esa-sceva/satcom-chunk-collection huggingface dataset:
# A Model to Measure U-Learning in Virtual Higher Education: U-CLX
## 4 The U-CLX Model Proposal
### The Mathematics under the U-CLX Model  
Once the requirements, functions, and theoretical specifications of our model had been defined, we decided to add an individual logical-mathematical behavior. This approach has been reported in specific studies [43], and in our case it was based on computation of the elements proper to ubiquity. For example, some of these elements had a maximum of only three study variables; therefore, they used elements of vector algebra mathematics, such as three-dimensional hyperplanes.  
Since the U-CLX model has four dimensions, the model is based on the concepts of the Rn points, lines, vectors, hyperplanes, convex sets, and hyperspheres. In this case, the calculations are in four dimensions, namely R4, time, where, device, and context. The U-CLX model is based on the TAG Model's mathematical idea [26], which measures the level of ubiquity in institutions in three dimensions: learning, technology, and management.  
Because the U-CLX Model has four dimensions, it uses the hypersphere concept in R4. We propose an equation to calculate the volume of a figure in a hypersphere, which will allow us to estimate the level of ubiquitous learning in the U-CLX Model using the volume equation of a hypersphere in four dimensions (R4). In mathematics, an n-sphere is the generalization of a sphere to a Euclidean space of arbitrary dimension.  
The volume of the hypersphere with radius \(r\), in the space of four dimensions with volume n = 4, is given by the following Equation (1) ([PERSON] [48] and [PERSON] [49]):  
\[V_{4}=\frac{\pi^{2}r^{4}}{2} \tag{1}\]  
Note that the U-CLX Model defines four dimensions, each of which is a plane inside the hypersphere: time (T), place (L), medium (M), and context (C). It is flat in the hypersphere, and, therefore, the four planes of the hypersphere correspond to the four dimensions.  
The mathematical description of the model considers the following assumptions: (a) all four variables have the same weight or value, (b) all variables have the same level of development, and (c) all are positive. This assumption is due to the ease of calculating the variables. In addition, no previous study has been conducted to indicate which variable has a greater or lesser weight in the model.  
In the U-CLX Model, all points, lines, planes, and hyperplanes are inside the hypersphere in four dimensions or R4, so the calculated volume of the hypersphere will give the measure of Ubiquitous Learning.  
The four dimensions of the U-CLX Model are the variables with which it is possible to measure the level of ubiquitous learning through the hypersphere. The result is a 4D volume in a solid sphere with four dimensions. The variables cannot be negative; dimensions in the hypersphere start at points (0,0,0,0); the level of the variables or dimensions begins at 0, and the level is E = 0, as shown in Figure 5.  
Figure 5: Hypersphere in four dimensions [2].
## 6 Performance Evaluation
### Evaluation Results
#### 6.3.5 Communication Overhead  
Figure 12 shows the comparison results of communication overhead as a function of the number of Interest packets in the network. For the network overhead analysis, we vary the Interest generation frequency between 5 Interests/secs to 30 Interests/sec. It is evident from the results that the CIDF-WSN has a lower communication overhead compared to the NDN and GIF. It can be observed from the results that the network overhead in the CIDF-WSN is around 1000 KB against 30 Interests/sec while being extremely low in low-interest packet frequency (e.g., approximately 100 KB against 5 packets/secs). The main reason behind the reduced overhead is the proposed Interest and Data transmissions mechanism accompanied by the corresponding Cache table, which significantly reduces the number of Interest and Data packet retransmissions. As already expressed in the previous results, the ICT and TCT tables are utilized by the immediate neighbors during the vulnerable network conditions when the designated nodes fails to perform further transmissions.  
Figure 11: Data energy consumption as a function of Interest.  
The proposed procedure avoids the packet retransmissions by the downstream forwarder and significantly reduces the communication overhead. However, in both GIF and NDN, the communication overhead highly increases with the increase in interest frequency. GIF generates more than the double overhead packets while in the NDN this figure is significantly greater compared with both GIF and CIDF-WSN. In NDN, each node rebroadcasts the packet in case of a cache miss, while in GIF the number of retransmissions increases when a node fails to retransmit the packet further due to its resource constraints and vulnerable channel conditions. All these factors significantly contribute to increasing the overall communication overhead and affect the entire performance of the network.
## 3 Methodology
### Proposed Algorithm  
In our proposed algorithm, Round Robin with Adaptive Priority Scheduling (RRAPS), once the new process enters the ready queue (RQ), the CPU loads the high-priority process for execution. If the ready queue is empty, the TQ is set to the burst time and assigned to the process for execution. However, if the ready queue is not empty, the time quantum is set as the average of the burst times of all the processes in the ready queue. The CPU then executes the process for the assigned time quantum. If the process terminates, it is marked as completed. Otherwise, if the process is not terminated and its priority is greater than 1, its priority is decreased by one and returned to the ready queue with its updated burst time. This allows lower-priority processes to have a chance to execute while ensuring that higher-priority processes still receive priority. However, if the process's priority is already at the lowest level (i.e., 1), it is returned to the ready queue without priority changes. The process flow then repeats until all the processes in the ready queue are completed.  
By combining priorities and dynamic time quantum, our algorithm aims to balance fairness and efficiency, allowing the system to prioritize critical processes while ensuring that all processes receive a fair share of CPU time. Lowering the priority of a process ensures that low-priority processes are given a fair share of the CPU and prevents high-priority processes from hogging the CPU and causing starvation. The RRAPS algorithm has several strong points, which are as follows:  
* It allocates more CPU time to high-priority processes.
* It helps prevent starvation by periodically boosting the priority of low-priority processes.
* It fairly distributes the CPU time between processes.
* It is easy to implement and requires minimal changes to the existing Round Robin scheduling algorithm.
## 5 Conclusions  
The literature review highlighted the importance of validating results in real-world scenarios and the necessity of preprocessing images before recognition. This need led to the incorporation of bio-inspired retina models to enhance the overall experimental outcomes in the license plate recognition process. This process involved several critical stages, including the detection and extraction of the region of interest, character segmentation, and alphanumeric recognition, where an increased application of CNNs was observed due to their superior performance. However, it is essential to consider that the success of these techniques heavily depended on the availability and variability of training data.  
It was expected that the bio-inspired retina model would offer an overall improvement of the images before processing them using techniques such as OCR and neural networks. This expectation was met, as the bio-inspired retina acted as a filter, accentuating specific image attributes such as contours, lighting, and motion.  
In this application, utilizing the parvocellular ganglion cell response proved appropriate, and the obtained results demonstrated a reduction in the error rate. However, it is imperative to acknowledge that instances marked by image contamination, paint anomalies, or high-contrast shadows could inadvertently accentuate irrelevant elements. Consequently, the use of this model is recommended in controlled lighting environments.  
Notably, parameter tuning significantly influenced the performance of the bio-inspired retina model. Consequently, meticulous tuning was performed in advance to achieve optimal results.  
Regarding the processing time, the OCR model had a processing time between 50 and 60 milliseconds, which increased to 55 and 63 milliseconds when incorporating the retinal model, while the CNN model had an execution time of approximately 150 milliseconds for static images, which increased to 155 milliseconds when incorporating the bio-inspired model. The SNN that operated under a pulse concept showed a processing time of around 50 milliseconds, very similar to the OCR case. While the SNN was a good candidate considering the processing time, it should be remembered that the CNN presented a better accuracy percentage.  
In summary, the results obtained effectively addressed the challenge of reducing error rates in character detection using CNN-, SNN-, and OCR-based techniques, demonstrating that the CNN model with the retinal model offered the best accuracy percentage with processing times that allowed it to operate in real time.  
Finally, it was concluded that the bio-inspired retinal model offers a wide range of potential applications for further exploration, including motion detection, pattern recognition, and dynamic range enhancement in images, among others.
# Deep Reinforcement Learning for Query-Conditioned Video Summarization
## 4 Experiments
### Ablation Analysis
#### 4.3.4 The Effect of Representativeness+Diversity Rewards  
We further compare our model to the one that does not make use of representativeness and diversity rewards (_W/O rep-div rewards_), and the results are shown in Table 3. The representativeness and diversity rewards are originally designed for the traditional generic video summarization task that does not take into account the user queries. However, it can be observed that the two rewards can also be utilized in this query-conditioned task. The performance decreases by 2.57% on average after dropping the two rewards, with decreases around 1.0%\(\sim\)2.5% for the first three videos and 4.66% for the last one. This indicates that modeling the representativeness and diversity of the summaries in addition to the relatedness reward, which establishes the relation between the videos and the query, helps to produce a minimal and concise set of video shots.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**W/O Rewards**} & \multicolumn{3}{c}{**W/O Rep+Div Rewards**} & \multicolumn{3}{c}{**Ours**} \\ \cline{2-10}  & **Pre** & **Rec** & **F** & **Pre** & **Rec** & **F** & **Pre** & **Rec** & **F** \\ \hline Video1 & 58.49 & 35.23 & 42.07 & 39.89 & 60.67 & 46.18 & 50.81 & 51.57 & **48.77** \\ Video2 & 33.88 & 59.23 & 42.38 & 37.17 & 57.83 & 44.61 & 39.99 & 58.51 & **46.65** \\ Video3 & 43.51 & 63.06 & 50.83 & 46.86 & 71.73 & 56.03 & 63.48 & 54.91 & **57.01** \\ Video4 & 24.90 & 49.82 & 32.73 & 39.91 & 31.27 & 31.71 & 39.03 & 38.37 & **36.37** \\ \hline Avg. & 40.20 & 51.84 & 42.00 & 40.96 & 55.38 & 44.63 & 48.33 & 50.84 & **47.20** \\ \hline \hline \end{tabular}
\end{table}
Table 3: We compare the proposed model to two other models, one that does not make use of the three rewards, and one that does not make use of the representativeness+diversity rewards, but that are otherwise identical. Note that we repeat the results of the combined full model in this table, in order to provide a more convenient comparison. The best results (F-measure) are highlighted in bold.
## 3 Methodology
### Principles of the PSO-LSSVM Model
#### 3.2.2 Parameter Selection of the LSSVM Using PSO  
When the kernel function of the LSSVM is RBF, there are two parameters that need to be determined by users in order to determine the efficiency and generalization performance of the LSSVM: the regularization parameter, \(\gamma\); and the kernel function width, \(\sigma\). Inspired by the social interaction model of birds foraging, PSO uses swarm intelligence to achieve the goal of optimization. A particle is used to simulate the individual birds. Particles have only two attributes: speed and position. Speed represents the speed of movement, and position represents the direction of movement. The optimal solution where each particle searches individually is called individual extremum, and the optimal individual extremum in this particle swarm is the current global optimal solution. The speed and position are updated through constant iterations. Finally, the optimal solution that satisfies the termination condition is obtained. Therefore, the PSO is chosen to search for the optimal parameters in this paper.  
The subgrade is a multidimensional dynamic system affected by multiple factors, such as rainfall and time during operation. The test section is located in the coastal area of Southeast China, with an obvious subtropical monsoon climate. Impacted by long-term rainfall and short-term rainstorms from June to August every year, the subgrade distresses easily accumulate and form mutations. However, in the months with less rainfall, the subgrade maintains a relatively stable state. Therefore, the influencing factors of rainfall and time are considered to be the keys to predicting the SPI. The implementation procedure of the overall PSO-LSSVM model is shown in Figure 5. In order to verify the prediction accuracy of the model, the Mean Square Error (MSE) is employed to obtain the fitness value, which is shown as:  
\[MSE=\frac{1}{N}\sum_{i=1}^{N}\left(y_{i}-\hat{y}_{i}\right)^{2} \tag{15}\]  
where \(y_{i}\) and \(\hat{y}_{i}\) are the actual and predicted value, respectively. \(N\) is the number of samples for training.
###### Abstract  
The vortex beam carrying Orbital Angular Momentum (OAM) has infinite orthogonal characteristic states, which theoretically can infinitely increase the communication transmission capacity, thus attracting much attention in the field of optical communication. Due to the large amount of data required for training each OAM mode, the increase in channel capacity leads to an exponential growth in the required data volume. At the same time, the phase wavefront distortion caused by atmospheric turbulence (AT) further increases the difficulty of OAM pattern recognition. This article introduces transfer learning into the field of OAM modal detection and establishes an OAM modal classifier for detecting the topological charge of distorted vortex beams. The influence of different data volumes, turbulence intensities, and propagation distances on the accuracy of OAM modal detection during the transmission of Laguerre Gaussian beams in atmospheric turbulent channels is studied, and the generalization ability of the model is analyzed. The results show that compared with traditional convolutional neural networks, the modal classifier proposed in this paper reduces the dataset size to 1/10 of the original and successfully improves the OAM detection accuracy by 15.84%. It also exhibits good generalization under unknown atmospheric turbulence strengths, providing a new approach for identifying OAM modes.  
Keywords:orbital angular momentum; atmospheric turbulence; transfer learning; oam detection +
Footnote †: journal: Applied sciences
## 8 Experimental Results
### Usefulness of Haptic Aids for AR Resizing Tasks  
In Task 3, users are proposed to resize some of the figures above (octahedron, cylinder, pyramid, cube, and sphere) with the help of a haptic reference that is of the size to be reached. From there, they have labeled the difficulty of manipulation process with a 3.57 on average (\(\sigma:\)1.04) and a 3.71 (\(\sigma:\)1.03) regarding how intuitive the process is (1 is not intuitive at all, and 5 is very intuitive). When rating whether the haptic feedback helped them for the manipulation process, there seems to be a discrepancy (2.12 on average, \(\sigma:\)1.45). The fact is that some users ignored the part of the task statement in which they were told that the system helped them to identify the right position by means of haptic feedback. Therefore, when asked if they would get rid of haptic feedback and rely only on visual AR estimations, 57.1% of the users said yes, 28.6% answered no, and the remaining 14.3% said it was not clear for them. Among some reasons in favor of haptic feedback, they stated that _\"it is easy and intuitive\"_, and although they are able to only estimate with AR clues, haptic feedback was necessary to have greater precision. One user suggested that, instead of setting the haptic feedback at the right, fixed position, it could be better to generate different haptic intensities depending on the height. That is, the closer the hand gets to the correct position, the greater the intensity of the feedback gets. Table 2 shows the offsets between the target heights in the resizing process and the final values participants produced, measured in centimeters. As can be seen in the table, participant U5 had the best results while U4 and U7 did the worst. These last two are part of the group that ignored the part of the statement about haptic feedback. Actually, a one-way ANOVA test shows that there is a statistically significant difference with respect to the accuracy achieved among the participants (\(F(8,36)=6.366\), P*), and a [PERSON] post hoc test reveals that U1, U4, and U7 conform to an homogeneous group with lower performance. The same experiment applied to the time for task completion does not show a statistically significant difference between groups (\(F(8,36)=0.656\), \(p=0.725\)). Therefore, in a relatively complex task such as estimating height based on AR, the introduction of haptic feedback has allowed most users to perform more than acceptable, with minimal errors.
## 3 Results and Discussion
### Structural Analysis Using X-ray Diffraction (XRD)  
The cellulose contained in the fiber has a crystalline zone oriented on a nano and microscale. Therefore, each fiber has a characteristic degree of crystallinity, where the percentage of crystallinity is proportional to the mechanical properties of a material, including lignocellulosic materials. In Figure 7, the characteristic diffraction patterns of the plant fibers are shown, and the crystallinity index was calculated using Equation (3), obtaining the following significantly different values.  
The bagasse fiber of the fruit (Figure 7A) had a value of 43.21 \(\pm\) 0.96% compared with the same fiber after the organosolv method (Figure 7B), with a value of 73.28 \(\pm\) 1.11%, showing greater crystallinity in response to the removal of amorphous material from the hemicellulose and lignin fraction as cellulose-coating macromolecules. On the other hand, for the case of the fiber of the leaves (Figure 7C), it had an I\({}_{\text{c}}\) of 61.13 \(\pm\) 1.81%, a value higher than that presented by the fiber in Figure 7A but less than that in Figure 7B. The index of crystallinity was higher in the fiber of the leaves than the fruit (before the organosolv process), which is congruent with a higher content of cellulose and proteins in its fiber because several of the components mentioned serve as structural support materials in the fiber.  
The X-ray diffraction spectra revealed a highly ordered solid structure after organosolv treatment (Ic between 57% and 73% for the leaves and fruit). Here, the change in the crystalline behavior for both sections of fibers (fruit and leaf) showed an increased crystallinity index in the fruit bagasse, which remained almost unchanged after organosolv treatment for the leaf, which would indicate a minor loss of minimal amorphous material in the leaf or a low content of hemicellulose or lignin. This would represent less processing in the leaves of the plant, unlike the fruit which, although the crystallinity increased, the loss of amorphous material increased.  
Figure 7D shows the leaf fiber after the organosolv process, with an Ic of 57.39 \(\pm\) 1.44%. Unlike the behavior presented with the leaf fiber, that value of the Ic was slightly lower compared with the one in Figure 7C due to its lower lignin content as a cellulose coating macromolecule in the leaf with respect to the fruit, which could cause the organosolv method to affect the cellulose structure [15; 26; 28]. The crystallinities of other plants of the Bromeliaceae family used by the resistance of their fibers, such as _Bromelia_ sp., _Ananas comosus_ var. Comosus and _Aechmaa magdalenae_ (leaf fibers), are 58.6, 50.0 and 55.0%, respectively [14], indicating that the fiber of _B. hemisphaerica_ leaves is a candidate for use as a biomaterial and that microcrystalline cellulose can be obtained for use as an excipient or future uses as a carrier for colorants and flavors in the food industry.
## 3 All-Optical Ultrasound Transducers and Imaging
### Development of Transducers and Imaging
#### 3.1.4 Multi-Modality Imaging  
By integrating AOUSI with all-optical photoacoustic imaging (AOPAI) [98; 99], complementary information originated from contrasts of acoustic impedance and light absorption can be provided. Owing to the same signal form acquired by ultrasound and photoacoustic (US/PA) imaging, acoustic waves for each modality can be captured using one ODUS detector. Thus, the advantages such as high spatial resolution and easy miniaturization existing in AOUSI can be shared with AOPAI. There are several US/PA dual-modality imaging systems developed based on an all-optical approach. (i) A scanhead consisting of an optical fiber with an axicon tip for LGUS and photoacoustic excitation for AOUSI and AOPAI, respectively, and an optical microring resonator for acoustic detection of the two imaging modes is demonstrated [100]. A single laser pulse is used to generate both US/PA signals, and thus, the information for both images is received simultaneously. (ii) The approach of simultaneous US/PA excitation using a single laser pulse is employed in another all-optical US/PA imaging setup, where an optical integrating detector is used for acquiring signals for US/PA imaging [101]. (iii) Instead of using a single laser pulse for US/PA excitation, all-optical US/PA scanhead is built using a dichroic filter to select the laser wavelengths for switching between the two imaging modes [102], where the microring resonator is used for signal detection. Figure 8 shows the mechanisms for US/PA imaging.