esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Based Super-Resolution Reconstruction and Segmentation of Photoacoustic Images
## 3 Results
### Model Evaluation Measures
#### 3.2.2 Quantitative Assessment Metrics for Image Segmentation Models  
In image segmentation experiments, the image segmentation results are compared with the ground truth using metrics such as accuracy, intersection and union ratio (IoU), boundary F1 score (BFScore), and Dice coefficient. For different categories in the segmentation results, accuracy denotes the ratio of correctly classified pixels to total pixels in that category. IoU denotes the ratio of correctly classified pixels in that category to the total number of ground-truth pixels and predicted pixels in that category. B-score denotes the extent to which the predicted boundaries match the true boundaries of each category. The Dice coefficient is used to measure the overlapping of the algorithm's predicted results with the real labels degree. It takes values between 0 and 1, where 1 indicates perfect overlap and 0 indicates no overlap. For each image, mean accuracy is the average of the accuracies of all categories in that image. Global accuracy is the ratio of correctly categorized pixels (without categories) to the total number of pixels. Mean IoU is the average IoU score of all the classes in it. Weighted IoU is the average IoU for each class, weighted by the number of pixels in that class. Mean BFScore is the average BFScore score for all classes in it. These model evaluation measures are calculated according to Equations (7)-(17).  
\[Accuracy=\frac{TP}{TP+FN} \tag{7}\]  
\[MeanAccuracy=\frac{1}{N}{\sum_{i=1}^{N}}{Accuracy_{i}} \tag{8}\]  
\[GlobalAccuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{9}\]  
\[IoU=\frac{TP}{TP+FP+FN} \tag{10}\]  
\[MeanIoU=\frac{1}{N}{\sum_{i=1}^{N}}{IoU_{i}} \tag{11}\]  
\[WeightedIoU={\sum_{i=1}^{N}}{w_{i}}\times IoU_{i} \tag{12}\]\[BFScore=\frac{2\times precision\times recall}{recall+precision} \tag{13}\]  
\[precision=\frac{TP}{TP+FP} \tag{14}\]  
\[recall=\frac{TP}{TP+FN} \tag{15}\]  
\[MeanBfscore=\frac{1}{N}{\sum_{i=1}^{N}Bfscore} \tag{16}\]  
\[Dice=\frac{2\times TP}{2\times TP+FP+FN} \tag{17}\]  
where \(TP\) denotes the number of pixels correctly categorized as positive categories, \(TN\) denotes the number of pixels correctly categorized as negative categories, \(FP\) denotes the number of pixels correctly categorized as positive categories, \(FN\) denotes the number of pixels incorrectly categorized as negative categories, \(N\) denotes the number of categories, and \(w\) denotes the pixel weight of each category.
## 3 Results and Discussion
### Random Forest  
The decision tree algorithm exhibits high variance that can correspond to less prediction accuracy. That means that if we apply a certain model built from the DT algorithm to different data sets, we will probably observe significantly different results. Instead, the prediction accuracy of a model with low variance would be consistent in any data set. To improve this issue, the method of bagging is applied to improve the DT algorithm's performance. This method makes use of the general principle that averaging different sets of data will result in lower variance. Therefore, if we train our DT model for different samples from a single training data set and average their predictions, we will achieve a model with higher prediction accuracy.  
The RF algorithm elaborates on this principle and by decorrelating the trees to achieve an even higher performance. The way to do so is by selecting only a random subset of predictors in each node split (approximately equal in value to the square root of the total number of the predictors), thus permitting less important predictors to play a role in the model construction. That being said, running a RF model by using the whole set of predictors at each split, will result in a simple bagged decision tree.  
The results of running a RF algorithm in our data set, improved compared to the single decision tree method. A coefficient of determination, R\({}^{2}\), for the model evaluation of 0.95 is achieved and is indicative that this occurs with not so many different trees, as shown in Figure 8. Approximately, 20 different trees are sufficient to construct a model with the highest possible accuracy. Therefore, considering the lower complexity of a RF model with less trees and since more trees do not provide better results, we selected a value of 20 for our model. It is also important to notice that training and testing R\({}^{2}\) do not coincide for any number of trees; instead, there is a constant difference of 0.05. On the other hand, the RMSE begins with a value of 4.5 for two trees and rapidly reduces to less than 3 for the rest of the number of trees. A significant advantage of the RF algorithm is that by adding more trees we do not risk overfitting [29].  
Figure 8: Performance metrics plot for random forest algorithm against different number of trees.  
Figure 7: Performance metrics plot for decision tree algorithm against different number of neighbors.
# Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors
## 4 Discussion
### Spatial Resolution  
UAVs provide the greatest spatial coverage of any of the near-earth sensors we surveyed. The UAV imagery we examined had a spatial resolution of 0.03 m and a total spatial extent of \(\sim\)105 ha--which we clipped to 5 ha for analysis. The higher resolution and greater spatial extent of UAV imagery allows for greater consideration of spatial heterogeneity in the system over either below-canopy imagery or PhenoCams. PhenoCams are fixed and take the same image repeatedly, but the spatial extent of that image is determined by the height of the camera, its azimuth, and in part, the system that it is targeting--cameras mounted on towers above forests tend to have greater spatial coverage than do cameras that are focused on prairies, grasslands, or similar low-stature ecosystems [19]. Extending coverage would require additional cameras and additional infrastructure at a site. The number of individual images that an operator can acquire is limited by cost, travel time, safety, terrain, and weather. We have focused on using derived values from the _phenocamr_ package to estimate rates and amounts of green-up in this analysis as this is a fairly plug-and-play, off-the-shelf approach with high-quality output that can be employed by many researchers, even with limited remote sensing experience. However, additional means of analysis (see _PhenoPix_ package in R [52]) can be used to analyze images for within scene variance that can increase the utility of these data that may alleviate some of these concerns.
# A Scalable and Trust-Value-Based Consensus Algorithm for Internet of Vehicles
## 4 C-PBFT Consensus Algorithm
### Reputation Calculation Using Multi-Weighted Subjective Logic Model
#### 4.2.2 Multi-Weight Local Opinions of Subjective Logic
Assuming that the distance between the location where the event is generated and the location where the message is sent is 0-\(L\), we believe that the information is credible in terms of distance factors. The factors influencing distance are shown as follows: \[\left\{ \begin{array}{l}DF_{i\to j}=1,\Delta L\leq L\\ DF_{i\to j}=\frac{L}{2\sqrt{\left|X_{i}-X\right|^{2}+\left|Y_{i}-Y\right|^{2}} },\Delta L>L\end{array} \right.\] (8) where \(\Delta L=\sqrt{\left|X_{i}-X\right|^{2}+\left|Y_{i}-Y\right|^{2}}\), \(\quad(X_{i},Y_{i})\) and \((X,Y)\) are the location coordinates of the message generated by vehicle Vi and the geographic location coordinates of the message generation, respectively. Therefore, the overall credibility value weight of the local opinion is \(\delta_{i\to j}=\rho_{1}*IF_{i\to j}+\rho_{2}*DF_{i\to j}\), where \(\rho_{1}+\rho_{2}=1,0<\rho_{1}\leq 1,0<\rho_{2}\leq 1\).
# ITOC: An Improved Trie-Based Algorithm for Online Packet Classification
## 4 Experimental Results  
### Simulation Setup  
The popular way to get test datasets is using the tool called ClassBench [22]. We use a work based on it called ClassBench-ng [23] because the old ClassBench is too old and no longer supported now. We use ClassBench-ng to generate 5-tuple rulesets to test our method. ClassBench-ng also provides three types of seed files: access control lists (ACL), firewalls (FW), and IP-chains (IPC)). So we now have three types of rulesets with different properties. As the size of the ruleset is also important, we generated rulesets in three different sizes to show the influence of rule numbers.  
The simulation program runs on a Linux platform with Intel Xeon Silver 4208 CPU@ 2.10 GHz and 64 GB of RAM. The operating system is CentOS release 7.9.2009. Our program works with DPDK, gets rulesets from configuration files, and reads data files to simulate network packet input.  
As ITOC is an online packet classification algorithm improved from DPDK ACL, we conducted these experiments in the simulation environment. We first designed an experiment to find the best threshold set for ITOC. Then another experiment was finished to verify the effectiveness of the optimization in ITOC. Afterward, we provided the lookup performance comparison of ITOC and some recent algorithmic approaches. Finally, we compared the performance of ITOC with DPDK ACL library, especially the overall performance in online work scenes.
## 3 Simulation Results and Discussions
### LEF and SEF with OFB \(K_{ex}\) at HB Point  
The OFB strength \(K_{ex}\) at the HB point versus the LEF and SEF is shown in Figure 2a,b based on the results shown in Figure 1. In Figure 2a, the LEF changes as a nonlinear function (LEF exponential decay function) with the OFB \(K_{ex}\) when the SEF \(C=10^{-4}\), \(10^{-5}\), and \(10^{-6}\). Then, the OFB intensity \(K_{ex}\) can be increased by reducing the LEF values, which is desired to set the LD into a stable dynamic, such as PD or PO. We notice that the effect of the LEF is noticeable when the SEF \(C=10^{-4}\) and decreases by decreasing the SEF. Figure 2b shows the SEF with OFB \(K_{ex}\) at many values of LEF \(\alpha=1.0\), \(3.0\), and \(5.0\), and a nonlinear behavior representing an exponential function of the SEF with OFB intensity \(K_{ex}\) is found. Then, by enhancing the OFB \(K_{ex}\), the SEF increases, which leads to a PO dynamic. The SEFhas a strong effect when the LEF \(\alpha=1.0\) and decreases by increasing the LEF. In Ref. [20], the change in the LEF with the phase due to OFB has been examined numerically and experimentally. We have included these characteristics in our proposal to enhance the predicted results shown in Figures 1 and 2a with respect to the LEF and OFB intensity \(K_{ex}\) and establish new calculated results on the SEF and OFB intensity \(K_{ex}\), of the LD with short EC shown in Figures 1 and 2b. Figure 1 demonstrates that decreasing/increasing the LEF/SEF takes the LD into a more stable dynamic by enhancing the OFB \(K_{ex}\). As shown in the figures, decreases/increases in the LEF/SEF led to an increase/decrease in \(K_{ex}\) with the HB point, which led to an increase in the threshold current. The calculated results in Figure 1 show that the LD with short EC dynamic state instability can be improved by decreasing/increasing the LEF/SEF and enhancing \(K_{ex}\), which, overall, helps in drawing LDs with better dynamics. The impacts of the LEF and SEF with OFB with selecting the LD design to adjust the values of the LEF and SEF are introduced for the first time here, which increases the stability of LDs with short EC.
# CERRT: A Mobile Robot Path Planning Algorithm Based on RRT in Complex Environments
## 4 Simulation and Experiment
### Evaluation of Algorithms in Real Environment  
In order to evaluate the performance of the algorithm, we have chosen an actual map scenario, which is Tianjin Central Square with geographical coordinates of 117\({}^{\circ}\)04\({}^{\prime}\)56.88\({}^{\prime\prime}\) E, 39\({}^{\circ}\)05\({}^{\prime}\)49.19\({}^{\prime\prime}\) N, as shown in Figure 15a. The map covers an area of 175 m \(\times\) 175 m and is divided into a grid of 1000 px \(\times\) 1000 px, where each pixel represents an actual area of 0.03 m \(\times\) 0.03 m. Figure 15b depicts the map generated based on the real scene, where black represents the obstacle areas and white represents the free space.  
The coordinates of the starting point are marked with a red circle at [100, 480], and the coordinates of the target point are marked with a green circle at [870, 240]. We will  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Path Cost** & **POS Cost** & **BSOS Cost** & \begin{tabular}{c} **Path** \\ **Smoothness** \\ \end{tabular} & \
\begin{tabular}{c} **POS** \\ **Smoothness** \\ \end{tabular} &
\begin{tabular}{c} **BSOS** \\ **Smoothness** \\ \end{tabular} \\ \hline RRT & 1723.38 & 1386.87 & 1289.38 & 32.75 & 4.48 & 4.15 \\ RRV & 1618.49 & 1383.94 & 1281.70 & 13.58 & 4.78 & 4.34 \\ Fast-RRT & 1682.35 & 1375.54 & 1285.46 & 29.74 & 4.36 & 4.21 \\ CERRT & 1619.61 & 1323.14 & 1283.38 & 55.53 & 4.08 & 4.05 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results for Optimization in the Bug Trap Environment.  
Figure 14: Pruning and bidirectional shrinking optimization in the Bug Trap environment. The blue lines represent the original paths, the green lines represent the pruned paths, and the red lines represent the paths optimized by bidirectional shrinking.  
Figure 15: Actual map environment.  
perform path planning tasks 100 times for a mobile robot in this scenario and evaluate the algorithm's performance by taking the average.
## 4 MAC Protocol Design  
### Requirements Analysis  
The ISWN is expected to complete both on-board sensing and subsystem data transmission, thus the data demands should be considered seriously. According to the data service requirements, the on-board data can be classified into three categories, as shown in Table 2.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameters** & **Value** & **Units** \\ \hline
**Micro Processor** & & \\ Program memory & 64 & KB \\ RAM & 4352 & Byte \\ Active Power & 27 & mW \\ Sleep Power & 27 & \(\mu\)W \\ Storage Size & 64 & KB \\ \hline
**RF transceiver** & & \\ Data rate & 500 & Kbps \\ Receive power & 40 & mW \\ Transmit power at 0 dBm & 68 & mW \\ \hline
**ISWN Node** & & \\ Total Active power & 143 & mW \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the wireless nodes.  
Figure 3: Structure of the wireless nodes.  
Let \(N_{A}\), \(N_{B}\) and \(N_{C}\) be the number of nodes in each category. The first two kinds of nodes are expected to have high data rate or strict packet delay requirements. Thus, guaranteed slots with a center controlled scheduling scheme are more suitable. In contrast, the last kind of nodes have loose requirements both in time delay and data rates, which means that they are tolerant to contention access. To summarize, the requirements are as follows:  
* [noitemsep,topsep=0 pt]
* Enough data rate for both payloads and subsystems;
* Delay performance guarantee for the time critical subsystems, such as AOC subsystem;
* Simple structure and support rapid development;
* Robustness and fault tolerance;
* Energy efficiency, since the available on-board power resources are limited.
## 4 Study of the TE Noise Reduction with Serrated Treatment Using Linear Microphone Array
### Results and Discussions
Figure 13: Spectra of the TE noise with different sawtooth amplitudes (\(\lambda\) = 3 mm). Figure 14 shows the 1/3 octave spectra of the TE noise for the blades with different sawtooth wavelengths and with an amplitude of 30 mm. The inflow speeds are, respectively, 20 m/s, 40 m/s, 60 m/s and 70 m/s. It can be seen from Figure 14 that, at 20 m/s, the H30\(\lambda\)1.5 configuration has a large noise reduction in the frequency band of 1250-10,000 Hz, and the noise can be reduced by 3-4 dB. When the airflow velocity is 40 m/s, the TE serrations can considerably mitigate the TE noise below 4000 Hz. However, when the frequency is above 4000 Hz, the noise reduction effect is obviously weakened, and at some frequencies, the TE noise can be enlarged. With the increase in airflow velocity up to 60 m/s and 70 m/s, the noise reduction difference between different sawtooth treatments increases. The serration with the smallest wavelength H30\(\lambda\)1.5 has the smallest noise reduction, and the serration H30\(\lambda\)10 has the largest noise reduction. The maximum noise reduction effect can be up to 15 dB. Similar to the results in Figure 1, the serrations with various wavelengths can effectively reduce the low-frequency noise. However, the serrations have a negligible
## 3 Design and Implementation  
### Data Collection  
The data used for this study included responses from five PG companies, 21 banks (including commercial and local banks) and five securities companies that offered payment methods such as card payments, mobile phone payments and bank transfers. The PG data included 620 cases from \"NicePay\", 124 from \"SettleBank\", 977 from \"Fiserve\", 435 from \"KSNET\", 1119 from \"Toss\" and 245 from \"Eximbay\", and 8336 cases from 26 banks and securities companies, for a total of 11856 cases used.  
To label the data, we used KonNLPy's OKT (Open Korean Text) morphological analyser to extract nouns, letters, and adjectives only, and used them as keywords to directly label 1000 messages for training. These 1000 labelled messages were used as training data with an LSTM classifier model was built based on the defined labels, and the labelled messages were used to label the rest of the messages. In spite of the above process, there was still unlabelled information; therefore, in order to label this information, the newly labelled information was added to the existing labelled finished information, using the newly labelled information as the learning data and the unlabelled data as the test data re-labelled using the LSTM classifier. This process was repeated, and finally the labelling of 11,865 pieces of information data was completed. The pre-processed information is shown in Figure 1.  
These works are based on enterprise perspective considerations to prevent user fraud to reduce enterprise losses, but our proposed model is based on both enterprise and user considerations to detect the specific reasons for classification payment errors to reduce losses for both parties.
# A Systematic Review on Low-Level Laser Therapy in the Management of Shoulder Impingement Syndrome
## 3 Results
### Evaluation of Outcomes  
The rating scales used by the studies in this review were:  
* Visual analogue scale (VAS), a common response option used to measure pain; it generally includes a 100 mm line with anchor words at each end [18];
* Shoulder Pain and Disability Index (SPADI), an index that measures shoulder pain and disability in an ambulatory setting. It includes 13 items divided into two subscales: a five-item domain that measures pain and an 8 eight item domain that measures disability [19];
* Constant scoring system, in which 35 points are given for subjective assessments of pain and ADL (activities of daily living), and 65 points are allocated for objective range of motion and shoulder strength measures [20];
* Disabilities of Arm, Shoulder, and Hand Questionnaire (DASH), a 30-item scale that addresses the difficulty in performing various physical activities that require upper extremity function: 21 items for physical function, five items for pain symptoms, and four items for emotional and social function [21];
* UCLA SCORE, a measure that provides a score based on five different domains that are pain, function, active forward flexion, strength of forward flexion, and general satisfaction. It has one item for each domain. Pain accounts for 10 points, function for 10 points, forward flexion for 5 points, strength for 5 points, and general satisfaction for 5 points; the total score is 35 points [11].
## 3 Results
### Retention of EO Content in CE EO and NLC EO During Storage at 4 \({}^{\circ}\)C
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Kinetic Model** & **Parameter** & **Eucalypttol** & **Linalool** & **Camphor** & **Linalyl Acetate** & **EO** \\ \hline \hline \multirow{4}{*}{Zero order} & R\({}^{2}\) & 0.937 & 0.960 & 0.596 & 0.948 & 0.930 \\  & K\({}_{0}\) & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 \\  & R\({}^{2}\) & 0.021 & 0.051 & 0.067 & 0.026 & 0.027 \\  & K\({}_{1}\) & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 \\ Higuchi & R\({}^{2}\) & 1.000 & 0.972 & 0.807 & 0.781 & 0.991 \\  & K\({}_{\rm{H}}\) & 0.274 & 0.291 & 0.274 & 0.242 & 0.281 \\  & R\({}^{2}\) & 0.679 & 0.669 & 0.595 & 0.729 & 0.671 \\  & K\({}_{\rm{HC}}\) & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c} \hline \hline
**Kinetic Model** & **Parameter** & **Eucalypttol** & **Linalool** & **Camphor** & **Linalyl Acetate** & **EO** \\ \hline \hline \multirow{4}{*}{Zero order} & R\({}^{2}\) & 0.937 & 0.960 & 0.596 & 0.948 & 0.930 \\  & K\({}_{0}\) & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 \\ First order & R\({}^{2}\) & 0.021 & 0.051 & 0.067 & 0.026 & 0.027 \\  & K\({}_{1}\) & 0.000 & 0.000 & 0.000 & 0.000 \\ Higuchi & R\({}^{2}\) & 1.000 & 0.972 & 0.807 & 0.781 & 0.991 \\  & K\({}_{\rm{H}}\) & 0.274 & 0.291 & 0.274 & 0.242 & 0.281 \\  & R\({}^{2}\) & 0.679 & 0.669 & 0.595 & 0.729 & 0.671 \\  & K\({}_{\rm{HC}}\) & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters of EO release kinetics obtained from model fitting of the in vitro release data for the CE EO and NLC EO. Figure 2: Monitoring of the content (**a**) and the reduction percentage (**b**) of eucalypttol (**III**), linalool (**III**), camphor (**III**),linalyl acetate (**III**), and total EO in the CE EO (gray line) and NLC EO (blue line) for 90 days. Considering the EO as one substance, we analyzed its release mechanism from the CE EO and NLC EO. As summarized in Table 3, in the case of the CE EO, the best fit was achieved by the Higuchi model (R\({}^{2}\) = 0.991), and the zero-order model followed (R\({}^{2}\): 0.930). For NLC EO, the results were reversed, as the best fit was achieved by the zero-order model (R\({}^{2}\): 0.994) followed by Higuchi (R\({}^{2}\): 0.945).
## 2 Proposed System
### Sensors and Embedded System Hardware  
#### 2.2.1 Air Temperature and Humidity Sensor  
Air temperature and humidity are the typical air parameters to be monitored. This work uses the DHT22 to measure air temperature and humidity. It is similar to the DHT11 but has superior accuracy. In the DHT22, the temperature is measured by a thermistor, and a capacitive humidity sensor measures the humidity. This sensor compensates for temperature and is calibrated. The DHT22 specifications are given in Table 3[53].