esa-sceva/satcom-chunk-collection huggingface dataset:
# Metrics Related to Confusion Matrix as Tools for Conformity Assessment Decisions
## 3 Results and Discussion
### Guard Banding by Confusion Matrix
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**w/mm**} & **Model** & \multicolumn{1}{c}{**Metrics**} & **r** & **Risks** & \multicolumn{1}{c}{**[\(\boldsymbol{A_{L}A_{U}}\)]/mm**} \\ \hline \multirow{2}{*}{\(-0.001475\)} & In* & \multirow{2}{*}{\(precision=recall=F1=0.9647\)\(kappa_{Im}=MCC=0.662825\)} & \multirow{2}{*}{\(-0.59\)} & \(R_{C}=3.16\%\) & \multirow{2}{*}{\([99.976525,\ 100.023475]\)} \\  & Rp\_min & & & \(kappa=MCC=0.731748\) & & \\ \hline \multirow{2}{*}{\(-0.001548\)} & In & \multirow{2}{*}{\(precision=recall=F1=0.991\)\(kappa_{Im}=MCC=0.731748\)} & \multirow{2}{*}{\(-0.18596\)} & \(R_{C}=0.086\%\) & \multirow{2}{*}{\([99.97754,\ 100.0225]\)} \\  & Rp\_min & & & \(kappa=MCC=0.731748\) & & \\ \hline \multirow{2}{*}{\(-0.001548\)} & In, Im & \multirow{2}{*}{\(kappa_{Im}=kappa_{Im}=0.6625\)} & \multirow{2}{*}{\(-0.6192\)} & \(R_{C}=3.21\%\) & \multirow{2}{*}{\([99.976452,\ 100.023548]\)} \\  & Rp\_min & & & & \(R_{P}=3.09\%\) & \\ \hline \multirow{2}{*}{\(0.001003\)} & In, Im & \multirow{2}{*}{\(kappa_{Im}=kappa_{Im}=0.6355\)} & \multirow{2}{*}{\(0.4012\)} & \(R_{C}=1.84\%\) & \multirow{2}{*}{\([99.979,\ 100.021]\)} \\  & Rc\_min & & & & \\ \hline \multirow{2}{*}{\(-0.001808\)} & In, Im & \multirow{2}{*}{\(MCC_{In}=MCC_{Im}=0.6614\)} & \multirow{2}{*}{\(-0.7232\)} & \(R_{C}=3.36\%\) & \multirow{2}{*}{\([99.97619,\ 100.0238]\)} \\  & Rp\_min & & & & \(R_{P}=2.85\%\) & \\ \hline \multirow{2}{*}{\(0.001282\)} & In, Im & \multirow{2}{*}{\(MCC_{In}=MCC_{Im}=0.6444\)} & \multirow{2}{*}{\(0.5128\)} & \(R_{C}=0.019\%\) & \multirow{2}{*}{\([99.97928,\ 100.0207]\)} \\  & Rc\_min & & & & \\ \hline \end{tabular}  
* In—initial model, Im—improved model, Rp_min—producer’s risk minimization, Rc_min—consumer’s risk minimization.  
\end{table} 
Table 2: Suggested values of the guard band and associated parameters. In addition, the length of the guard band determined using the new method, based on the intersection points of the MCC curves of the initial and improved models, is smaller than the length of the guard band determined using the standard method based on expanded uncertainty. According to the standard method, it follows from the formula \(w=2u_{0}\) that the length of the guard band for the initial model is \(w=0.022\) mm, and for the improved model \(w=0.0132\) mm. All values in Table 2 are obtained numerically, although originally, measurements can be made up to the order of magnitude \(10^{-7}\) mm, i.e., in \(0.1\) um. The values in Table 2, as well as in the text, are intentionally not rounded to make it easier to see the difference between the reference parameters that describe the models.
## 4 Results and Discussion
### Characterization of UWSN Channel Model  
Considering the need for a computationally efficient model for UWSN, at first, a robust channel model was developed with intent to consider the small scale fading and deviation as well as the large-scale deviations caused due to dynamic network conditions. The inter-path correlation was estimated in both the time as well as frequency domain. Before implementing the proposed model for the SRLNC based UWSN routing protocol design, the derived channel model was assessed for its effectiveness for small- as well as large-scale fading conditions. Since we intended to enable the proposed routing model with multipath communication, the inter-path correlation was investigated, where it exhibited satisfactory outputs among different paths. Here, we selected the operating frequency range as 13 kHz, and the distance between transmitter and receiver was maintained at 0.2-1 KM. For simulation, the depth of the water column was fixed at 10 m and the transmitter and receiver heights from the bottom surface were assigned at 4 and (approx.) 2 m, respectively. For data transmission as performed for routing, the carrier frequency was fixed at 13 kHz where the transmission rate was maintained at 6.5 kb/s. For channel assessment, the pseudo-noise sequence was used and repeated iteratively and BPSK modulated onto the centre frequency. Before processing for performance analysis, the signals were re-sampled based on the retrieved data packet length so as to compensate for the motion-caused frequency shifting and time scaling. We used a Doppler factor to compensate for the aforementioned variations and, due to the sensor movement, it formed a Doppler rate in the order of \(10^{-3}\) and, hence, required data re-sampling. We applied a fine Doppler compensation using a recursive least square model and 2 nd order phase-locked loop. To estimate the underwater acoustic channel, a generic least square (LS) based algorithm orthogonal matching pursuit (OMP) was applied. It performs better for sparse channel estimation [53]. Based on the channel response, the path gains were estimated. Figure 2 presents the time advancement of the magnitude baseband impulse response for the developed channel model.  
In this experiment, numerous local minima and maxima were observed over a delay that characterizes the channel taps where the impulse repost is the strongest. As depicted in Figure 3, the path delays allied with the channel geometry are labelled as P\({}_{0}\), P\({}_{b}\) and P\({}_{s}\). It signifies the reflections from the bottom and the surface. Here, the minor deviations in path delays can be observed from the nominal ones due to change in node locations, motion caused Doppler effect and, without a doubt, the delay spreading across intra-paths.  
Now, before implementing proposed network coding based UWSN routing protocol, the effectiveness of the SRLNC scheme is examined in terms of throughput, data drop, etc. The following section presents the result obtained for SRLNC.
# Computer-Aided Detection of Hypertensive Retinopathy Using Depth-Wise Separable CNN
## 3 Materials and Methods  
### Data Acquisition  
In the interest of developing the CAD-HR diagnosis system, we must build a dataset with a sensible number of retinography images. Thus, we prepared 1310 HR retinography photos and 2270 regular retinography photos to test and compare the proposed CAD-HR diagnosis methodology. These retinography photos were obtained from two distinct web and one private source. An experienced ophthalmologist was involved in the development of the training dataset (manually distinguishing the HR and regular fundus photos from different datasets). To produce a gold standard, the medical professional inspected all the HR associated characteristics in a set of 3580 fundus photos, as shown in Figure 1. The explanation of the three separate datasets (used to prepare our training and testing fundus set) with varied lighting settings and dimensions exists in Table 2. All those images were re-sized to (700 \(\times\) 600) pixels to conduct experiments.  
In addition, the proposed CAD-HR system is tested on and trained with data obtained from a private hospital known as Imam-HR. The Imam-HR dataset comprised a total number of 3170 retinal samples where 1130 images were from HR patients and 2040 represented non-HR. The resolution of all images was 1125 \(\times\) 1264 and they were saved in JPEG format. These pictures were obtained as part of a standard procedure for diagnosing patients with hypertension. These images were all downsized to standard resolution (700 \(\times\) 600) using data from three datasets. In addition, a professional ophthalmologist is involved in the process of preparing HR and non-HR datasets for ground truth evaluation. A sample of a fundus image from the datasets used in this study is shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Title 1** & **Name** & \({}^{\mathbf{1}}\) **HR** & \({}^{\mathbf{2}}\) **Non-HR** & **Size** & **Fundus** \\ \hline
[64] & DRIVE & 100 & 150 & (768 \(\times\) 584) pixels & 250 \\
[65] & DiaRetDB0 & 80 & 80 & (1152 \(\times\) 1500) pixels & 160 \\
[PRV] & Imam-HR & 1130 & 2040 & (1125 \(\times\) 1264) pixels & 3170 \\  & & **1310** & **2270** & Downsized: (700 \(\times\) 600) pixels & **3580** \\ \hline \hline \end{tabular} \({}^{\mathbf{1}}\) Hypertensive retinopathy (HR), \({}^{\mathbf{2}}\) Non-hypertensive retinopathy (non-HR).  
\end{table}
Table 2: Retinal fundus datasets used with the CAD-HR-diagnosis system.
# Mobile Charging Scheduling Approach for Wireless
## 6 Discussion
### Comparison of Network Lifetime  
The above experiments discuss the impact of different parameter changes on charging performance based on the preset lifetime. This section compares the lifetime distribution of six mobile charging approaches based on different scenarios, including JSSTC50, JSSTC100, and JSSTC200. To compare the impact of different approaches to prolonging the lifetime,  
Figure 10: Impact of the MC moving speed on the (a) average tour length (b) average number of failed sensors.  
Figure 9: Impact of the MC charging speed on the (a) average tour length (b) average number of failed sensors.  
we also add the lifetime distribution of the network where the sensors are not charged. In particular, each approach has been run 50 times, and the results are shown in Figure 11.  
Obviously, the approaches based on reinforcement learning framework can obtain longer lifetime than those heuristic approaches. Among the heuristic approaches, the lifetime of the greedy approach is always the shortest. The lifetime of the proposed  
Figure 11: Comparison of network lifetime (**a**) JSSTC50, (**b**) JSSTC 100, and (**c**) JSSTC 200.  
approach is always the longest in different scenarios, and it becomes more outstanding as the number of sensors increases. This is because the proposed approach can adaptively adjust the charging upper threshold of the sensor. Furthermore, introducing Bi-GRU can also train the agent based on the historical and future state information to determine charging actions that prolong the network lifetime.
## 1 Introduction
[40] utilized stacked ensemble approaches to construct four ensemble models, achieving notable improvement in prediction performance. [PERSON] et al. [41] used a decision tree algorithm based on two different construction methods: (1) using a DT model for each rock type (DT-RT) and (2) developing a single DT model for all rock types (Unique-DT) to predict rockbursts. [PERSON] et al. [42] assessed the efficacy of six machine learning methods in predicting rockburst by evaluating the ejection velocity and kinetic energy of failed rocks. [PERSON] et al. [43] improved sixty BP models to predict rockburst based on the standard gradient descent algorithm and four optimization algorithms (momentum gradient descent algorithm, quasi-Newton algorithm, conjugate gradient algorithm, and Levenberg-Marquardt algorithm) and four index dimensionless methods (unified extreme value processing method, differentiated extreme value processing method, data averaging processing method, and normalized processing method). [PERSON] et al. [44] constructed the NGO-CNN-BiGRU-Attention model and applied it to predict the real rockburst data from Daxiangling Tunnel. Although there is extensive research and comparative analyses of rockburst prediction models, the uncertainty and complexity of rockburst are still not effectively tackled. It remains a significant challenge to develop an intelligent prediction model with higher accuracy and reliability [45]. Ensemble learning models have been widely used to solve a variety of real-world engineering problems due to their excellent performance. As an important strategy for ensemble learning, boosting techniques can combine multiple weak learners into one strong learner, improving the performance of the model significantly. Hence, two prominent boosting techniques, AdaBoost and XGBoost, were employed for rockburst prediction. Moreover, compared to conventional gradient descent algorithms, the particle swarm optimization (PSO) algorithm demonstrates higher global search capabilitiesand can quickly locate the best configuration of the backpropagation neural network (BPNN). Therefore, the PSO technique was employed to optimize the weights and biases of the BPNN. In this study, the PSO-optimized BPNN was integrated with AdaBoost and XGBoost techniques for rockburst prediction. First, a rockburst dataset was constructed by extensively compiling rockburst cases. After feature analysis and outliers identification, the SMOTE algorithm was used to balance the dataset. Subsequently, the hybrid PSO-BPNN-AdaBoost and PSO-BPNN-XGBoost models were developed for rockburst prediction. To verify the validity and generalization of these two models, the rockburst hazard in Zhongnanshan Tunnel, Daxiangling Tunnel, and Cangling Tunnel was evaluated. Finally, the reliability and superiority of the proposed models were further demonstrated by comparing them with other machine learning models.
## 5 Experiments and Results
### Quality of the Rebalancing Operations
#### 5.5.4 Effect of Number of Trucks \(N_{t}\)  
In our analyses we imposed the constraint that a single truck is used to rebalance a single neighbourhood, i.e., to apply a single rebalancing operation. Figures 11 and 12 show the performances obtained for 3 different values of \(N_{t}\).  
Figure 10: Number of fixed stations per bike movement with varying _minSupport_.  
Figure 9: Number of fixed stations with varying _minSupport_.  
As the value of \(N_{t}\) changes, the average value of fixed stations per movement remain almost stable. This means that the top \(N_{t}\) rules used to plan the \(N_{t}\) rebalancing operations assigned to the \(N_{t}\) trucks (one rebalancing operation per truck) are characterized by a similar rebalancing quality.  
In this case, the trend of the total number of fixed critical stations is dependent on the data partitioning strategy. Those strategies that have worse performances in terms of total number of fixed stations when five trucks are considered are the ones that are more positively affected by the increase of the number of trucks and planned rebalancing operations. To understand this trend, we analysed the top \(N_{t}\) rules in the ranking for each partitioning strategy and we noticed that some strategies (e.g., the per timeslot partitioning one) have a higher number of complementary association rules among the top rules related to different set of nearby stations. This means that the selected top \(N_{t}\) rules are related to rebalancing operations that impact on different neighbourhoods, increasing the overall number of fixed critical stations. Conversely, the top \(N_{t}\) rules in the ranking when the per day of the week or the per day of the week and timeslot strategies are applied are significantly overlapped and many of them are related to subsets of the same neighbourhood. It implies that similar redundant rebalancing operations should be simultaneously assigned to more trucks, reducing the overall benefit of increasing the number of trucks. This result suggests that an analysis of the overlaps among rules could be beneficial to further improve the quality of our methodology.  
Figure 11: Number of fixed stations with varying \(N_{t}\).  
Figure 12: Number of fixed stations per bike movement with varying \(N_{t}\).  
Given the small difference in terms of absolute performance (from 225 to 228), we chose to keep \(N_{t}=5\) in the reference configuration to limit the number of trucks used and hence the costs.
# New and Efficient Algorithms for Producing Frequent Itemsets with the Map-Reduce Framework
## 4 Incremental Frequent Itemset Mining with Map-Reduce  
### Problem Definition  
Let \(\mathcal{D}\) be a database of transactions, \(I\) a set of items and \(minSup\) the minimum support level as described in Section 3.1. Define \(FI\) to be a set of all frequent items in \(\mathcal{D}\): \(FI(\mathcal{D},minSup)=\left\{x|sup_{\mathcal{D}}(x)\geq minSup*\mathcal{D}\right\}\). Let \(PK\) be some previous knowledge that we produced during the FIM process of the \(\mathcal{D}\). Let \(\Delta\) be the set of additional transactions. Let \(\mathcal{D}^{+}\) be a new database defined as \(\mathcal{D}^{+}=\mathcal{D}\cup\Delta\). The problem is to find \(FI(\mathcal{D}^{+},minSup)\), the set of all frequent itemsets in the updated database. We denote \(FI(\Delta)\)_deltaFI_. We may omit \(minSup\) from functions if the support level is clear from the context. We call any \(x\) s.t. \(x\in FI\) a frequent itemset or just \"frequent\".
## 3 Background
### Multi-Level Feedback Queue  
The _multi-level feedback queue_ (_MLFQ_) scheduling algorithm explanation is based on the description in book [3]. MLFQ is a dynamic priority-based process scheduling technique employed in some operating systems. MLFQ dynamically assigns processes to various priority queues based on their recent behavior, aiming to optimize _response time_ and _turnaround time_. This algorithm initiates processes in the highest-priority queue and transitions to lower-priority queues based on CPU usage patterns. MLFQ will learn about the processes and use their past behavior to predict future patterns. Based on this, the priority of the processes will be changed. The algorithm ensures fairness by continually adjusting process priorities in response to their behavior. However, significant drawbacks are identified. One major concern is the potential for interactive jobs to monopolize CPU time, leaving long-running jobs starved for processing resources. Additionally, the algorithm is susceptible to manipulation, allowing clever users to trick the scheduler and gain an unfair CPU share through strategic use of I/O operations. Another limitation is the algorithm's inability to adapt to program behavior changes over time, overlooking transitions from CPU-bound to interactive phases. MLFQ settings are carefully adjusted to overcome these obstacles, such as queue quantity, time slice sizes, and priority boosting frequency.  
MLFQ adheres to the rules outlined in the bullet points below [3]. Some rules have been adapted to enhance compatibility within a Petri net model.  
* **Rule 1:** select the job with the highest priority.
* **Rule 2:** in the event of two jobs sharing the same highest priority, execute them round robin.
* **Rule 3:** assign the highest priority to jobs entering the queue.
* **Rule 4:** if the allocated time for a job elapses, decrease the job's priority by one.
* **Rule 5:** if jobs have been in the queue for a specified period without running, elevate their priority to the highest.
## 4 Results  
### Clustering Results  
For the various types of feature sequences obtained from feature engineering, we used the PSO-optimized k-means algorithm to cluster each feature. The number of cluster centers for each type of feature was set to 3, thereby clustering the feature into low, medium, and high clusters. The clustering results of all features are shown in Figure 5, and the interval ranges and sample sizes of different clusters are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(q^{3h}\) & \(q^{6h}\) & \(q^{12h}\) & \(q^{24h}\) & \(q^{3d}\) & \(q^{7d}\) \\ \hline \(v_{DB01}\) & 0.970735 & 0.971045 & 0.971962 & 0.973857 & 0.978478 & 0.979868 \\ \(v_{DB02}\) & 0.964633 & 0.964742 & 0.96582 & 0.968061 & 0.973537 & 0.975926 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Gray relation degree between rainfall characteristics and displacement characteristics.  
Figure 5: Visualization of all feature clustering results. **(a)** The velocity of DB01. **(b)** The velocity of DB02. **(c)** 3-h cumulative rainfall. **(d)** 6-h cumulative rainfall. **(e)** 12-h cumulative rainfall. **(f)** 24-h cumulative rainfall. **(g)** 3-day cumulative rainfall. **(h)** 7-day cumulative rainfall.  
Combining Figure 5 and Table 2, it can be seen that the number of samples in different clusters differs by an order of magnitude. The number of samples of low-rank clusters is much higher than that of middle-rank and high-rank clusters, and the number of samples of middle-rank clusters is also much higher than that of high-rank clusters. Combining Figure 5 and Table 2, it can be seen that the number of samples in different clusters differs by an order of magnitude. The number of samples of low-rank clusters is much higher than that of middle-rank and high-rank clusters, and the number of samples of middle-rank clusters is also much higher than that of high-rank clusters. Taking \(v_{DB01}\) as an example, the speed of samples in the DB01-Low-Velocity cluster is between \(-3.64\) and \(4.70\), which has a total of \(4887\) samples. The speed of samples in the DB01-Medium-Velocity cluster is between \(4.78\) and \(15.54\) with a total of \(253\) samples, which is an order of magnitude less than the DB01-Low-Velocity cluster. The speed of samples in the DB01-High-Velocity cluster is between \(16.49\) and \(60.96\), and the number of samples is only \(56\), which is an order of magnitude less than the DB01-Medium-Velocity cluster. The clustering results of other features have similar characteristics to \(v_{DB01}\), differing only in the range of intervals. The boundaries between the different clusters are very clear, and the characterized velocities or intensities of rainfall are largely consistent with the actual situation.
## 3 Results and Discussion
### Destructive Tests
#### 3.3.3 Discussion of the Test Results
Showing the best performance among the developed mortars, V had flexural and compressive strengths 9% and 13% higher than V.NMS', 129% and 206% higher than P's, and 152% and 182% higher than P.NMS', respectively. When comparing the commercial composition with the developed mortars, the V and V.NMS groups had enhanced flexural strengths and decreased compression strengths; however, the results are within a similar range of values. The results shown in Table 8 are in accordance with the non-destructive test results, as the highest velocities/frequencies are seen in V and the lowest in P.NMS. C and V stand out as the compositions with the best mechanical behaviours; however, passive fire protection mortars' mechanical strengths are not limited by a minimum value, according to EN 13279-1 [48]; hence, only thermal requirements should be complied with. To assess data reliability, various linear regressions were conducted. It was observed that the flexural strength shows a strong correlation with both the Young's modulus and shear modulus of the mortars, with Pearson correlation coefficients of 0.9955 and 0.9920, respectively. Similarly, there was a strong correlation between the compressive strengths of the mortars and their corresponding Young's and shear moduli, with coefficients of 0.9579 and 0.9669, respectively. Although the correlation between the flexural strength and compressive strength was not as pronounced, a correlation coefficient of 0.9429 was still observed. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mortar Designation** & **Flexural Strength (MPa)** & **Flexural Strength (\%)** & **Compression Strength (MPa)** & **Compression Strength (\%)** \\ \hline C & 1.945 & - & 3.297 & - \\ P & 0.955 & 49.1 & 1.023 & 31.0 \\ P.NMS & 0.868 & 44.6 & 1.110 & 33.7 \\ V & 2.189 & 112.5 & 3.139 & 95.2 \\ V.NMS & 2.008 & 103.2 & 2.767 & 83.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Developed mortars’ destructive test results and comparison ratios to the commercial compositions.