esa-sceva/satcom-chunk-collection huggingface dataset:
# Emotion Detection Using Facial Expression Involving Occlusions and Tilt
## 4 Results and Discussions
### Experiment 2
#### 4.2.3 Testing Phase
Results for Test Model 1 are given in Table 23. According to the end result of the test, the total accuracy of Test Model 1 is 93.16%. The most precise sentiment detected is 'disgust' with 97.29% accuracy and the least detected emotion is neutral with 76.19% accuracy. Results for Test Model 2 are given in Table 24. Results indicate that Test Model 2 predicts only the'sad' emotion with a 100% accuracy while the accuracy for all other emotions is 0. The average accuracy of Test Model 2 for all emotions is 14.55%. Table 25 shows the results of the proposed model after 100 epochs. The proposed model is capable of detecting'sad' emotion with 100% accuracy, whereas other sentiments are identified with 94% or higher accuracy except for the 'neutral' emotion with an accuracy of 84.29%. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Emotions** & **Dataset** & **Correct Prediction** & **Incorrect Prediction** & **Percentage** \\ \hline Anger & 420 & 392 & 28 & 93.33\% \\ Disgust & 406 & 395 & 11 & 97.29\% \\ Fear & 448 & 435 & 13 & 97.10\% \\ Happy & 434 & 412 & 22 & 94.93\% \\ Neutral & 420 & 320 & 100 & 76.19\% \\ Sad & 434 & 422 & 12 & 97.24\% \\ Surprise & 420 & 403 & 17 & 95.95\% \\ Average & 2982 & 2779 & 204 & 93.19\% \\ \hline \hline \end{tabular}
\end{table} 
Table 23: Cross dataset testing result of Model 1 after 100 epochs. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Emotions** & **Test Model 1 [21]** & **Test Model 2 [22]** & **Proposed Model** \\ \hline Anger & 84.29\% & 100\% & 80.24\% \\ Disgust & 95.32\% & 0\% & 91.38\% \\ Fear & 76.12\% & 0\% & 80.80\% \\ Happy & 95.16\% & 0\% & 83.41\% \\ Neutral & 50.71\% & 0\% & 59.52\% \\ Sad & 92.4\% & 0\% & 97.93\% \\ Surprise & 95.48\% & 0\% & 96.67\% \\ Average & 84.17\% & 14.08\% & 84.27\% \\ \hline \hline \end{tabular}
\end{table} 
Table 22: Comparison of Test Model 1, Test Model 2, and proposed model after 10 epochs with the cross dataset. According to the observation of test 2 based on the cross dataset with 100 epochs, Test Model 1 is adequate to distinguish emotions like disgust, fear, and surprise at a higher rate whereas happy emotion is detected at the same rate by Test Model 1 and the proposed model. The proposed model, on the other hand, recognizes anger, neutral and surprise faces more correctly, as shown in Table 26.  
The overall accuracy rate of the proposed model is 94.94%. Test Model 1 predicted emotions above 76% and up to 97.29%, whereas the proposed model predicted all emotions above 84% and up to 100% accuracy. The overall accuracy of Model 1 is 93.19%, which is less than that of the proposed model.
## 3 Results
### Gene Expression Evaluation in the hMSC/*PCL Constructs  
The expression levels of collagens type I, II, IV-\(\alpha\)1 and IV-\(\alpha\)1 in both differentiated and undifferentiated hMSCs on the *PCL scaffolds under static or dynamic culture conditions were analyzed and compared after seven days (Figure 2).  
Collagen type I mRNA expression was significantly increased in the constructs differentiated under static conditions if compared to the undifferentiated counterpart (\(p<0.01\)). In the constructs differentiated under dynamic conditions, no statistically significant difference of Collagen type I mRNA expression was revealed if compared to the undifferentiated control. However, it was observed a statistically significant decrease if compared with the constructs differentiated under static conditions (\(p<0.01\)) (Figure 2a). In both differentiated groups (statically and dynamically cultured), collagen type II mRNA expression was significantly higher than in the undifferentiated control (\(p<0.05\)); however, no statistically significant differences could be detected between the differentiated groups (Figure 2b). Collagen type IV-\(\alpha\)1 gene expression was increased in the constructs differentiated under static conditions if compared to the undifferentiated control (\(p<0.05\)). Differently, in the constructs differentiated under dynamic conditions, no difference of mRNA expression was detected for this collagen type if compared to the undifferentiated control. A statistically significant decrease of mRNA expression was highlighted in dynamically differentiated samples if compared to those differentiated under static conditions (\(p<0.05\)) (Figure 2c). Collagen type IV-\(\alpha\)2 gene expression resulted to be modulated by neither the differentiating CM nor culture methods in our experimental conditions (Figure 2d).  
Figure 1: Neutral red assay show red-stained viable cells imaged via stereomicroscopy on human mesenchymal stromal cell (hMSC)/*PCL constructs: (**a,b**) differentiated in static conditions; (**c,d**) differentiated in dynamic conditions; and, (**e,f**) undifferentiated in static conditions. (**a,c,e**) Scale bar = 2 mm; (**b,d,f**) scale bar = 600 μm.
# Efficient Parallel Processing of Big Data on Supercomputers for Industrial IoT Environments
###### Abstract
This gap is critical, as ARM-based architectures are increasingly being adopted in edge and cloud computing environments due to their energy efficiency and scalability, making them well-suited for industrial IoT applications. This paper explores two central aspects of deploying big data processing frameworks on ARM-based HPC architectures: first, the extent to which current software stacks support advanced vectorization features like SVEs, and second, how these architectural capabilities influence the performance of in-memory processing systems like Apache Spark. First, we investigated the extent to which current big data processing software stacks, such as Apache Spark 3.0.2, support vector instructions (SVEs) for ARM-based HPC architectures. Second, we assessed the performance of in-memory big data processing systems, specifically Apache Spark, on ARM-based HPC systems leveraging SVE instructions. To answer these questions, we conducted a first-of-its-kind extensive performance evaluation on an ARM-based HPC deployment with JVM SVE support. This study aims to equip researchers and system architects with empirically derived deployment guidance and performance optimization strategies for big data frameworks operating on ARM-based HPC systems, particularly within industrial IoT and edge computing contexts. This study aims to investigate the performance and feasibility of deploying Apache Spark, a widely adopted in-memory big data processing framework, on ARM-based High-Performance Computing (HPC) systems equipped with Scalable Vector Extensions (SVEs). Specifically, we seek to address two key objectives: (1) to evaluate the extent to which current big data software stacks support vectorized instructions such as SVEs on ARM-based HPC architectures, and (2) to assess the performance characteristics of Apache Sparkwhen executing industrial IoT workloads on ARM-based HPC systems leveraging SVE capabilities. These objectives shape the direction of our experimental evaluation and help identify key performance factors and configuration strategies for deploying big data frameworks in energy-efficient and scalable computing settings. In this study, we characterized the performance of Apache Spark on an ARM-based HPC system using representative big data benchmarking workloads. We analyzed the impact of various configuration settings and provided recommendations for optimizing performance in industrial IoT scenarios. The remainder of this paper is organized as follows. Section 2 provides an overview of the theoretical foundations and relevant background. Section 3 describes the system architecture and methodology used for testing. Section 4 presents the deployment, test cases, and results of our performance evaluation. Section 5 discusses the implications of our findings, while Section 6 reviews related work in the field. Finally, Section 7 concludes the paper with recommendations for future research.
## 4 Discussion
On the other hand, VIs and MODIS GPP explained only a small part of the variance in water stress, indicating that they could not fully capture the effects of water stress on plant productivity. Our results further indicated that VIs in combination with environmental stresses (temperature stress and water stress) could better explain the variance in GPP. The two environmental stresses - \(\mathrm{f_{Topt}}\) and \(\mathrm{f_{VPD}}\) limited GPP during low temperature and drought periods, respectively. For example, VPD is an important controlling factor of GPP at AU-Das (a savanna ecosystem in Australia), while temperature is a dominant controlling factor of the photosynthesis at the NL-Loo site (an ENF ecosystem in Netherlands). Drought can reduce stomatal conductance when hydraulic capacity cannot meet the transpirational demand in a drought season, particularly with increasing atmospheric VPD [62]. Low temperature can inhibit photophysical and photochemical processes involved in light absorption and energy transfer and transformation and can also decrease the rates of the enzymatic reactions including C, N, and S [63; 64], leading to the reduction of GPP.
## 4 Discussion
### Absolute Value  
The absolute value of CLM4 FPAR is not evaluated due to disagreements among observational datasets. Inter-comparison among satellite-based FPAR products (Figure 13) shows discrepancies among them, and such disagreements have been noted in many remote sensing evaluation papers [28, 31, 67]. There are many factors contributing to these discrepancies. First, the satellite passing time is different for each satellite. We have illustrated how the angular effect in direct solar radiation would lead to an FPAR difference. Second, their statistical methods differ. MODIS selects the maximum FPAR (across the eight days) for the eight-day product [21] while SeaWiFS and MERIS use the median value in generating their products [48]. Last, radiative assumptions are different for different retrieval algorithms, though their influences require more study. Therefore, CLM4-FPAR's absolute value is not be evaluated in this study. The difference in the absolute value is removed by subtracting the temporal averaged value for evaluation on diurnal and seasonal cycles and spatial  
Figure 12: Averaged rain rate in northern and southern Amazon (divided by the equator) from the Global Precipitation Climatology Project (GPCP) dataset (2003–2005).  
patterns. The absolute value is used to calculate linear regression in long-term trend analysis, but does not affect the slope which is the key statistical parameter we analyzed.
# Modernization Data Analysis and Visualization for Food Safety Research Outcomes
## 3 Descriptive Statistics
### Variance and Standard Deviation  
Variance is a measurement of the spread between observations in a data set, and it allows us to measure how far the values in the data set are from the mean. The adjusted sample variance is calculated by the sum of the deviances between the values and the mean value and divided by the degrees of freedom of the data set (\(n-1\)). Since the geometric mean is the measurement used for positively skewed data such as microbial counts, variance of log\({}_{10}\)-transformed values should be estimated. Moreover, variance doesnot have the same unit as the original data, as the deviance is squared for its calculation. The formula of the variance is represented in Equation (4).  
\[s^{2}=\sum_{i=1}^{n}\frac{\left(log_{10}x_{i}-\overline{log_{10}x}\right)^{2}}{n-1} \tag{4}\]  
where \(s^{2}\) is the variance of the transformed values and \(n\) is the number of observations.  
\[s^{2}=\left[\frac{(3-3.6954)^{2}+(3.3010-3.6954)^{2}+(3.34771-3.6954)^{2}+(3.69 90-3.6954)^{2}+(5-3.6954)^{2}}{5-1}\right]\]  
The standard deviation represents the average amount of variability in the data set, and it is calculated by the square root of the variance, resulting in a measurement with the same units as the original data. A simple interpretation for the standard deviation is that a high standard deviation suggests that the observations in the data set are generally far from the mean, while low values indicate that the observations are clustered close to the mean. The wider the distribution, the further the sample mean is from the population mean, suggesting that the parameter is not very good at explaining the data set. The formula for the standard deviation is represented in Equation (5).  
\[s=\sqrt{s^{2}} \tag{5}\]  
where \(s\) is the standard deviation.  
\[s=\sqrt{0.5971}\] \[s=0.7727\text{ Log CFU}/\text{g}\]  
Note that the variance and the standard deviation of the geometric mean (mean log count) should not be directly back-transformed, since the resulting value can be misleading.
# An Edge-Based LWM2M Proxy for Device Management to Efficiently Support QoS-Aware IoT Services
###### Abstract  
The Internet of Things (IoT) brings Internet connectivity to devices and everyday objects. This huge volume of connected devices has to be managed taking into account the severe energy, memory, processing, and communication constraints of IoT devices and networks. In this context, the OMA LightweightM2M (LWM2M) protocol is designed for remote management of constrained devices, and related service enablement, through a management server usually deployed in a distant cloud data center. Following the Edge Computing paradigm, we propose in this work the introduction of a LWM2M Proxy that is deployed at the network edge, in between IoT devices and management servers. On one hand, the LWM2M Proxy improves various LWM2M management procedures whereas, on the other hand, it enables the support of QoS-aware services provided by IoT devices by allowing the implementation of advanced policies to efficiently use network, computing, and storage (i.e., cache) resources at the edge, thus providing benefits in terms of reduced and more predictable end-to-end latency. We evaluate the proposed solution both by simulation and experimentally, showing that it can strongly improve the LWM2M performance and the QoS of the system.  
LWM2M Proxy; IoT; OMA LWM2M; QoS
# Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer's Disease Detection
## 4 Results and Discussion
The performance of two ensemble techniques, stacking and hard voting, for Alzheimer's disease classification is summarized in Table 2. Both techniques were evaluated using key metrics accuracy, precision, recall, and F1-score to assess their effectiveness in classifying the stages of Alzheimer's disease. As seen in the table, the stacking ensemble method achieved the highest accuracy of 98.0%, with a precision of 98.0%, recall of 97.0%, and an F1-score of 97.0%. This high level of performance demonstrates the effectiveness of stacking in leveraging the strengths of multiple base classifiers, resulting in a robust model capable of capturing complex patterns within the dataset. The elevated precision and recall metrics indicate that stacking excels in distinguishing between different stages of [PERSON]'s disease, minimizing misclassification errors and enhancing classification reliability. The hard voting ensemble also exhibited strong performance, achieving an accuracy of 97.0%, with precision, recall, and F1-scores all at 97.0%. Although showing slightly lower performance than the stacking method, the hard voting technique still delivers high classification accuracy and consistency across all evaluation metrics. This performance suggests that hard voting, by aggregating the predictions of multiple classifiers, provides a balanced and effective approach that closely approximates the stacking method in terms of overall classification capability. Figure 5 shows the comparative analysis of the stacking and hard voting ensemble methods, revealing virtually identical high performance across all metrics (accuracy, precision, recall, and F1-Score). Both methods consistently reach or exceed the 95% reference threshold and exhibit minimal variance, as evidenced by the tight confidence intervals. This suggests interchangeability between these ensemble techniques for high-performance classification tasks. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Ensemble Techniques** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Stacking & 98.0\% & 98.0\% & 97.0\% & 97.0\% \\ Hard Voting & 97.0\% & 97.0\% & 97.0\% & 97.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of ensemble techniques in [PERSON]’s classification. Figure 4: The 95% confidence intervals for performance metrics for ViT and each of the based models.
###### Abstract  
Solar irradiance is an available resource that could support electrification in regions that are low on socio-economic indices. Therefore, it is increasingly important to understand the behavior of solar irradiance. and data on solar irradiance. Some locations, especially those with a low socio-economic population, do not have measured solar irradiance data, and if such information exists, it is not complete. There are different approaches for estimating solar irradiance, from learning models to empirical models. The latter has the advantage of low computational costs, allowing its wide use. Researchers estimate solar energy resources using information from other meteorological variables, such as temperature. However, there is no broad analysis of these techniques in tropical and mountainous environments. Therefore, in order to address this gap, our research analyzes the performance of three well-known empirical temperature-based models--[PERSON] and [PERSON], [PERSON] and [PERSON], and Okundamiya and Nzeako--and proposes a new one for tropical and mountainous environments. The new empirical technique models daily solar irradiance in some areas better than the other three models. Statistical error comparison allows us to select the best model for each location and determines the data imputation model. [PERSON] and [PERSON]'s model had better results in the Pacific zone with an average RMSE of \(936,195\leavevmode\
obreak\ [\mathrm{Wh/m^{2}\leavevmode\
obreak\ day}]\), SD of \(36,01\%\), MAE of \(748,435\leavevmode\
obreak\ [\mathrm{Wh/m^{2}\leavevmode\
obreak\ day}]\), and \(\mathrm{U_{95}}\) of \(1.836,325\leavevmode\
obreak\ [\mathrm{Wh/m^{2}\leavevmode\
obreak\ day}]\). The new proposed model showed better results in the Andean and Amazon zones with an average RMSE of \(1.032,99\leavevmode\
obreak\ [\mathrm{Wh/m^{2}\leavevmode\
obreak\ day}]\), SD of \(34,455\leavevmode\
obreak\ [\mathrm{Wh/m^{2}\leavevmode\
obreak\ day}]\), MAE of \(825,46\leavevmode\
obreak\ [\mathrm{Wh/m^{2}\leavevmode\
obreak\ day}]\), and \(\mathrm{U_{95}}\) of \(2.025,84\leavevmode\
obreak\ [\mathrm{Wh/m^{2}\leavevmode\
obreak\ day}]\). Another result was the linear relationship between the new empirical model constants and the altitude of 2500 MASL (mean above sea level).  
t  
temperature-based models; data imputation; tropical environment; logistic regression; [PERSON] and [PERSON]; [PERSON] and [PERSON]
## 6 Results
### Results from Studying the PDU
#### 6.1.2 Data Exchange Security Study  
To verify that the data exchange between the control center and the PDU is secure, the capabilities of the Wireshark network protocol analyzer are used. All subsequent results are obtained from it.  
Figure 6 shows that the username and password are exchanged in plain text. Exchanging user information in this way is very dangerous because it can easily be intercepted. This is a major drawback of the studied PDU. If someone is nonstop \"sniffing\" the traffic in a network (there are many persons of this kind), they can easily capture this information and use it to hack the device (for fun or for something else). As a result, the PDU will be compromised and misused.  
Figure 5: Top port by total traffic.  
Figure 7 shows that in addition to the username and password, other important information is exchanged in plain text. This is the menu information for changing the password of the user account to access the device. This way of exchanging information is unacceptable.  
Figure 8 shows the code that is exchanged between the PDU and the control center to control the power contacts of the PDU. The information is again in plain text and can be manipulated very easily.  
Figure 8: Power management buttons.  
Figure 7: Account menu settings.  
supply to various forms of communication equipment. For the normal and trouble-free operation of expensive communication equipment, the AC power must be with an ideal sine wave. Such an AC voltage is characterized by only one spectral component, the first harmonic, with the other harmonics being absent. This is not the case with the other two UPS types (line interactive and passive (stand by)). In these types of UPSs, the output voltage is not sinusoidal but is rather a modified sine wave, which is characterized by a rich harmonic composition. Such a voltage can cause the expensive communication equipment to be defective or to not function properly.
# A Dual-Polarized Omnidirectional Rectenna Array for RF Energy Harvesting Harvesting
## 3 Power-Combining Scheme for the Rectenna Array  
In the design of rectenna arrays, two possible power-combining schemes can be considered, i.e., RF power combining and DC power combining. RF power combining is to first combine the RF power received by the array elements, and then convert the total RF power to DC power. On the other hand, DC power combining is to first rectify the RF power outputs of the array elements into DC power outputs, and then combine them together.  
During the RF energy harvesting, there are large discrepancies between the power levels received by the six ports of the proposed antenna array, which is detrimental to the PCE of the whole system. In addition, as the incident direction of the EM wave varies, the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parameter** & **Value (mm)** & **Parameter** & **Value (mm)** & **Parameter** & **Value (mm)** & **Parameter** & **Value (mm)** \\ \hline \(W_{\text{m1}}\) & 1.6 & \(W_{\text{m5}}\) & 4.1 & \(L_{\text{m2}}\) & 10.6 & \(L_{\text{m6}}\) & 7.2 \\ \(W_{\text{m2}}\) & 0.5 & \(W_{\text{m6}}\) & 2.4 & \(L_{\text{m3}}\) & 6.9 & \(L_{\text{m7}}\) & 11 \\ \(W_{\text{m3}}\) & 2.1 & \(W_{\text{m7}}\) & 4.8 & \(L_{\text{m4}}\) & 9.7 & & \\ \(W_{\text{m4}}\) & 0.5 & \(L_{\text{m1}}\) & 5 & \(L_{\text{m5}}\) & 6 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dimensions of the proposed rectifier (unit: mm).  
Figure 8: Simulated and measured PCEs and output voltages of the rectifier.  
Figure 7: Simulated PCEs of the rectifier under different load resistances.  
ratios of those received power levels will change as well. As discussed in [26; 27], when the input power ratio of the two parallel rectifiers is changed, the PCE of the rectifier array will also be degraded. Therefore, under this circumstance, just using RF power combining or just using DC power combining is not the best choice. In this design, the horizontally polarized RF power and the vertically polarized RF power are combined separately into two independent portions. The RF power from all the horizontally polarized EM waves is combined into one port and named the horizontally polarized port (H-port). Similarly, the RF power from all the vertically polarized EM waves is combined into another port and named the vertically polarized port (V-port). Then, the two portions of RF power are evenly distributed by a 3-dB hybrid coupler, which can help the backend rectifiers obtain a more stable PCE.