esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Study of Local Descriptors and Classifiers for Facial Expression Recognition
## 5 Experimental Results and Discussion
### Performance Analysis of the Classifiers  
Figure 6 summarizes the best results obtained when varying the sub-region sizes on the CK+ dataset with six expressions. We observe that the best recognition rates (95-98%) were achieved using SVM and MLP classifiers, while the lowest accuracies were produced by the NB and KNN classifiers (73-91%). As reported in Figure 7, a similar trend is observed on the RFD dataset with six expressions. To further analyze the performances of the SVM and MLP classifiers, the previous experiments were repeated on other datasets with varying numbers of expressions. Table 4 gathers the results obtained when the number of expressions was varied. For the CK+ dataset, the expressions were anger, disgust, surprise, sadness, happiness, and disgust in the six-class problems, and the seven-class problem added the neutral expression to the previous list. For the RFD dataset, the expressions were anger, disgust, surprise, sadness, happiness, and disgust in the six-class problem, the seven-class problem added the expression of contempt to the previous list, and the eight-class problem added the neutral expression. Considering the six-class problem, SVM and MLP achieve similar classification rates (above 95%) on the two datasets. Although their performances are very similar, the MLP classifier achieved slightly better recognition rates than SVM when classifying WLD, ALDP, LBP, CLBP, and HOG descriptors. Both classifiers are negatively affected by the introduction of the neutral. The largest performance drop is seen in the ALDP + SVM combination, where the accuracy drops by almost 7%. The recognition rates of both classifiers do not change much when the Contempt expression is introduced in the RFD dataset with seven expressions.  
Figure 5: FER results for different sub-regions sizes on RFD with 6 expressions (**a**) SVM results; (**b**) NB results; (**c**) KNN results; (**d**) MLP results.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Descriptor** & **SVM** & **NB** & **KNN** & **MLP** \\ \hline WLD & (88, 19) & (8, 19) & (96, 10) & (88, 13) \\ LDP & (48, 7) & (12, 10) & (8, 10) & (48, 13) \\ ALDP & (112, 13) & (16, 16) & (112, 13) & (128, 7) \\ LBP & (96, 19) & (32, 19) & (128, 13) & (128, 10) \\ CLBP & (248, 10) & (24, 10) & (248, 13) & (216, 10) \\ HOG & (50, 13) & (6, 7) & (9, 10) & (50, 13) \\ \hline \end{tabular}
\end{table}
Table 3: Best parameters for each combination of classifier and descriptor (number of bins, block size).
## 2 Results and Discussion
### Figures of Merit  
#### 2.3.1 Linearity and Limits of Detection  
Linearity was evaluated by addition of standards to a model white wine at eight levels. The observed linearity was excellent for the three thiols (\(r^{2}>0.99\), Table 1). The linear range for 3-MH and 4-MMP covered the full range of standards (6-20,000 ng/L). The linear range for 3-MHA (63-6325 ng/L) was more limited, as the two lowest concentrations were at or below the detection limit, and the highest concentration interfered with the deuterated standard trace.  
Method detection limits were determined by standard addition of thiols to a model wine and are reported in Table 1. Signal-independent noise and limits of detection were calculated based on all analytical replicates using [PERSON]'s method [23]. The limits of detection for 4-MMP (0.9 ng/L) and 3-MH (1 ng/L) compared well to their respective sensory thresholds, 0.8 ng/L and 60 ng/L. The LOD for 3-MHA was somewhat higher than its sensory threshold (17 _vs._ 4.2 ng/L) although still below concentrations reported in many Sauvignon blanc wines (mean = 29-516 ng/L, depending on region [24]) as well as other white wines [3]. The high LOD of 3-MHA in comparison to those achieved for 4-MMP and 3-MH was due largely to the lack of characteristic, interference-free ions for 3-MHA-PFB.
## 5 Other Characterization of the Dielectric Gas Mixture
### Liquefaction Temperature  
The important parameter for a dielectric gas is its liquefaction temperature, which is applicable in practical applications. The liquefaction temperature is the point at which gases melt to a liquid state. At high pressure, the temperature of dielectric gas also increases, transforming the gaseous state to a liquid. The HFO-1336 mzz (E) boiling point is comparatively high, at 7.58 \({}^{\circ}\)C at 0.1 MPa pressure; hence, it cannot be employed for low-temperature applications. Therefore, it is mixed with buffer gases such as CO\({}_{2}\) and N\({}_{2}\), having low liquefaction temperatures of \(-\)79 \({}^{\circ}\)C and \(-\)196 \({}^{\circ}\)C, respectively, at 0.1 MPa pressure. The HFO-1336 mzz (E) and CO\({}_{2}\) mixture's liquefaction temperatures are analyzed. The relationship between the liquefaction temperature at room conditions \(T_{b}\), vapor pressure (P), and liquefaction temperature of the base gas in mixture \(T\) is expressed in Equation (10). \(A\) and \(R\) are gas constants with values of 87 J/mol. K and 8.3 J/mol. K, respectively.  
\[P=exp\bigg{[}\frac{A}{R}\bigg{(}1-\frac{T_{b}}{T}\bigg{)}\bigg{]} \tag{10}\]  
Figure 12: GWP of HFO-1336 mzz (E)/CO\({}_{2}\) mixture.  
Equation (11) can be modified by introducing \(m\), a mixing ratio, by considering ideal gas conditions; the HFO-1336 mzz (E) and CO\({}_{2}\) mixture's liquefaction temperature can be calculated using Equation (5) [27].  
\[T_{mb}=\frac{T_{b}}{1-\frac{\ln(10 mP)}{A/R}} \tag{11}\]  
The HFO-1336 mzz (E) and CO\({}_{2}\) mixture's liquefaction temperatures with different mixing ratios and pressure ranges, from 0.1 to 0.3 MPa, are depicted in Figure 13. Increasing the pressure and the HFO-1336 mzz (E) percentage in the mixture increases the liquefaction temperature. A similar relation is observed by increasing the pressure, which increases the liquefaction temperature. Figure 13 indicates the constraint on the choice of mixing ratio and pressure. If, for instance, a pressure of 0.1 MPa and a critical temperature of \(-20\)\({}^{\circ}\)C are required for medium-voltage application, the HFO-1336 mzz (E) mixing ratio should not exceed 30%. Similarly, for 0.2 and 0.3 MPa, the HFO-1336 mzz (E) should be within 15% and 10%, respectively.
###### Abstract
This novel fragile scheme appears to demonstrate a high payload capacity that can be used for authentication and recovery processes. To minimize the negative influence of the tampering coincidence problem, the proposed algorithm generates \(15 AB/16\) bits in total, \(A\times B\) being the size of the image. Additionally, \(AB/16\) bits should be produced to detect tampered pixels in any area of an image. Thus, the designed framework can embed three, six or nine copies of recovery and authentication watermarks, achieving a high recovery and tamper detection capability. The novel framework can be implemented using several variants for embedding the recovery and authentication watermarks, such as the _least-significant bit_ (LSB) methods: 1-LSB, 2-LSB and 3-LSB, where each one of them provides different advantages that are analyzed below. For the 2-LSB and 3-LSB embedding processes, a bit adjustment stage is performed [34] on the watermarked pixels, increasing the protected images' objective quality. A hierarchical algorithm in tamper detection is employed to achieve higher tamper detection accuracy. Additionally, an inpainting process is used to resolve the tampering coincidence problem by regenerating the eliminated blocks. For evaluating the quality of the results obtained in the numerous experiments, the _peak signal-to-noise ratio_ (PSNR) and _structural similarity index measure_ (SSIM) criteria are employed in this study. Moreover, we use a variation of the PSNR criterion, denoted as PSNR-HVS-M, which considers the _human visual system_ (HVS) and visual masking (M). This criterion employs the _contrast sensitivity function_ (CSF) and maintains a close relationship with _discrete cosine transform_ (DCT) basis functions [35]. Additionally, it has demonstrated good correspondence with human subjective visual perception. Consequently, it could be useful for the justification of the good performance of our novel system. The rest of this paper is organized as follows. Firstly, Section 2 presents a review of related works. Secondly, Section 3 describes the proposed SR-HTR, followed by Section 4, which explains the experimental setup. Section 5 shows the proposed method's analysis when the embedding and extraction processes in 1-LSB, 2-LSB and 3-LSB are used. Section 6 presents the experimental results obtained by the proposed framework and their performance comparison against state-of-the-art techniques. Finally, the study's conclusion is drawn in Section 7.
## 3 Signal Quality Index Algorithm
### Parameter Extraction  
In the study, four significant parameters were extracted from the original and smoothed PPG pulses. The four parameters included the number of zero crossing points for the differential PPG pulse, the difference ratio for the intervals of two PPG pulses, the difference ratio for the magnitudes of two smoothed pulses, and the smoothness for the peak of the smoothed pulse. These four parameters that could describe the signal quality of a specific PPG pulse were used as the input vector of the SONFIN to classify the SQIs of PPG pulses. The number of zero crossing points was found from the beginning of the differential PPG pulse to 300 ms after the main peak of the PPG pulse. Once the three continuous pulses were distinguished from the PPG signal, both Intervall and Interval2 were found, as shown in Figure 5a. In the study, the difference ratio of intervals (dI) can be computed with Equation (4):  
\[\mathrm{dI}=1-\frac{\min\{\mathrm{Interval1,\ Interval2}\}}{\max\{\mathrm{Interval1,\ Interval2}\}} \tag{4}\]  
when the magnitudes of two smoothed PPG were found, the difference ratio of magnitude (dM) is defined by Equation (5):  
\[\mathrm{dM}=1-\frac{\min\{\mathrm{M1,M2}\}}{\max\{\mathrm{M1,M2}\}}, \tag{5}\]  
where M1 is the magnitude between the levels C and D, and M2 is the magnitude between the levels C and E for the smoothed PPG signal, as shown in Figure 5b.  
Figure 4: The peak and two side valleys (Valley1 and Valley2) of the PPG pulse (solid line) have the same signs. Its smoothed PPG signal (dashed line) is same as the smoothed PPG signal without the wandering noise.  
The smoothness for the peak of the smoothed pulse was to use a mask curve according to Equation (6) to calculate two correlation coefficients between the mask curve and the points B and C of the smoothed PPG signal, respectively. The smaller correlation coefficient would be adopted as the parameter of smoothness.  
\[Y(n)=\sin(2\cdot\pi((n+74)\times 0.002)),1\leq n\leq 101 \tag{6}\]
# Electron Beam Characterization of REBCO-Coated Conductors at Cryogenic Conditions
## 2 Material and Methods
### Sample Analysis
#### 2.2.2 Composition Analysis via EDS  
The energy dispersive X-ray spectrometry analysis was performed on both studied samples to verify the surface chemical composition. EDS utilizes the X-ray spectrum emitted by a solid sample bombarded with a focused electron beam to obtain a localized chemical analysis. We identified the specific energy of the X-ray peaks characteristic for each element. The relative intensity of an X-ray line is approximately proportional to the mass concentration of an element. In a quantitative analysis, the concentrations of the  
Figure 2: SEM images of REBCO samples with the well–visible nanocrystal–covered surface. Crosses represent the EDS measurement positions. **Left**: PLD–deposited, a wide range of crystal sizes. **Right**: RCE–deposited coating, uniformly sized crystals. Details in the text. The SEM images were taken on FE–SEM Zeiss Sigma.  
elements present are determined by measuring the relative intensities for the lines identified in the spectrum for each element. The resulting concentrations of the identified elements are compiled in Table 1. However, the EDS analysis depth of \(\sim\)5 \(\upmu\)m also incorporates all buffer layers and part of the Hastelloy substrate, which offsets the composition measurement.  
For the RCE-deposited sample, the EDS spectrum was measured in an area with a nanocrystal and an area without, i.e., the substrate. The locations analyzed by EDS are marked by crosses in the insets of Figure 2. The precipitates segregated at the RCE-deposited surface may be Cu-O or Cu-Ba-O, resulting from the liquid process of RCE-DR to grow the Gd-Ba-Cu-O [31]. The PLD-deposited sample was analyzed in the same way, and the measured compositions derived from EDS spectra are listed in Table 1. Given the PLD growth method, the segregated precipitates at the surface are droplets of the same composition as the REBCO layer [32].
# Analyzing TCP Performance in High Bit Error Rate Using Simulation and Modeling
## 4 Analysis and Validation of Results
### Theoretical Maximum Throughput
#### 4.3.2 Medium (Ethernet) Limitation  
Each TCP segment sent over an Ethernet connection will have some overhead because of the lower layer protocols involved in the communication [46]. Different protocol layers include additional headers (see below).  
TCP header (20 byte): TCP header without options  
IP header (20 byte): IP header without options  
Ethernet overhead (38 byte), preamble (8 byte), CRC (18 byte), inter-packet gab (12 byte)  
Thus, a TCP segment has an overhead of 78 bytes. The expected throughput \(T_{TCP}\) over a 10 Mbit/s Ethernet connection will be:  
\[T_{TCP}=\frac{MSS}{MSS+overhead}\times T_{Ethernet} \tag{8}\]  
\[T_{TCP}=\frac{1460}{1460+78}\times 1,250,000=1,186,605.98\;\mathrm{bytes/s} \tag{9}\]  
This is significantly higher than the throughput achieved in the simulation because most TCP feathers, such as timeout, acknowledgement mechanism and retransmission, are ignored and only the protocol overhead is considered.
## 4 Results and Discussion
### Seasonal Correlations Between Air Pollutants and Meteorological Parameters
RainF emerged as a key factor in pollutant reduction, showing a consistent negative correlation with PM\({}_{10}\) across all seasons, with the strongest effects in winter (r = \(-\)0.48) and spring (r = \(-\)0.46). This underscores RainF's effectiveness in particulate matter removal via wet deposition. For NO, a weak positive correlation was observed in summer (r = 0.20), likely due to localized emission patterns, while its influence on NO\({}_{2}\) and CO was negligible. The analysis highlights WS and RainF as the most influential meteorological parameters in reducing PM\({}_{10}\) and NO concentrations, particularly in colder and wetter seasons. Meanwhile, temperature and RH exhibited complex, pollutant-specific effects that varied by season. These findings emphasize the necessity of incorporating seasonal meteorological data into air quality management strategies. By addressing pollutant-specific challenges and adapting to seasonal variations, more effective mitigation measures can be implemented, improving air quality in urban and industrial regions.
## 4 Results
### Hypothesis Testing
#### 4.3.3 Additional Multivariate Analysis: Linear Regression  
In order to go beyond bivariate correlations and to evaluate the joint contribution of each omnichannel dimension, a multiple linear regression was performed with Customer Satisfaction as the dependent variable, and Data, Omnichannel Payment, and Logistics as predictors. The model yielded an adjusted R\({}^{2}\) of 0.002, indicating that less than 1% of the variance in satisfaction was explained by the predictors collectively. None of the variables reached statistical significance: Data (\(\beta=0.060\), \(p=0.559\)), Omnichannel Payment (\(\beta=-0.066\), \(p=0.642\)), and Logistics (\(\beta=-0.349\), \(p=0.103\)). Despite the weak correlations observed earlier, this result suggests a substantial interdependence between dimensions and potentially omitted variables influencing customer satisfaction. These findings imply that, in this emerging market context, the observed associations are not sufficient to assert predictive power, and a more robust mixed-methods approach or larger sample may be required to draw inferential conclusions (Table 15).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Predictor** & \(\beta\) & **Std. Error** & **t** & \(p\)**-Value** \\ \hline Constant & 25.877 & 2.890 & 8.955 & 0.000 \\ Data & 0.060 & 0.102 & 0.586 & 0.559 \\ Omnichannel & \(-0.066\) & 0.142 & \(-0.467\) & 0.642 \\ Payment & \(-0.349\) & 0.212 & \(-1.644\) & 0.103 \\ \hline \hline Adjusted R\({}^{2}\) = 0.002; F(3,104) = 1.05; \(p=0.372\). & & & \\ \hline \hline \end{tabular}
\end{table}
Table 15: Linear regression results—predicting customer satisfaction.  
Figure 5: Scatter plot of omnichannel logistics and satisfaction.
# Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things
## 3 Methodology
### Outline of the Approach  
This study advocates a method of video compression in which each consecutive frame of a video is preprocessed, as the basis of making a decision regarding whether the video information at that time shall, after compression, be sent or not. That is to say, it decides whether to transmit the compressed video data from an originating IoMT device across the IoMT network. A difference frame is used to establish whether any meaningful information is present or not at any one moment in time. Based on that information, reduction of data can be achieved through motion detection. By reducing the video content, the method not only avoids storage problems, given the limited memory sizes of typical IoMT devices, but it also enables efficient processing and secure streaming of the compressed video data, preserving the video quality, while not omitting any significant information contained in the acquired video frames.
## 2 Materials and Methods
### Development of Models
#### 2.5.5 Gradient Tree Boosting  
Gradient Tree Boosting (GTB) is also a boosting method. It builds an additive model in a forward stage-wise fashion allowing the optimization of a differentiable loss function. The existing residuals are used to build new trees sequentially. In each stage, a regression tree is added and fitted on the negative gradient of the loss function, reducing its value and improving the prediction. The prediction \(\hat{y}_{i}\) for a given input \(x_{i}\) is of the form:  
\[\hat{y}_{i}=F_{M}(x_{i})=\sum_{m=1}^{M}h_{m}(x_{i}) \tag{6}\]  
where \(h_{m}\) are the weak learners. The constant M corresponds to the number of estimators. GTB is built as follows:  
\[F_{m}(x)=F_{m-1}(x)+h_{m}(x) \tag{7}\]  
where the added tree \(h_{m}\) is fitted in order to minimize a sum of losses \(L_{m}\), given the previous ensemble \(F_{m-1}\):  
\[h_{m}=arg\ min_{h}L_{m}=arg\ min_{h}\sum_{i=1}^{n}l(y_{i},\ F_{m-1}(x_{i})+h(x_ {i})) \tag{8}\]  
where \(l[y_{i},F_{m-1}]\) is the loss function measuring how much the predicted value \(F(x)\) differs from the true value \(y\). Trees will be built, and each iteration must satisfy the above equation. The initial model \(F_{0}\) is chosen as the constant that minimizes the loss. Using a first-order Taylor approximation, the value of 1 can be approximated as:  
\[l(y_{i},\ F_{m-1}(x_{i})+h_{m}(x_{i}))\approx l(y_{i},\ F_{m-1}(x_{i}))+h_{m}( x_{i})\bigg{[}\frac{\partial l(y_{i},F(x_{i}))}{\partial F(x_{i})}\bigg{]}_{F=F _{m-1}} \tag{9}\]  
Removing the constant terms,  
\[h_{m}\approx arg\ min_{h}\sum_{i=1}^{n}h(x_{i}) \tag{10}\]  
This is minimized if \(h(x_{i})\) is fitted to predict a value that is proportional to the negative gradient. Therefore, at each iteration, the estimator \(h_{m}\) is fitted to predict the negative gradients of the samples. The gradients are updated at each iteration. This can be considered as a form of gradient descent in a functional space.  
Regularization techniques are usually applied during training to reduce overfitting and improve the generalization of the model. A regularization strategy that scales the contribution of each weak learner by a constant factor \(\
u\) is the following [72]:  
\[F_{m}(x)=F_{m-1}(x)+vl_{m}(x) \tag{11}\]The parameter \(\
u\) is called the learning rate because it scales the step length of the gradient descent procedure.  
Stochastic Gradient Boosting combines gradient boosting with bootstrap averaging (Bagging) [73]. At each iteration the base estimator is trained on a fraction subsample of the available training data. The subsample is drawn without replacement.
## 9 Discussion
### Challenges, Open Issues, and Future Research Directions for EIAVs
security protocols within AVs is essential. Therefore, future research must concentrate on analyzing the inter-relations between safety and security measures to ascertain their coordinated efficiency. * Engagement with non-automated road participants: In mixed traffic conditions, AVs need to effectively communicate and cooperate with both automated and non-automated road users, such as traditional vehicles, bicycles, and pedestrians. Gaining insight into and making predictions about human behavior in mixed traffic situations is difficult but vital for ensuring safety. There is an urgent need for research that improves our grasp of the interactions between humans and AVs in these contexts. * Safeguarding CAN bus communications: The Controller area network (CAN) bus, which handles sensor data transmission, is exposed to potential attacks. In the absence of security measures like encryption, vital mission-planning data can be exposed and at risk. Future investigations should focus on strategies for effectively safeguarding CAN bus communications to maintain data integrity and authenticity. * Adapting to new attack methods: As technology keeps advancing, so do the methods used in cyber attacks. Future research needs to stay one step ahead by trying to predict the new ways that attackers might attempt to breach security and by developing strategies to prevent these attacks before they can happen. * Formulation of holistic standards and regulations: The absence of universal standards for the safety and security of AVs presents a considerable obstacle. The creation and enforcement of international norms would supply a uniform framework to steer the development of secure and reliable AV systems, consequently bolstering the overall security stance of AVs. * Addressing machine learning weaknesses: Machine learning, especially deep learning, is a cornerstone of AV technology but is vulnerable to targeted attacks. Future studies should consider the development of solid strategies to shield machine-learning algorithms in AVs from these potential incursions.