esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results of the Proposed Method
The classifier gave us the following result based on the accuracy rate in the classification of 30 patients, an accuracy rate of 100% was obtained, which classified 30 of 30 patients correctly based on the result given by the ESH table and a classification error rate of 0%, which is equivalent to 0 of 30 incorrectly classified. In these experiments Table 8 shows the results were very good since the classifier was successful in the total of the tests. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Patient** & **Systolic** & **Diastolic** & **Classifier 3** & **Fuzzy Percentage** & **ESH BP\_Leves Table** \\ \hline
**1** & **139** & **84** & **Ishgrade 1** & **62.5** & **High normal** \\
2 & 135 & 90 & Grade 1 & 64.4 & Grade 1 \\
3 & 160 & 98 & Grade 2 & 72.3 & Grade 2 \\
4 & 177 & 110 & Grade 3 & 84.6 & Grade 3 \\
5 & 142 & 85 & Ishgrade1 & 62.5 & Ish grade 1 \\
**6** & **160** & **89** & **Grade 2** & **70.2** & **Ish grade 2** \\
**7** & **182** & **89** & **Grade 2** & **83.4** & **Ish grade 3** \\
8 & 85 & 50 & Hypotension & 10.2 & Hypotension \\
9 & 110 & 70 & Optimal & 36.6 & Optimal \\
10 & 125 & 82 & Normal & 54.5 & Normal \\
**11** & **135** & **85** & **Grade 1** & **61.5** & **High normal** \\
**12** & **159** & **94** & **Grade 2** & **70.2** & **Grade 1** \\
13 & 175 & 105 & Grade 2 & 79.3 & Grade 2 \\
**14** & **180** & **110** & **Grade 2** & **84.2** & **Grade 3** \\
15 & 110 & 80 & Normal & 52 & Normal \\
**16** & **128** & **89** & **Grade 1** & **64.4** & **High normal** \\
**17** & **158** & **70** & **Grade 2** & **70.5** & **Ish grade 1** \\
18 & 150 & 108 & Grade 2 & 83.2 & Grade 2 \\
19 & 199 & 95 & Grade 3 & 88.6 & Grade 3 \\
20 & 179 & 99 & Grade 2 & 81.8 & Grade 2 \\
21 & 181 & 100 & Grade 3 & 82.8 & Grade 3 \\
22 & 210 & 90 & Grade 3 & 88.1 & Grade 3 \\
23 & 140 & 100 & Grade 2 & 74.5 & Grade 2 \\
24 & 159 & 120 & Grade 3 & 88.5 & Grade 3 \\
25 & 178 & 115 & Grade 3 & 88.6 & Grade 3 \\
26 & 140 & 80 & Ishgrade1 & 62.5 & Ish grade 1 \\
**27** & **150** & **89** & **Grade 1** & **64.4** & **Ish grade 1** \\
28 & 179 & 80 & Ish grade 2 & 81.8 & Ish grade 2 \\
**29** & **179** & **89** & **Grade 2** & **81.7** & **Ish grade 2** \\
30 & 199 & 82 & Ish grade 3 & 88.5 & Ish grade 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Shows the results of the 30 patients who were monitored and classified in the classifier 3. The following figures show the behavior of the data of a patient, in each figure, the vertical indicates the systolic or diastolic measure of the patient and the horizontal part indicates the hour that the patient was monitored, Figure 20 shows the input data for systolic and in Figure 21 for diastolic, Figures 22 and 23 show the learning of the neural network with the data provided and It shows clearly how you learn the behavior in a correct way, finally we have Figures 24 and 25 where it shows the trend of this data which will give us as a result to send it to the fuzzy system and perform the classification.
# Evaluation of Optimal Visible Wavelengths for Free-Space Optical Communications
## 3 Methodology
### Current and Frequency Modulation Control  
Two key system parameters--driving current and modulation frequency--were dynamically controlled during testing:  
* Driving Current Control: The laser diode current was varied from 50 mA to 200 mA using digital signals from the microcontroller. These were routed through analog voltage regulators (LM317T, Texas Instruments, Dallas, TX, USA) and transistor driver arrays (ULN2003A, Texas Instruments, Dallas, TX, USA). Ports AD0-AD3 managed the digital-to-analog conversion for current regulation.
* Modulation Frequency Control: Square-wave signals were generated using a voltage-controlled oscillator (74S124, Texas Instruments, Dallas, TX, USA) in combination with dual D-type flip-flops (74S74N, Texas Instruments, Dallas, TX, USA) and MOSFET drivers (TC4428 ACPA, Microchip Technology Inc., Chandler, AZ, USA). These components enabled frequency tuning from 0.6 MHz to 6 MHz via ports IO8-IO11.  
This setup allowed for precise, real-time adjustment of optical signal parameters, enabling characterization of diode response across a broad modulation range.
## 2 Background  
### The Basis of Quantum-Dot Cellular Automata (QCA) Technology  
This technology is based on QCA cells, and the basis of the QCA cell can represent a logical bit with occupied space in the nanoscale. A QCA cell includes two electrons, and, based on the Coulombic repulsion created between two electrons, two logical values of \"0\" and \"1\" are possible. The QCA cells are square, as shown in Figure 1. Each enclosure consists of four holes. The two electrons are trapped inside and can move freely between the holes; by placing two electrons in four spots, six different states are created, which is impossible due to Coulombic repulsion forces between the electrons. As a result, to satisfy these forces, electrons are placed inside the holes with as far apart as possible, until the Coulombic repulsion law is satisfied. Depending on the location of the electrons and their diameter, two structures are created; by the establishment of two electrons in each of these two poles, two different states are created. With these two types of systems, two logical values can be obtained; we will consider one of the logical values. We attribute these two polar structures 1 and -1 to the logical values of \"1\" and \"0\", respectively; the poles at 1 and -1 same are those of the square cells, as shown in Figure 1[6; 7; 8].  
When the electrons move inside the cell, they tunnel between the holes. Then, the moving of the electrons inside the cell is similar to a nonlinear move, and the Coulombic repulsion force is not exerted just between the electrons inside a cell. However, as shown in Figure 2, each cell adjacent to this one, which has a logical value, is affected and affects the next adjacent cell that has no value, converting it to its value [6; 7; 8].  
Figure 1: (**a**) Normal QCA cells’ structure, (**b**) Normal QCA wire’s structure, (**c**) Rotated QCA cells’ structure and (**d**) Rotated QCA wire’s structure.
# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 4 Results
### Configuration of the Simulation Environment
#### 4.2.7 Configuration of the Data Collection Optimization Model
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Parameter**} & \multicolumn{1}{c}{**Value**} \\ \hline Network simulation & NS-3.35 \\ \hline Optimizer & Gurobi 9.5.2 \\ \hline Plotting tool & MATLAB R2021a \\ \hline Mobility scenario generation & SUMO version \(\imath\)1\_14\_1 and OpenStreetMap \\ \hline Simulation time & 600 s per each run \\ \hline Maximum vehicles & 200 \\ \hline Maximum RSUs & 64 \\ \hline Maximum speed of vehicles & 0–60 kph (Urban), 0–100 kph (Non-urban), 0–250 kph (autobahn) \\ \hline Transmission protocol & User Datagram Protocol (UDP) \\ \hline Communication channels & DSRC for (I2V, V2I, V2V), point to point between RSUs, CSMA from RSU to controller nodes, and LTE between vehicles and the controller node \\ \hline Wi-Fi standard & IEEE 802.11p \\ \hline DSRC transmission power & 33 dBm (urban), 41 dBm (non-urban), 44 dBm (highway) \\ \hline DSRC OFDM datarate & 12 Mbps \\ \hline DSRC propagation loss model & Cost-Hata (urban), 3-log distance (non-urban,autobahn) \\ \hline DSRC propagation delay model & Constant speed propagation delay \\ \hline DSRC error rate model & Nist error rate model \\ \hline LTE pathloss model & Cost-Hata \\ \hline LTE maximum transmit power & 23 dBm \\ \hline LTE SRS periodicity & 2, 5, 10, 20, 40, 80, 160, 320 as given in Equation (24) \\ \hline LTE fading model & Trace fading loss model \\ \hline LTE EPC datarate & 1000 Mbps \\ \hline RSU backbone datarate & 1000 Mbps \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary table of simulation parameters.
## 3 Experiment and Discussion
### Performance on Imbalanced Data  
In the MIMIC-III dataset, the proportion of hospitalized patients who died is much lower than that of surviving patients, indicating a sample imbalance. In order to further verify the robustness of our model under imbalanced conditions, we constructed sub-datasets with different positive and negative sample ratios. According to the prediction tasks of hospital mortality and ICU mortality, we selected 19,240 patients who did not die as positive samples and 1860 patients who died as negative samples, and further randomly down-sampled the positive samples according to the positive-negative sample ratios of 1:1, 1:3, 1:5, 1:7 and 1:10. Finally, we selected PM\({}^{2}\)F\({}^{2}\)N and DESAM-cp as baseline models for comparison. The experimental results are shown in Figure 6.  
As shown in Figure 6, the experimental results show that as the degree of data imbalance increases (from 1:1 to 1:10), the AUROC and AUPRC performance of all models show a downward trend, and the degree of decline is positively correlated with the degree of data imbalance. Among them, PM\({}^{2}\)F\({}^{2}\)N decreases most significantly, followed by DESAM-cp, while the proposed model declines the slowest, showing a significant robustness advantage. We speculate that this performance difference is mainly due to two core designs: first, the MacLN designed in this paper effectively stabilizes the feature learning process of the model under different data distributions through an adaptive parameter adjustment mechanism. Second, the cross-modal attention mechanism introduced in this paper can automatically focus on the key features in the minority class samples to avoid the model being dominated by the majority class samples. It is worth noting that even in the case of extreme imbalance (1:10), the proposed model can still maintain high AUROC and AUPRC  
Figure 6: Performance of different models on imbalanced data.  
values, significantly better than PM\({}^{2}\)F\({}^{2}\)N and DESAM-cp, proving the application value of the proposed method in real clinical scenarios.
## 2 Materials and Methods
### Datasets  
Two distinct datasets were processed using an identical training protocol, employing the same architecture, hyperparameters, and training procedures, with the only difference being the input data. The base neural network architecture and dataset preparation are similar to those described in our prior work [43], but the current study expands upon that work by introducing new composite performance metrics and a standardized evaluation protocol aligned with recent FDA guidance. Specifically, the model was trained on Dataset A and also, separately, on Dataset B, and each model was subsequently cross-tested on the alternate dataset to evaluate their performance on external data. Dataset A was acquired from Kaggle [44] and contains knee X-ray images labeled with Kellgren-Lawrence (KL) grades from 0 (healthy) to 4 (severe osteoarthritis). This dataset provides data for both knee joint detection and KL grading. The images have a balanced distribution across grades and were preprocessed to standardize resolution and orientation. Dataset B was obtained from Mendeley Data [45] and similarly comprises knee X-ray images with KLannotations (grades 0-4) following the same definitions as Dataset A. Including Dataset B allowed us to perform external validation and assess our model's generalizability across different sources.  
The selection of Datasets A and B was guided by the need to assess both the in-domain performance and the generalizability of the proposed model for knee osteoarthritis grading. Dataset A (Kaggle) was chosen due to its widespread use in benchmarking ML models for musculoskeletal radiographs and its balanced distribution of KL grades, which enables robust model training. Dataset B (Mendeley Data) was selected as an independent source to enable external validation, as it provides KL-graded knee X-rays collected separately from Dataset A, thus differing in imaging conditions, patient demographics, and potential annotation variability. Including two distinct, publicly available datasets ensures that our evaluation reflects real-world variability and addresses the critical issue of domain shift, thereby supporting a more clinically relevant assessment of model robustness and transferability.
# Comparative Analysis of Audio Features for Unsupervised Speaker Change Detection
## 3 Methodology
### Change Point Detection
#### 3.2.1 BIC-GMM Method  
This method uses the Bayesian information criterion to compare consecutive audio segments \(X_{i}\) and \(X_{i+1}\). It evaluates the following two hypotheses:  
* \(H_{0}\): both segments are derived from a Gaussian \(\{X_{i},X_{i+1}\}\sim N(\mu,\Sigma)\) distribution, in other words, both segments belong to the same speaker.
* \(H_{1}\): both segments are derived from two distinct Gaussians \(X_{i}\sim N(\mu_{1},\Sigma_{1})\) and \(X_{i+1}\sim N(\mu_{2},\Sigma_{2})\). The segments belong to different speakers.  
With these hypotheses in mind, a BIC difference \(\Delta BIC\) is calculated as:  
\[\Delta BIC=(N_{1}+N_{2})\log|\Sigma|-N_{1}\log|\Sigma_{1}|-N_{2}\log|\Sigma_{2} |-\lambda P \tag{2}\]  
\[P=\frac{1}{2}\bigg{(}d+\frac{1}{2}d(d+1)\bigg{)}\log(N) \tag{3}\]  
where \(P\) is penalty term and \(N=N_{1}+N_{2}\) are the sample sizes of the segments, and \(d\) is the dimension of the feature vector, and \(\lambda\) is a penalty weight.  
The larger value of \(\Delta BIC_{i}\), the less similar the two segments are, indicating a greater distance between them. Instead of setting a threshold to it, we compare two \(\Delta BIC_{i-1}\) value, if \(\Delta BIC_{i}>\Delta BIC_{i-1}\), a change point is detected; otherwise, it is not.
## 5 Data Imbalance
### Data Imbalance Handling Techniques Future Research Directions
#### 5.5.7 Signing of a New Loss Function  
Reasonable loss function design and weight allocation can significantly improve the model's training effectiveness and generalization ability. The weighted loss function is a modification of the standard loss function used during model training. Weight is used to allocate higher penalties for misclassification of minority categories, making the model more sensitive to minority categories by increasing the misclassification cost of that category. The most common method to implement a weighted loss function is to assign higher weights to minority classes and lower weights to majority classes. The weight can be inversely proportional to the frequency of the category [131,132].
# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## 4 Results
### Classification Using Random Forest Classifier
#### 4.2.4 RF Using Combined Optical and SAR Data  
The RF classifier using the 15 important PC variables shows slightly higher accuracy than that using the WT coefficients. For this reason, the important variables of the PC-RF were combined with the important variables of the NDVI-RF in a new RF classifier. The accuracies obtained are presented in Table 6. In general, a slight increase in the overall accuracy is obtained when adding the NDVI data to the SAR data using the PC values. The overall accuracy increased by 1.6% while kappa increases by 3%. The F-score of the classification remains unchanged.
## 3 Results and Discussion
### THz Data Analysis
We implemented a procedure of automatic computation of optimal \(\alpha\) value based on the k-fold CV with repetition (sklearn library, RepeatedKFold module with parameters n_splits (number of partitions) 5, n_repeats (number of repetitions) 5) and the LassoCV procedure from the sklearn library, with the parameters: \(\alpha\) in the range 0 to 1 with step 0.01, maximum number of iterations 100,000, error during optimization 0.01. The calculated optimal \(\alpha\) value was then used to create a model, using the Lasso module of sklearn with the same error parameters and number of iterations as during optimization. The model was validated using the k-fold CV with repetition procedure described above, with the same parameters, but the sample composition differed due to applying a random number generator. The THz spectra of groups with glioma from the first to the third week were used as input data. The model quality was assessed by the determination score (R\({}^{2}\)), and mean absolute error (MAE) (see Table 4). The model with R\({}^{2}\) = 0.83 was considered good. A list of informative THz frequencies was obtained (Figure 11). As can be seen from Figure 11, the informative frequencies for the THz spectra were 0.19 and 1.9 THz, the latter making the largest contribution. These informative frequencies were similar to those obtained by the combination of PCA and SVM, especially for the third week of the experiment. Therefore, we believe that there was a relation between tumor size and THz spectra of the studied samples. \begin{table}
\begin{tabular}{c c c} \hline \hline
**R\({}^{2}\)** & **MAE** & **\(\alpha\) Value** \\ \hline
0.83 & 1.120 & 0.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Values of MAE, variance, and optimal regularization.
# Experimental Study on the Risk Preference Characteristics of Members in Supply Chain Emergencies
## 3 Results  
### Data Analysis of Experiment 1  
The reliability test of the questionnaire showed that the coefficient of the overall Cronbach's \(\alpha\) was 0.865, which indicated the good consistency of the questionnaire. The normality test of experiment 1 indicated that the \(p\)-value was 0.582, which showed a normal distribution. A \(t\)-test was conducted on the samples with the result of \(p>0.05\), which indicated that the difference between the two was not statistically significant.  
As shown in Table 4, in general, the proportion of risk neutrality for the subjects was 65.6% in experiment 1, and the proportions of risk aversion and seeking were 18% and 16.4%, respectively. The proportions of risk aversion and seeking for subjects were not outstanding. In the ten risk fields for a supply chain, namely, morality, law, information, supply, logistics, demand, cooperation, economy, politics, and emergency, the risk preference distribution of the subjects was slightly different. That is, the risk preferences of the subjects in the supply chain emergency field were relatively consistent with that of the conventional risk field. It should be noted that the subjects showed more risk aversion or risk seeking in the risk fields of information, logistics, and cooperation than in other risk fields; the lower-risk-aversion or higher-risk-seeking characteristics were shown in the risk field of morality; the higher-risk-aversion or lower-risk-seeking characteristics were shown in the supply risk.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Gender**} & \multicolumn{2}{c}{**Management Position**} \\ \cline{2-6}  & **Men** & **Women** & **Sales** & **Operation** & **Technology** \\ \hline Number of people & 220 & 112 & 168 & 104 & 60 \\ Proportion & 66.27\% & 33.73\% & 50.60\% & 31.33\% & 18.07\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Subjects statistics.  
The study further analyzed the same subject's risk preference in ten risk fields. It was found that no subject always showed risk-averse or risk-seeking behavior, and 44 subjects always showed risk neutrality. The inconsistency of the risk preference distribution for subjects in ten risk fields was 86.7%. Therefore, it can be concluded that individuals' preferences in different supply chain risk fields were not consistent, as shown in Table 5.  
Conclusion 1: In general, the risk preferences of the members could be measured and were consistent in the conventional risk fields and emergencies of a supply chain; the risk preferences of decision makers in the ten fields were not consistent from an individual perspective. These results did not reject hypothesis 1 but rejected the first part of hypothesis 2.
# Impact of Yeast Selection on Volatile Phenol Levels of Wines Produced from Smoked-Exposed Juice
## 2 Materials and Methods
### Laboratory-Scale Yeast Trials  
The Chenin Blanc trial investigated yeast performance under white wine production conditions, while the Merlot trial simulated red wine conditions.  
In the Chenin Blanc yeast trial, the 12 commercial _Saccharomyces cerevisiae_ white wine yeast strains (0.3 g/L) were rehydrated and inoculated into 200 mL smoked juice in sterile glass bottles. Di-ammonium phosphate (DAP) was added at a dosage of 0.50 g/L to ensure nitrogen limitation would not occur. Yeast assimilable nitrogen (YAN) was 179 mg/L. The juice was fermented at ambient temperature of 15 \({}^{\circ}\)C and the bottles were closed with fermentation locks containing distilled H\({}_{2}\)O. A spontaneous fermentation treatment was also included and received no yeast inoculum. Each yeast treatment, as well as the spontaneous fermentation had three replicates. Bottles were weighed daily to determine CO\({}_{2}\) mass loss. Bottles were weighed prior to sampling and after sampling. Samples were taken every 3-4 days for routine chemical analyses and volatile phenol samples were collected at the end of alcoholic fermentation and stored at 4 \({}^{\circ}\)C until analyzed.  
The Merlot yeast trial was conducted in a similar manner to the Chenin Blanc trial, but with 15 commercial red wine yeast strains and the fermentation temperature was ca. 24 \({}^{\circ}\)C. The YAN of the Merlot juice was 131 mg/L. A spontaneous fermentation treatment was also included. Each yeast treatment, as well as the spontaneous fermentation had three replicates.
# Beacon Success Rate versus Gateway Density in Sub-GHz Sensor Networks
Collisions cause co-channel interference, occurring when there are one or more interfering signals present in the channel during communication between the desired transmitter and receiver. The LBT requires the transmitter to transmit only when the channel is determined to be idle by Carrier Activity Detection (CAD). If the CAD determines the medium to be busy, the backoff mechanism requires each EN to wait for a random period before attempting a retransmission. This improves the performance by reducing collisions. However, the LBT scheme suffers from hidden-node problem, where two nodes might be out of coverage with each other while causing interference to a given GW [12]. The LBT mechanism is used for Uplink (UL) transmissions by the ENs to avoid interfering with each other and with the other beacons of neighboring GWs. This improves the performance significantly [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11]. Nonetheless, in the Downlink (DL), GWs do not employ the LBT mechanism. Instead, GWs are required to periodically transmit their high-priority beacons, sending one beacon at each beacon interval. Ensuring this procedure is essential to prevent ENs from losing synchronization with the desired GW, which could lead to network disconnection, particularly when consecutive beacon signal losses occur. This study primarily examines the performance of beacon collisions between multiple GWs and their success rates as received by ENs. Figure 2: Multiple GWs co-existing in a (sub)-urban neighborhood with numerous sensors.