esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Experimental Results
### Comparison on Multi Features or Classifiers Fusion Performance Analysis  
To verify the rationality and validity of our proposed multi-layer fusion strategy, we designed two comparison experiments. One compares one feature with multiple feature fusion, and the other compares one classifier and multiple classifiers. This paper makes comparison on the recognition performance (accuracy rate) between our proposed full fusion methods with multiple features and multiple classifiers with two partial fusions.  
Here, we use one of four features (Gabor/geometry/LBP/WLD) as the feature for AUs and employ combined classifiers with MLP, SMO, IBK and NB to do facial expression recognition. From the experimental results (Table 7), compared with using single feature, the multiple feature fusion based method exhibits the best expression recognition results even in different classifiers and different datasets.  
Similarly, this paper used one of three classifiers (IBK/MLP/SMO) as a single classifier. Comparison is made between this classifier and a multi-classifier combination (SMO + IBK + MLP) under the same AUs feature for facial expression recognition. From the experiment (Table 8), we found that the recognition rate of all single classifiers alone does not exceed the multiple classifiers fusion method.  
As is shown in the Tables 7 and 8, all the cases with highest prediction accuracy are specified in bold. The results show that the multiple features or the multiple classifiers fusion method achieves the highest accuracy rate in most cases and comparative results with the highest ones in a few other cases. It indicates that the fusion methods generally outperform any single feature or classifier-based methods. The results also verify that there is a great complementarity between different features/different classifiers. Therefore, benefiting from making full use of the diversity among different features and classifiers, fusion-based methods overcome the shortcomings of a unilateral aspect. Conclusively, the fusion strategy for AUs' prediction based on a stacking framework is feasible.  
\begin{table}
\begin{tabular}{l c c} \hline
**Emotions** & **CK** & **JAFFE** \\ \hline
1: angry & 39 & 30 \\
2: disgust & 43 & 29 \\
3: fear & 55 & 32 \\
4: happy & 81 & 32 \\
5: sad & 62 & 30 \\
6: surprise & 75 & 30 \\ Total images & 355 & 183 \\ \hline \end{tabular}
\end{table}
Table 6: Sample size of each emotion on CK and JAFFE datasets.
# Code Edit Recommendation Using a Recurrent Neural Network
## 7 Discussion
### Why CERNN Works  
The result of our experiments in Section 6.2 (for RQ2) shows that our CERNN model (called CERNN\({}_{\text{v2}}\)) yields a 64% F-score, while MI-EA yields 59%, whereas the experimental result in Section 6.1 (for RQ1) shows that our CERNN model (called CERNN\({}_{\text{v1}}\)) yields a 45% F-score. The main difference between the two cases is that CERNN\({}_{\text{v2}}\) stops recommendations when the first recommendation is found to be incorrect. We built CERNN\({}_{\text{v2}}\) with several differences. First, CERNN\({}_{\text{v1}}\) uses ordered contextual information. We expected that ordered contextual information can lead to more precise recommendations than unordered contextual information that is typically used in existing association rule mining methods. Second, CERNN\({}_{\text{v2}}\) uses a deep learning technique. Third, when we created data for CERNN\({}_{\text{v2}}\), we used the data that contained edit elements. Because the context around edit elements could be the information that might lead to the edit elements, using only the data that contain edit elements could positively affect the recommendation accuracy. However, the combination of these factors does not yield higher recommendation accuracy than MI-EA. Finally, we added one more difference to CERNN\({}_{\text{v2}}\) by making it stop recommending when the first recommendation in a task (i.e., interaction trace) is found to be false.  
We also note that the answer set for the CERNN is slightly different from the answer set for the MI-EA. For CERNN, which keeps the order of records in interaction traces, only the files edited after the recommendation timepoint in the answer set are included. For MI-EA, on the other hand, the files edited over the entire interaction trace in the answer set are included. We conjecture that this decision would work negatively on the performance of CERNN, that is, the number of edited files in an answer set for CERNN would be smaller than that for MI-EA, because CERNN has fewer candidates than MI-EA in the answer set. However, we adopted this decision in our evaluation, because it is more reasonable to predict the next element to be edited. Because of this decision, we also trained CERNN by regarding the edited elements after a recommendation timepoint as an answer set. When generating the learning model, CERNN assigns only the edited files that appear after the last event of the context as files to recommend whereas MI-EA assigns all of the edited files existing in the interaction trace as the files to recommend. In the case of a model that emphasizes the sequence of interactions, it is reasonable that files edited before a context are not regarded as correct. However, because CERNN considers fewer edited files than MI-EA, it could negatively affect the recommendation accuracy of CERNN, compared to MI-EA. That is, if the number of edited files is smaller, the number of recommendation candidates will be smaller for the recommendations made in a similar context.
## 4 Simulation Results
### Results of Pattern 3  
In this section, the PSC condition with non-uniform radiation values of 900 and 600 W/m\({}^{2}\) (Pattern 3) for the 2S PV configuration is considered to evaluate the ROA's ability to obtain the GMPP and also the results compared with the MRFO and PSO in this pattern. Changing the PV power, voltage, and current and also the converter duty cycle in tracking process are demonstrated in Figure 11. The GMPP in this pattern is equal to 79.72 W. The obtained results show that the extracted power using the ROA, MRFO, and PSO are 79.69 W, 79.65 W, and 79.61 W, respectively. Figure 11 confirmed the better capability of the ROA in achieving GMPP with lower fluctuations and more tracking efficiency and speed in comparison with the MRFO and PSO. Hence, the ROA is a competitive method with lower control parameters compared to the MRFO and PSO in MPPT solving.  
The numerical results of ROA application for tracking the PV system's GMPP in pattern 3 is presented in Table 6. As shown, the ROA is achieved to maximum power, with a tracking efficiency equal to 99.96%; the values obtained for the MRFO and PSO methods are 99.91% and 99.86%. Furthermore, the results clarify that the ROA is a fast tracking method in comparison with the MRFO and PSO in achieving the PV maximum power. The ROA reached the GMPP in 0.90 S, but the MRFO and PSO obtained this point in 2.20 S and 3.10 S. Thus, the results confirm the better capability of the ROA in comparison with the MRFO and PSO in terms of higher tracking efficiency and rate.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter/Method** & **ROA** & **MFRO** & **PSO** \\ \hline Global power (W) & 79.72 & 79.72 & 79.72 \\ Extracted power (W) & 79.69 & 79.65 & 79.61 \\ Tracking efficiency (\%) & 99.96 & 99.91 & 99.86 \\ Tracking rate (S) & 0.90 & 2.20 & 3.10 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Numerical results of the ROA, MRFO, and PSO methods in pattern 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter/Method** & **ROA** & **MFRO** & **PSO** \\ \hline Global power (W) & 90.54 & 90.54 & 90.54 \\ Extracted power (W) & 90.52 & 90.47 & 90.45 \\ Tracking efficiency (\%) & 99.97 & 99.92 & 99.90 \\ Tracking rate (S) & 1.08 & 2.25 & 2.78 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Numerical results of ROA, MRFO, and PSO methods in pattern 2.
## 6 Conclusions  
A hardware firewall implemented on an FPGA for the security of network data transmission using the Ethernet protocol was presented in this work. The firewall was experimentally validated in the case of a data throughput of 1 Gb/s, and preliminary simulations have shown that the proposed design can be upgraded with minor modifications to work at a data throughput of 10 Gb/s. The device can process network packets of the types ARP, UDP, TCP and ICMP with a latency of 627 ns and a data throughput in the range from 0.811 Gb/s to 0.982 Gb/s for a packet size in the range from 100 bytes to 1000 bytes. It is user configurable using a whitelist or blacklist approach, with up to 256 rules for each port that are used to discriminate between safe and potentially dangerous data packets. A set of statistics, such as the number of received/transmitted packets and the amount of received/transmitted data, is calculated and can be used to detect possible anomalies in the network traffic. Overall, the proposed firewall based on an FPGA is a cost-effective solution that is highly competitive with hardware firewalls based on ASICs and can be used to mitigate security threats of malicious data within the network, yielding users full observability of the device behavior. Future steps of this research project will be the implementation of the upgraded 10 Gb/s firewall design on an FPGA to test its performance under real operative conditions and/or the implementation of a stateful packet inspection.  
Conceptualization, M.G., A.G. and F.A.; methodology, M.G., A.G. and F.A.; software, M.G. and F.A.; validation, M.G. and F.A.; formal analysis, M.G. and F.A.; investigation, M.G. and F.A.; resources, M.G. and F.A.; data curation, M.G.; writing--original draft preparation, M.G.; writing--review and editing, M.G., A.G., F.A. and M.P.; visualization, M.G., A.G. and F.A.; supervision, A.G. and M.P.; project administration, A.G. and M.P.; funding acquisition, A.G. All authors have read and agreed to the published version of the manuscript.  
Italian Ministry of University and Research, Grant/ Award Number: J45F21002000001; \"Alma Idea 2022\" Linea di Intervento A (D.M. 737/2021); Italian Ministry of Industry Incentives (MISE); Ministry of University and Research (MUR).  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors would like to thank the National Institute for Nuclear Physics (INFN, Bologna division) and the National Center for Frame Analysis (CNAF, Bologna division) for the support in the development and testing of the presented hardware firewall on FPGA.  
The authors declare no conflicts of interest.
# Comparison of Traffic Flow Models with Real Traffic Data Based on a Quantitative Assessment
## 3 Materials and Methods  
### Data  
To develop and apply the methodology, the data were sourced from a section of the S6 express road located in Gdansk, Poland (54 deg25' N, 18 deg29' E). The section is part of a dual carriageway with four lanes running within the conurbation. The speed limit for passenger cars is 120 km/h and 80 km/h for heavy goods vehicles. The share of trucks in overall traffic is 9% on average. Annual average daily traffic is 74,000 vehicles per day. During peaks of traffic (summer holidays), volumes are observed to exceed 100,000 vehicles per day.  
The data [30] come from a continuous traffic measurement station which is operated by a double induction loop. Vehicles crossing the station are automatically detected by devices installed on each lane, which register the time a vehicle is detected, ascertain its spot speed, and identify the type of vehicle and lane it is using.  
The data cover a period of 36 months between 2014 and 2017 on the southbound carriageway. A total of 37.5 million vehicles were recorded over that period.  
Structured Query Language (SQL) was used for data processing and initial analysis. The data processing was divided into the following stages:  
1. Raw traffic data that were provided by the national road authority (General Director for National Roads and Motorways, GDDKiA) in the text file format were imported to the database on the installed SQL server.  
2. The data were verified in terms of empty rows, zero values, vehicle speeds beyond the expected range, and unusual vehicle lengths. The problem of zeros or unusual values concerned approximately 2% of registered vehicles and had marginal impact on the number of registered vehicles--the records were excluded from further processing.
3. Individual vehicle headways were calculated for each record.
4. The data were aggregated into 5 min intervals generating information about traffic volume, space-mean speed, share of heavy goods vehicles, or average headways. The traffic volume was calculated into flow rate using passenger car equivalents [5]. Traffic density was determined from the relation (1).  
The database was extended with weather information (condition and intensity of precipitation, condition of road surface, horizontal visibility), conditions of natural lighting (dusk, day, dawn, night), and presence of road events (road works, collisions, accidents, stationary vehicles, etc.). Precipitation, wet or snow-covered/icy road surface, lack of natural lighting, and visibility below 200 m were proved to have a significant impact on space-mean speed [31]. Thus, periods of road events and adverse weather and lighting conditions were excluded from further analyses.
# Optimal Tests for Combining \(p\)-Values
## 4 Real Data Examples
### Example 1: A Meta-Analysis  
In a meta-analysis, 12 randomized trials examining the effect of patient rehabilitation designed for geriatric patients versus usual care on improving functional outcome at 3-12 month follow-up were used [31; 32]. The estimated odds ratios (ORs) from the 12 trials are listed in Table 1.  
The \(p\)-value from the [PERSON]'s test for homogeneity was 0.021, indicating that the commonly used fixed effect model of meta-analysis was inadequate for this data set. Therefore, the authors ran the meta-analysis with a random effect model and estimated the overall OR as 1.36 with 95% CI (1.08, 1.71) [32]. However, the goodness-of-fit test for the random effect model obtained a \(p\)-value of 0.025 [33], indicating the lack of fit of the random effect model for this data set. Therefore, instead  
Figure 3: Empirical power values of the tests based on two-sided \(p\)-values under scenario 3: \(|\mu_{i}|=\mu/m\), and \(\mu=1.5\), \(1.2\), \(0.8\), and \(0.6\) when \(m=10\), \(20\), \(40\), and \(50\), respectively.  
Figure 2: Empirical power values of the tests based on two-sided \(p\)-values under scenario 2: \(|\mu_{i}|=\mu v_{i}/\sum_{i=1}^{m}v_{i}\), where \(v_{i}\sim U(1,100)\), and \(\mu=1.2\), \(1.0\), \(0.6\), \(0.5\) when the number of true individual alternatives is \(m=10\), \(20\), \(40\), and \(50\), respectively.  
fixed or random effect models to combine information from the 12 trials, we use \(p\)-value combination methods to test whether there is an overall effect.  
In order to use the \(p\)-value combination methods, for each trial, we calculate its \(p\)-value based on its reported 95% CI. Denote U and L the upper and lower limits of the 95% CI; the test statistic can be approximated as \(t=\ln(U\times L)/\sqrt{4\ln(U/L)/3.92}\), whose asymptotic null distribution is \(N(0,1)\). The sample sizes of these 12 trials were relatively large, ranging from 108 and 1388; therefore, we can reasonably estimate their \(p\)-values using the asymptotic null distribution. We calculate the two-sided \(p\)-value for each trial and apply the gamma distribution-based tests. The \(p\)-values from the Min \(p\) (i.e., \(T_{G(0)}\)), Fisher (\(T_{\mathcal{C}(1)}\)), z test (\(T_{G(\infty)}\)), and \(T_{CLRT}\) for combining those two-sided \(p\)-values are 0.013, 0.0068, 0.075, and 0.0047, respectively. Except for the z test, which is known to be less powerful for this heterogeneous situation, all methods obtained \(p\)-values less than 0.05, and the proposed test \(T_{CLRT}\) had the smallest \(p\)-value, indicating that the proposed test is more powerful than the other tests under this specific situation.
## 5 Influence of SDIEC on Optimization Results  
### Comparison of Pareto Front for First and Second Case  
The comparison of the Pareto front obtained from the first case and the second case is shown in Figure 15, where the impellers in the orange wireframe are the impellers that meet the optimization requirements. To illustrate the influence of SDIEC on the optimization results, impellers A, B, C, and D were selected for further study. By comparing impellers A and C, it can be seen that, when the influence of SDIEC was considered in the optimization process, the optimization upper limit under low flow conditions increased by 1.2%. By comparing impellers B and D, it can be seen that the optimization upper limit under high flow conditions increased by 3.5%. Besides, in the second case, with the increase of pump efficiency at low flow conditions, the reduction of pump efficiency at high flow conditions was smaller, which means that the impeller with good comprehensive performance was more likely to be obtained in the second case.  
In summary, to further improve the mixed flow pump performance, it is necessary to consider the influence of SDIEC in the optimization process.
# Evaluation of Yogurt Quality during Storage by Fluorescence Spectroscopy
## 3 Results and Discussion
### Yogurt Classification Based on LDA  
Based on the PCA score coefficient matrix, LDA was used to improve the classification accuracy between different brands of yogurt that were difficult to separate using only PCA. Since the number of PCs selected for the LDA model will influence the classification result, the first six PCs accounting for over 99.99% of the total variance of the data matrix were chosen as the optimal variables to develop the LDA discrimination model. Thus, the 100 \(\times\) 1459 data matrix of fluorescence spectra was transformed to a new dataset consisting of a 100 \(\times\) 6 data matrix. To establish a suitable LDA discrimination model and then evaluate the model, the new dataset was divided into a training set and a prediction set. Each set consists of 50 fluorescence spectra (a 50 \(\times\) 6 data matrix), where the training set was used to establish the LDA discrimination model, and the prediction set was used to predict the feasibility of discrimination model.  
Figure 2a depicts the discrimination results of different yogurt brands by means of 2D scatter plot of the first two most important discriminant functions, an 85% confidence ellipse was used to describe the accuracy of predicted discrimination. As can be observed clearly, the yogurt samples are reasonably well discriminated. The samples of the classes A, B, and D show a good separation, while the samples of the classes C and E have several overlaps, which might produce some misclassifications.  
The predicted classification result of the LDA model is shown in Figure 2b, where the classes A, B, C, D, and E are indicated by the color of blue, green, red, cyan, and mauve, respectively. It can be observed clearly that almost all the fluorescence spectra of yogurt samples (47 samples) except five samples from class E in the prediction set were correctly discriminated. For these five misclassified samples from class E, one was identified as the class A, and the other four were identified as the class C. Therefore, the classes A, B, and D have a good discrimination, the misclassification of the classes C and E are mostly caused by the overlap of discriminant functions shown in Figure 2a. The results demonstrate the LDA discrimination model can be successfully employed for the accurate classification of yogurt brands.
# Electro-Optic Properties of High-Efficiency Organic Electronics with the Addition of An Interlayer
## 1 Introduction
The structure of SAMs has a head group that can bind to the surface of a solid material, an alkyl chain at the center of the molecular structure, and a functional group with a final role in the molecular membrane. The film is formed through hydrolysis and polymerization involving the head group on a solid surface [16]. In recent years, research has focused on the improvement of the performance of OLEDs using SAM [17; 18; 19; 20]. Fluorinated organic compounds are widely used for improving the efficiency of organic light-emitting devices owing to fluorine exhibiting a large electric dipole moment [21]. Additionally, in the field of medical devices and research, significant work has been done in recent years to develop OLED systems based on ITO and improve luminance and its impact on the human body and health [22]. The healing phenomenon occurs at the sites via a photobiological reaction depending on the depth through the human body in the spectrum between 300 nm and 830 nm [23]. An OLED device in photomedicine is engaged during mitochondrial ATP production at specific wavelength bands of PBM. This method is applied in the fields of biological and medical sciences for wound healing and the reduction of pain and inflammation, among other applications. Such methods are increasingly being used in LED and laser treatments, and recently, a PBM therapy using OLED-based flexible materials has been widely utilized [24; 25]. Long-term use is essential to obtain the PBM effect in optimal conditions [26]. Recent studies have reported varying effects of OLED-based PBM therapy on the human body and cells of different types according to the generated wavelength bands. Beneficial effects are produced as melanin or nitric oxide (NO) levels that affect the human body are increased or decreased at the given wavelength bands. However, using a photomedical device requires a target site to be irradiated for a long period of time. Because of the short lifetime of the optical device, its application is limited [27]. Therefore, more studies on increasing the efficiency of OLED should be conducted, which could offer features such as long-time use and portability [28]. In addition, more studies should be conducted on methods to increase the efficiency of OLED long-time use with portability [28].
## 2 Materials and Methods
### Relationship between Glare Polarization Characteristics and Angle of Incidence  
The sea surface glare produced by the reflection of sunlight and moonlight off the sea surface exhibits distinct polarization characteristics [17; 18]. Consequently, the sea surface glare can be decomposed into two polarization components: the s-component, which is perpendicular to the plane of incidence, and the p-component, which is parallel to the plane of incidence. The expressions for the reflection coefficients of these two polarization components are as follows:  
\[r_{s}=\frac{\frac{n_{1}\cos\theta_{i}-n_{2}\cos\theta_{i}}{n_{1}\cos\theta_{i} +n_{2}\cos\theta_{i}}}{n_{1}\cos\theta_{i}+n_{2}\cos\theta_{i}} \tag{1}\]  
\[r_{p}=\frac{n_{2}\cos\theta_{i}-n_{1}\cos\theta_{i}}{n_{2}\cos\theta_{i}+n_{1} \cos\theta_{i}}\]  
Herein, \(\theta_{i}\) represents the angle of incidence of light on the sea surface; \(\theta_{i}\) denotes the angle of refraction of light entering the seawater; \(n_{1}\) is the refractive index of the air above the sea surface; and \(n_{2}\) is the refractive index of seawater. Based on the reflection coefficients of the two polarization components, the degree of polarization \(P\) of the sea surface glare is quantified as follows:  
\[P=\left|\frac{r_{s}^{2}-r_{p}^{2}}{r_{s}^{2}+r_{p}^{2}}\right| \tag{2}\]  
Combining the expression for the degree of polarization of sea surface glare with the Fresnel principles demonstrates that the polarization degree of sea surface glare varies with the changing angle of incidence of light. Hence, the polarization characteristics of sea surface glare are dependent on the light's angle of incidence.