esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Alcohols  
A total of six alcohols were detected: (E)-3-hexen-1-ol (B1), 2-hexen-1-ol, (E)- (B2), hexanol (B3), octanol (B4), 1-nonanol (B5), and 1-undecanol (B6) (Figure 3, Tables 2 and 3). The prominent alcohols were (E)-3-hexen-1-ol (B1), followed by hexanol (B3) and 2-hexen-1-ol, (E)- (B2) (Figure 3). Alcohols and aldehydes are also known as C6 compounds and are mostly known to exhibit \"grassy flavor\" or \"unripe aroma\". They are prominent in immature peaches, and they decrease as the peach ripens [8; 17]. In this study, C6 compounds were detected in certain peach cultivars, with some of them having high concentrations (Table 3). \"Fiesta Gem\" had the highest (E)-3-hexen-1-ol 925.6 (ng:g\({}^{-1}\)) among the cultivars (Table S1). Compounds (E)-3-hexen-1-ol and 1-undecanol relative concentrations were the highest for early-season cultivars in comparison to late- and mid-season cultivars. (E)-3-Hexen-1-ol is characterized by a \"green\" odor and 1-undecanol by \"waxy\", \"citrus\", and \"mandarin\" odors (Flavornet and the Good Scents Company databases). 1-Nonanol was the lowest for late-season cultivars in comparison with early- and mid-season (Table 3). 1-Nonanol is characterized as \"fat\" and \"green\" odor (Flavornet database); and \"floral\" odor and \"waxy\" flavor (Good Scents Company database). The relative total concentration of alcohols per cultivar were almost triple in early-season cultivars in comparison with the mid- and late-season cultivars (Figure 1). This result needs to be further studied as it may represent differences in odor and flavor possibly associated with fruit development period, and that may influence consumer perception.
# Acute Effect of a Single Functional Neurology Intervention on Muscular Trigger Point
## 3 Results  
The independent samples \(t\)-test results indicated no significant differences between the groups for age (t = \(-\)0.289, \(p\) = 0.773, mean difference = \(-\)0.59091, 95% CI [\(-\)4.65390, 3.47208]), height (t = 0.004, \(p\) = 0.997, mean difference = 0.00758, 95% CI [\(-\)4.13425, 4.14940]), weight (t = \(-\)0.137, \(p\) = 0.891, mean difference = \(-\)0.41061, 95% CI [\(-\)6.35844, 5.53723]), and BMI (t = \(-\)0.209, \(p\) = 0.835, mean difference = \(-\)0.15424, 95% CI [\(-\)1.62012, 1.31164]) (Table 1).  
The comparison of pre- and post-intervention scores between the experimental and control groups revealed significant differences. The experimental group showed a significant increase in the pressure pain threshold (PPT) post-intervention (t = \(-\)11.74, \(p\) < 0.001, d = 1.25, 95% CI [1.89, 3.52]), indicating a large effect size. In contrast, the control group exhibited no meaningful change (t = 1.09, \(p\) = 0.282, d = 0.05, 95% CI [\(-\)0.43, 0.52]), suggesting that the improvements observed in the experimental group were directly attributable to the intervention (Table 2). The between-group comparisons revealed a statistically significant difference in post-intervention outcomes, indicating that the functional neurology intervention had a measurable impact beyond natural variation or placebo effects. The control group did not show significant changes, reinforcing the validity of the intervention's effects.  
Between-group comparisons for the pressure pain threshold (PPT) showed a significant post-intervention difference (t = 3.931, \(p\) < 0.001), with the experimental group demonstrating an increase in PPT while the control group showed negligible changes. Peripheral vascular response measures also exhibited significant improvements in the experimental group, confirming the effectiveness of the intervention (Table 3).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Experimental Group (\(n\) = 30)** & **Control Group (\(n\) = 33)** & \(p\)**-Value** \\ \hline Age (years) & 29.5 \(\pm\) 8.8 & 30.1 \(\pm\) 10.5 & 0.773 \\ Height (cm) & 173.9 \(\pm\) 9.2 & 173.9 \(\pm\) 10.4 & 0.997 \\ Weight (kg) & 74.3 \(\pm\) 14.1 & 74.7 \(\pm\) 13.2 & 0.891 \\ BMI (kg/m\({}^{2}\)) & 24.4 \(\pm\) 3.5 & 24.6 \(\pm\) 3.1 & 0.835 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Demographic characteristics of participants.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Group** & **Pre-Evaluation** & **Post-Evaluation** & \
\begin{tabular}{c} **\% of** \\ **Change** \\ \end{tabular} & **t** & \(p\) & **Cohen’s d** &
\begin{tabular}{c} **95\% CI (Mean** \\ **Difference**) \\ \end{tabular} \\ \hline Experimental & 5.6 \(\pm\) 1.8 & 8.2 \(\pm\) 3.2 & 46.4 & \(-\)11.740 & 0.000 & 1.25 & [1.89, 3.52] \\ Control & 5.8 \(\pm\) 2.0 & 5.9 \(\pm\) 1.8 & 1.7 & 1.095 & 0.282 & 0.05 & [\(-\)0.43, 0.52] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Modification in pressure pain threshold in experimental and control groups in pre- and post-evaluation moments.
# Remote Sensing of Forest Burnt Area, Burn Severity, and Post-Fire Recovery: A Review
###### Abstract  
Wildland fires dramatically affect forest ecosystems, altering the loss of their biodiversity and their sustainability. In addition, they have a strong impact on the global carbon balance and, ultimately, on climate change. This review attempts to provide a comprehensive meta-analysis of studies on remotely sensed methods and data used for estimation of forest burnt area, burn severity, post-fire effects, and forest recovery patterns at the global level by using the PRISMA framework. In the study, we discuss the results of the analysis based on 329 selected papers on the main aspects of the study area published in 48 journals within the past two decades (2000-2020). In the first part of this review, we analyse characteristics of the papers, including journals, spatial extent, geographic distribution, types of remote sensing sensors, ecological zoning, tree species, spectral indices, and accuracy metrics used in the studies. The second part of this review discusses the main tendencies, challenges, and increasing added value of different remote sensing techniques in forest burnt area, burn severity, and post-fire recovery assessments. Finally, it identifies potential opportunities for future research with the use of the new generation of remote sensing systems, classification and cloud performing techniques, and emerging processes platforms for regional and large-scale applications in the field of study.  
forest fire; remote sensing; burnt area; forest ecosystems; burnt severity; post-fire forest recovery; meta-analysis 2022
## 3 Materials and Methods
### Data Preparation
#### 3.3.1 Feature Extraction  
To apply the ML models on DS2, many features were extracted as per previous studies and expert suggestions. As a result, the following well-known features were chosen for this study: contrast, correlation, energy, homogeneity, entropy, minor axis length, major axis length, orientation, eccentricity, mean, variance, standard deviation, skewness, kurtosis, minimum value, maximum value, autocorrelation mean, autocorrelation standard deviation, autocovariance mean, autocovariance standard deviation, median absolute deviation, root mean square, interquartile, etc. These shape and texture features were extracted from GLCM matrix images followed by LBP [62,63]. On the other hand, DL was applied to IDCBS10, in which the feature extraction technique was different. The convolutional operation was used throughout the training process to automatically extract the features for the DL models (CNN) [40,64,65].
## 4 Results
### Optimal Configurations for Different Models Across Different Performance Metrics  
This section explains the optimal hyperparameter configurations for all the trained models across seven key performance metrics. The best configurations for each metric, identified through extensive hyperparameter tests, are summarized in Tables 3-9.  
Figure 5: _Cont_.  
ResNet101 and EfficientNet-B2 follow with accuracies of 0.975223 and 0.973241, respectively, both leveraging the SGD optimizer with a learning rate of 0.01, StepLR scheduler, and CrossEntropy loss. DenseNet121, using SGD with identical settings, achieves 0.971259, benefiting from its dense connectivity, which enhances feature reuse and gradient flow.  
MobileNetV3 secures an accuracy of 0.970268 with SGD (learning rate 0.01), CosineAnnealingLR, and FocalLoss. ConvNeXt attains 0.969277 with Adam (learning rate 0.0001), CosineAnnealingWarmRestarts, and CrossEntropy, where the cyclical learning rate restarts potentially aid in escaping local minima. ViT-B-16, with an accuracy of 0.96333, uses SGD (learning rate 0.001), ReduceLROnPlateau, and CrossEntropy, suggesting that transformer-based models may require adaptive scheduling to optimize performance.  
Figure 5: Comparative visualization of “collision” and “non-collision” prediction results from the seven top-performing models, each selected as the highest-accuracy variant of its respective architecture, under their optimal configurations.  
Figure 6: Confusion matrices for the seven top-performing models, each selected as the highest-accuracy variant of its architecture, displaying true versus predicted “collision” and “non-collision” classifications, ordered by decreasing accuracy. (**a**) ResNet50 (AdamW, 0.0001, CosineAnnealingLR, FocalLoss); (**b**) ResNet101 (SGD, 0.01, StepLR, CrossEntropy); (**c**) Efficient-B2 (SGD, 0.01, StepLR, CrossEntropy); (**d**) Desnet (SGD, 0.01, StepLR, CrossEntropy); (**e**) MobileNet (SGD, 0.01, CosineAnnealingLR, FocalLoss); (**f**) ConvexNet (Adam, 0.0001, CosineAnnealingWarmRestarts, CrossEntropy); (**g**) ViT-B16 (SGD, 0.001, ReduceLROnPlateau, CrossEntropy).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Optimizer** & **Learning Rate** & **Scheduler** & **Loss Function** & **Accuracy** \\ \hline ResNet50 & AdamW & 0.0001 & CosineAnnealingLR & FocalLoss & 0.978196 \\ ResNet101 & SGD & 0.01 & StepLR & CrossEntropy & 0.975223 \\ EfficientNet-B2 & SGD & 0.01 & StepLR & CrossEntropy & 0.973241 \\ DenseNet121 & SGD & 0.01 & StepLR & CrossEntropy & 0.971259 \\ MobileNetV3 & SGD & 0.01 & CosineAnnealingLR & FocalLoss & 0.970268 \\ ConvNeXt & Adam & 0.0001 & CosineAnnealingWarmRestarts & CrossEntropy & 0.969277 \\ ViT-B-16 & SGD & 0.001 & ReducelROnPlateau & CrossEntropy & 0.96333 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimal hyperparameter configurations for maximizing classification accuracy across deep learning models.
## 6 Experimental Verification
### On-Machine Machining Error (Diameter Error) Inspection  
In this experiment, the on-machine machining error auto-inspection function was applied to the production line of GlobalTek Co. (Taoyuan, Taiwan) to verify the effectiveness of the inspection function. A total of 7643 components were tested in this experiment. The inspection results made by the auto-inspection function were compared to that made using the air gauge instruments, which were originally used by the company. The threshold value of the inspection was set to \(\pm\)5 \(\upmu\)m. Of the produced components, 93% were qualified, and 7% of the produced components were inspected as No-Go parts. The results were very close to the
## 2 Related Work
The data are extracted from Twitter and annotated using TextBlob, while TF-IDF and BoW features are used for machine learning models. Results indicate that the ETC models achieve the best performance with BoW features and Textblob. Keeping in view the superior performance of deep learning models, several studies adopt deep learning models for sentiment classification. For example, Ref. [26] uses deep learning and NLP tools to determine how people feel about the COVID-19 vaccination in the UK (United Kingdom) and the US (United States). The data are collected from Facebook and Twitter using various COVID-19 and vaccine-related keywords. Afterward, the data are preprocessed and two lexicon-based techniques including VADER and Text Blob are applied for sentiments. The study shows that average positive, negative, and neutral emotions in the UK are better than in the US. Similarly, the study [27] analyzes the articles about the emergence of infectious diseases such as COVID-19 and MERS (Middle East Respiratory Syndrome) pandemics, etc., and analyze the main findings. The study discusses the classification models, lexicon-based approaches, and machine learning approaches--both individual and hybrid--as well as the application language. The authors perform sentiment analysis on tweets related to COVID-19 in [28] using deep learning models. A multi-layer LSTM (Long Short Term Memory) model is proposed for the classification of sentiment polarity and emotions. The study [29] uses a deep learning approach for COVID-19 tweets' sentiment analysis. It leverages LSTM and BERT (Bidirectional Encoder Representations from Transformers) models for sentiment classification. BERT achieves an 89% accuracy while LSTM achieves only 65% accuracy for sentiment classification. Research findings indicate that the knowledge process is not anticipated; rather, it is viewed as a last-minute learning technique [30]. To understand the need of the hour, many schools have started online courses. Almost everywhere there are two major issues; e-learning has little effect and learning through digital platforms is not as effective as traditional teaching methods are in achieving learning goals and focusing on educational priorities [31]. Table 1 provides a comprehensive summary of the discussed related works.
# Maintaining Secure Level on Symmetric Encryption under Quantum Attack
## 4 Programming and Testing
### Compatibility of Rijndael  
Table 13 compares the proposed AES-384 and AES-512 with the original AES function of the paper [15].  
The experimental results include three methods: the method proposed in this article, the method of Rijndael, and [15]. The proposed method and the Rijndael method can reach FIPS security levels I, III, and V under normal computer attacks, while [15] is a special architecture with a minimum key length of 512 bits, such that it cannot support any level other than level V. Under the FIPS security level of a quantum computer attack, the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Proposed** & **Rijndael** & **[PERSON]’d [15]** \\ \hline Security level & I, III, V & I, III, V & X \\ \hline Security Level under & & & \\ quantum calculation & I, III, V & I & V \\ \hline AES-128 & O & O & X \\ \hline AES-192 & O & O & X \\ \hline AES-256 & O & O & X \\ \hline AES-384 & O & X & X \\ \hline AES-512 & O & X & O \\ \hline \hline \end{tabular}
\end{table}
Table 13: Compatibility comparison.  
Figure 6: AES-512 key expansion simulation.  
Figure 7: AES All versions simulation result.  
proposed method can still maintain level I, III, and V, while Rijndael can only maintain level I, and [15] can only maintain level V.  
Finally, the authors' scheme obtains the calculation function of AES-128/192/256/384/512 bits, Rijndael obtains the calculation function of AES-128/192/256 bits, and [15] only obtains the calculation function of AES-512 bits.
## 4 Conclusions  
In summary, this study focuses on the monitoring application of LIF in petroleum hydrocarbon-contaminated soils by establishing the three-dimensional diffusion models. The spectra obtained from diesel oil are complex and unique, which peak intensity can represent the concentration of diesel oil, and the square correlation coefficient value of the calibration curve is 0.99. There is little difficulty in obtaining high-quality spectrum quickly, typically less than 1 min, which is a benefit to monitoring the soil quality.  
The longitudinal penetration spectra and the lateral diffusion spectra of diesel oil in soils were detected and the laws of permeation and distribution were analyzed. These laws indicate that the soil moisture can extend the diesel oil pollution to the soil. The three-dimensional diffusion models in different soil samples were established and the polluted volumes of soils by diesel oil were calculated. The analytical method employed in this paper can be extended to other petroleum-polluted soils. Therefore, it is feasible to develop the LIF technique for the rapid analysis of petroleum hydrocarbon-contaminated concentrations and distributions in high precision, and it is an effective path to improve the efficiencies and reduce the costs of soil pollution treatment.  
Writing--original draft preparation, Y.G.; data curation, Z.Z.; resources, C.S.; investigation, X.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Anhui Province Natural Key Science and Technology Projects (KI2018A0547), and the Talent Research Foundation of Hefei University (18-19 RC47, 18-19 RC38).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Soil Moisture** & **The Contaminated Soil Volume** & **The Calculated Amount of Diesel Oil (g)** & **RSD (\%)** \\ \hline
0\% & 233.90 cm\({}^{3}\) & 5.57 & 5.09 \\
5\% & 332.70 cm\({}^{3}\) & 5.81 & 9.62 \\
10\% & 660.05 cm\({}^{3}\) & 5.72 & 7.92 \\ \hline \end{tabular}
\end{table}
Table 2: The contaminated soil volumes by diesel oil in different soil moistures.  
Figure 10: The three-dimensional diffusion models of diesel oil in different humidity soils.
## 2 Results and Discussion
### Consumer Evaluation of Celery Samples
Both clusters expressed no significant correlation with any flavour or odour attributes and preferred the celery that expressed low relative abundance of the volatile compounds (Table 1). For this reason, genotype 12 was the most disliked celery sample for clusters 1 and 2. Genotype 12 expressed a high relative abundance of volatile compounds (Table 1) in addition to scoring significantly higher in grass/green flavour (Table 2). Ribbed appearance (RA), grass/green aroma (GGA), bitter taste (BT) and fresh parsley aroma and flavour (FPA and FPF) were attributes positively correlated with this genotype. Clusters 1 and 2 displayed similar overall liking scores in comparison to cluster 3. However, observed in the bottom right quadrant there appears to be a 'gap' where none of the clusters are placed (Figure 3) yet genotype 22 and hybrids 22x12 and 12x22 are positioned there. Although no cluster were associated with these hybrids, the consumers that are situated there displayed preference to celery that expressed a fresh fennel flavour and aroma accompanied by a soapy aftertaste. Hybrid 25x12 was the closest match to the highest proportion of consumers that were grouped into clusters 1 and 2. However, the hybrid requires further development with particular focus on the moist mouthfeel, stalk thickness and sweet taste attributes. These attributes are the drivers of liking for 82% of  
Figure 3: Internal preference map of six celery samples. Sensory attributes and consumer cluster means were regressed onto the consumer preference matrix generated by PCA. Blue squares—sensory attributes, codes correspond to those in Table 2. Green squares—clusters 1, 2, 3, mean liking positions of three clusters from AHC (Table 6). Red circles: overall liking scores of each consumer. the consumers in this study. On the other hand, the drivers of liking for those consumers placed in cluster 3 (18%) were grassy flavour and bitter taste. Penalty analysis was used to relate Just-About-Right (JAR) data to liking scores and explain drivers of overall liking in relation to aroma, sweetness, bitterness, flavour and stringiness intensity and the results are presented in Table 7.  
When the attributes are not at the optimum intensity for a consumer this may influence the overall liking. Sweetness was ranked by the consumers as the second most important characteristic, and this was reflected in Table 7, whereby for all genotypes and hybrids, there was a negative impact on the overall liking when the sweetness of the samples was considered too low. This agreed with over 50% of the consumers in all samples. On the other hand, there was a significant drop in the liking of all samples when the bitter taste intensity was \"too much\" by the consumers with the genotypes 12 and 22 perceived the most bitter and genotype 25 the least bitter.
## 5 System Model
### THz Channel Model  
The THz channel gain is represented by  
\[H_{THz}=h_{p}\cdot h_{a}\cdot h_{m}, \tag{3}\]  
where \(h_{p}\), \(h_{a}\), and \(h_{m}\) denote the free-space path loss, molecular absorption loss, and misalignment fading effect, respectively.
# Service for Deploying Digital Twins of QKD Networks
## 5 Validation and Results
### Performance Evaluation  
To understand better the efficiency of our prototype, in terms of key exchange time and discarded qubits in the formation of a key, a performance evaluation has been conducted. This experiment was run over the same scenario as the previous experiment, shown in Figure 8.  
In this evaluation, the application creates a key stream between neighboring modules, where the E91 protocol is implemented to form keys. It waits until 20 keys have been exchanged and retrieves them to get some information about the exchange times and number of discarded qubits per key. This test has been executed for different key lengths, in particular, 64, 128, 256, and 512-bit length keys have been used. The results of the evaluation are shown in Figure 10a,b.  
Figure 8: Scenario scheme of the functional validation and the tests regarding key exchange time and discarded qubits.  
The exchange time of a key increases linearly with the key size (note that the separations in the horizontal axis are not incremented linearly), increasing from as little as 20 s for a 64-bit key, to 2 min for a 512-bit key. However, these times do not necessarily match real-life times.  
In the case of the number of discarded qubits, it also grows linearly for the key size (note again the separation in the horizontal axis), which was the expected result since, in our implementation of the E91 protocol, the average number of discarded qubits is 2/3. In the implemented protocol, [PERSON] and [PERSON] use the same set of three bases. Consequently, there are 9 possible basis combinations, and only in 1/3 of those combinations [PERSON] and [PERSON] choose the same measurement basis. Therefore, the number of qubits that can be used to create the key is approximately 1/3 of the total created qubits.That is exactly what can be seen in the bar plot of Figure 10b.  
Figure 10: _Cont._  
Figure 9: Sequence diagram of the steps followed in the functional validation.