which means in the proposed platform, there are two distinct user groups that provide each other with network benefits.