esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In the present study, HS-SPME/GC-MS and principal component analysis were used to investigate the influence of freezing and different drying methods (oven-drying at 45 \({}^{\circ}\)C and 60 \({}^{\circ}\)C, freeze-drying, microwave-drying, shade air-drying) for the production of strawberry powders with desirable aroma, which is one of the most important quality attributes. In addition, the volatile profiles of some commercial strawberry jams were analyzed. A total of 165 compounds were identified, accounting for 85.03-96.88% of the total headspace compositions. Our study showed that freezing and different drying methods exerted different influences on types and the content of volatile compounds of strawberry, and distinct separations between the different strawberry treatments were generated. The results and PCA revealed that the most remarkable representative differential volatiles were ethyl hexanoate, hexyl acetate, (\(E\))-2-hexenyl acetate, mesifurane, (\(E\))-nerolidol, \(\gamma\)-decalactone, 1-hexanol, and acetoin. The PCA and obtained results showed that shade air-dried, frozen, freeze-dried, and oven-dried 45 \({}^{\circ}\)C samples retained more of the fruity and sweet aromas of strawberry fruits, representing more than 68% of the total aroma intensity according to the literature. In contrast, the microwave-drying method showed a drastic loss of fruity esters volatiles, while the oven-dried 60 \({}^{\circ}\)C sample demonstrated intermediate loss. Strawberry jams demonstrated almost a complete destruction of esters and alcohols in most jam samples, while terpen were significantly increased; in addition, off-flavor and heat-induced volatiles, such as furfural and 5-hydroxymethylfurfural, were generated. These findings help better understand the aroma of strawberry and provide a guide for the effects of drying, freezing, and jam processing. Each drying process may be more suitable for theproduction of a specific targeted flavor food ingredient, as a distinct volatile composition was observed in each strawberry powder. The produced powders could be added to different food products such as baking and dairy products, to improve their functional value by adding desirable strawberry flavor.  
Conceptualization, D.A. and A.M.M.M.; resources, S.V.; data curation, D.A., A.M.M., S.A. and G.B.; writing--original draft preparation, A.M.M. and D.A.; writing--review and editing, G.C., G.S. and S.V.; supervision, G.C., G.B., G.S., F.M. and S.V.; funding acquisition, S.V. All authors have read and agreed to the published version of the manuscript.  
The present work is part of the research activities developed within the project \"PLASMA-FOOD-Study and optimization of cold atmospheric plasma treatment for food safety and quality improvement\" founded by MIUR-Ministero dell'Istruzione dell'Universita e della Ricerca-PRIN: Progetti di Ricerca di Rilevante Interesse Nazionale, Bando 2017, Prot. 201772 NELM.  
Not applicable.  
The authors declare no conflict of interest.  
The samples of dried plant materials are available from authors upon the reasonable request.
## 1 Introduction  
In contemporary society, the prevalence of irregular sleep patterns and insomnia due to excessive studying, 24 h shift work, workplace demands, and frequent international travel is on the rise. According to the National Institutes of Health in the United States, the prevalence of sleep disorders in the American population is 14.71%, leading to a productivity loss valued at USD 50 billion due to work-related slowdowns, in addition to USD 16 billion spent on health care costs related to drowsy driving and insomnia [1]. Disturbances in sleep functionality can result in decreased quality of life, diminished concentration, frequent illnesses and headaches, muscle pain, psychiatric issues, and an increase in mortality and sudden death [2]. Sleep apnea is a condition characterized by airway blockage during sleep, leading to symptoms such as snoring, episodes of apnea during sleep, and unstable respiration. With the recent rise in obesity and an aging population, the rate of sleep apnea is steeply increasing, necessitating further research into the condition. A diagnosis of sleep apnea requires a polysomnography test in a medical facility, which patients find burdensome because of the high costs and unfamiliar environment [3]. Moreover, it is practically challenging to refer all patients suspected of having sleep apnea for testing, making alternative pre-screening tools such as the Berlin and STOP-BANG questionnaires crucial [4].  
Of these, the STOP-BANG questionnaire is highly regarded for its use in the initial assessment of patients suspected of having sleep apnea, as well as for evaluating the risk of sleep apnea in patients scheduled for emergency surgery or those who are unable to undergo immediate polysomnography. Despite its high sensitivity, the STOP-BANG questionnaire's low specificity has been deemed problematic [5; 6]. This concern was highlighted by the research team led by [PERSON] at the Mayo Clinic, who questioned the questionnaire's sensitivity for obstructive sleep apnea (OSA) screening [7]. This study was dedicated to evaluating the accuracy of the STOP-BANG questionnaire by analyzing data on sleep apnea outcomes. Furthermore, it aimed to compare the outcomes of various classification models designed to improve specificity. In doing so, this study assessed a machine learning model that demonstrated both elevated sensitivity and specificity.
# Demystifying DFT-Based Harmonic Phase Estimation, Transformation, and Synthesis
## 3 An Interpretable Time-Shift Invariant Harmonic Phase Model
### NRD Estimation Examples Based on the Sawtooth Wave Derivative
\[\frac{d}{dt}x(t) = \sum_{\ell=0}^{L-1}A_{\ell}(\ell+1)\frac{2\pi}{T_{0}}\cos((\ell+ 1)\Omega_{0}t) \tag{34}\]1 Overlay representation of magnitude spectra (**top**), and unwrapped NRD vectors (**bottom**) of a sawtooth derivative signal that is FM modulated (2.5 Hz deviation around the mean) and whose SNR is 10 dB.
## 3 Hybrid FE-SEA Modeling Parameter Identification Algorithm
### Derivation of the Energy Response of Random Subsystems of Three-Coupled Systems
#### 3.2.2 Energy Response of Multiple Coupled Random Subsystems Considering Power Flow  
In engineering practice, the power flow of stochastic subsystem 2 and stochastic subsystem 1 is generally not negligible. A three-coupled hybrid model considering power flow is shown in Figure 4.  
In a three-coupled system, the power-flow relationship between the random subsystems can be expressed as:  
\[P_{2\to 1}=\omega\eta_{21}^{(A)}E_{2} \tag{55}\]  
\[P_{in,2}+P_{1\to 2}=P_{diss,2}+P_{2\to 1} \tag{56}\]  
where \(P_{in,2}\) is the input power of subsystem 2, \(P_{1\to 2}\) is the power transmitted from subsystem 1 to subsystem 2, \(P_{2\to 1}\) is the power transmitted from subsystem 2 to subsystem 1, and \(P_{diss,1}\) and \(P_{diss,2}\) are the power lost from subsystem 1 and subsystem 2, respectively. The following relationship is satisfied:  
\[P_{diss,1}=\omega\eta_{1}E_{1} \tag{57}\]  
\[P_{diss,2}=\omega\eta_{2}E_{2} \tag{58}\]  
\[P_{1\to 2}=\omega\eta_{12}^{(A)}E_{1} \tag{59}\]  
\[P_{2\to 1}=\omega\eta_{21}^{(A)}E_{2} \tag{60}\]  
Figure 4: Power balance of FE-SEA subsystem under power-flow conditions.  
In the above equation, \(\eta_{12}^{(A)}\) and \(\eta_{21}^{(A)}\) are coupling loss factors in the classical SEA theory. Such factors are different from the effective coupling loss factor and can be obtained by combination with the energy-balance Equation (54):  
\[P_{in,dir}^{ext}+P_{in,1}+\sum\limits_{k=1}^{2}P_{2\to 1}+\sum\limits_{k=1}^{2}P_{ trans,2\to 1}=P_{ trans,1\to 3}+\sum\limits_{k=1}^{2}P_{ trans,1\to 2}+\sum\limits_{k=1}^{2}P_{1\to 2}+P_{diss,1} \tag{61}\]  
Equation (61) can be rewritten as:  
\[P_{in,dir}^{ext}+P_{in,1}+\sum\limits_{k=1}^{2}\omega\eta_{21}^{(A)}E_{2}+\sum \limits_{k=1}^{2}\omega\eta_{12}\eta_{1}\frac{E_{2}}{n_{2}}=\omega\eta_{3,1}E_ {1}+\sum\limits_{k=1}^{2}\omega\eta_{21}\eta_{2}\frac{E_{1}}{n_{1}}+\sum \limits_{k=1}^{2}\omega\eta_{12}^{(A)}E_{1}+\omega\eta_{1}E_{1} \tag{62}\]  
\[\omega\Bigg{[}(\eta_{1}+\eta_{3,1})E_{1}+\sum\limits_{k=1}^{2}\Big{(}\eta_{21 }\eta_{2}+\eta_{12}^{(A)}n_{1}\Big{)}\frac{E_{1}}{n_{1}}-\sum\limits_{k=1}^{2 }\Big{(}\eta_{12}\eta_{1}+\eta_{21}^{(A)}n_{2}\Big{)}\frac{E_{2}}{n_{2}}\Bigg{]} =P_{in,dir}^{ext}+P_{in,1} \tag{63}\]
# Application of Directed Energy Deposition-Based Additive Manufacturing in Repair
###### Abstract  
In the circular economy, products, components, and materials are aimed to be kept at the utility and value all the lifetime. For this purpose, repair and remanufacturing are highly considered as proper techniques to return the value of the product during its life. Directed Energy Deposition (DED) is a very flexible type of additive manufacturing (AM), and among the AM techniques, it is most suitable for repairing and remanufacturing automotive and aerospace components. Its application allows damaged component to be repaired, and material lost in service to be replaced to restore the part to its original shape. In the past, tungsten inert gas welding was used as the main repair method. However, its heat affected zone is larger, and the quality is inferior. In comparison with the conventional welding processes, repair via DED has more advantages, including lower heat input, warpage and distortion, higher cooling rate, lower dilution rate, excellent metallurgical bonding between the deposited layers, high precision, and suitability for full automation. Hence, the proposed repairing method based on DED appears to be a capable method of repairing. Therefore, the focus of this study was to present an overview of the DED process and its role in the repairing of metallic components. The outcomes of this study confirm the significant capability of DED process as a repair and remanufacturing technology.  
additive manufacturing; directed energy deposition; repairing; remanufacturing +
Footnote †: journal: Applied Chemistry and Chemical Physics
## 4 Discussion
### Crop Traits Retrieval  
Crop traits retrieval based on RTMs is a key step towards developing a reliable CYP system. The investigation of more crop traits retrieval through RTMs and/or integration with CGMs could be vital, not only for CYP systems but also for precision and smart agriculture to have data-driven decisions. Currently, all investigations mainly focus on LAI and/or chlorophyll retrievals through RTMs. Other crop traits like net primary productivity or above ground mass need much more investigation for the improvement of the reliability and retrieval efficiency of models as well as these traits.  
The highest average citation rate, for the study [58], was 25.0, with a minimum citation of 1 in the year 2015 and a maximum of 47 in the year 2023. This study has an R\({}^{2}\) of 0.928 with an RMSE of 0.485 through the PROSAIL model. Thus, in the case of crop traits, reliable information for LAI retrieval based on RTMs is possible, mainly due to significant (total, 54) research studies. However, the retrieval of a few other crop traits still needs the attention of researchers for reliability and efficiency. In the study [73], crop water content (Cw) had a lower value of R\({}^{2}\) = 0.30, but in the same study other crop traits, LAI (R\({}^{2}\) = 0.90), Cab (R\({}^{2}\) = 0.71) and CCC (R\({}^{2}\) = 0.89), indicated good retrieval performance of the model. The reason identified was the narrow variation range in the dataset, thus describing the limitation of RTMs to cater to the impact of narrow variations on the retrieval of crop traits.
# Electrophysiological Hyperscanning of Negotiation During Group-Oriented Decision-Making
## 2 Materials and Methods  
### Sample  
A total of 26 university students (6 males and 20 females; mean age = 25.4 years, standard deviation \(\pm\) 6.4 years) participated in the study. They were recruited through a non-probabilistic convenience sampling method from [BLIND]. An a priori power analysis was conducted using G*Power (version 3.1.9.7 [45]) to determine the required sample size for the planned repeated-measures analysis of variance (ANOVA), which included three Regions of Interest (ROIs), two groups, and two measurement points. Based on a medium effect size (\(f\) = 0.40) derived from prior hyperscanning EEG studies reporting regional differences in spectral activity during social interaction (e.g., [46]), the analysis indicated that a minimum of 24 participants would be required to achieve a statistical power of 0.95 at an alpha level of 0.05. This supports the adequacy of the recruited sample for detecting effects of interest in the present study. This analysis indicated that, for a medium effect size (\(f\) = 0.40), an alpha level of 0.05, and a statistical power of 0.95, a minimum of 24 participants was necessary to achieve reliable results across two groups and two measurement points, confirming the sufficiency of the recruited sample.  
The participants were randomly assigned to one of two experimental roles, labelled Member 1 and Member 2. This attribution was conducted to form 13 dyads, with each dyad comprising two individuals of the same gender. To control for potential biases and confounding variables, participants were explicitly selected to ensure that no prior familiarity or acquaintance existed between dyad members before the experimental session.  
The participants were enrolled in the study on a voluntary basis, with no monetary incentives or alternative compensation offered. Inclusion criteria were rigorously defined to ensure homogeneity in relevant variables: all participants were required to be right-handed, possess normal or corrected-to-normal visual acuity, and meet standardized thresholds for general health and cognitive functioning. Exclusion criteria were systematically applied to eliminate potential confounding factors. These included a self-reported history of psychiatric or neurological disorders, clinically significant depressive symptoms, diminished global cognitive functioning, and documented impairments in short- or long-term memory. Additionally, individuals currently using psychoactive medications with the potential to alter cognitive processes were excluded to maintain the integrity of the experimental outcomes. All participants signed a written informed consent before participating in the study.  
Ethical approval for the study was granted by [BLIND]. The study was carried out in full accordance with the ethical standards delineated in the Declaration of Helsinki (2013). Additionally, the study adhered to the General Data Protection Regulation (GDPR, EU Reg. 2016/679).
# Performance Optimization of MANET Networks through Routing Protocol Analysis
## 3 Results
### Packet Loss  
The packet loss results from the OLSR and AODV protocols are shown in Figure 5. The figure depicts that OLSR has a packet loss value higher than that of AODV. The default AODV packet loss protocol has an average value of 10.4%, while that of the OLSR protocol is 19.50%. While reducing the value of RREQ_RETRIES to 2 (AODV 2) reduces the packet loss by 13.8%, increasing RREQ_RETRIES to 4 (AODV 3) increases the packet loss by 14.66%. These results also apply to a reduction in the value of MAX_RREQ_TIMEOUT, which results in a decrease in packet loss. By decreasing the value of MAX_RREQ_TIMEOUT 5 s (AODV 4), this reduces packet loss by 40.5%, while increasing the MAX_RREQ_TIMOUT value of 12 s (AODV 10) increases the packet loss by 35%.  
Figure 4: Throughput results.  
Figure 3: PDR results.
## 4 Experimental Setup
### Dataset
The selection of risk parameters is a critical aspect of the proposed InSAR-RiskLSTM framework as these parameters directly influence the model's ability to predict railway deformation risks. The primary risk parameters were derived from InSAR data and include deformation velocity, terrain slope, coherence, and curvature. These features were chosen due to their strong correlation with ground deformation events and their ability to highlight risk-prone areas. For instance, deformation velocity provides a quantitative measure of ground movement, while terrain slope and curvature capture the topographic features that may exacerbate deformation risks. Coherence measures the consistency of radar signal reflections, offering insights into the stability of ground conditions. The training data used in this study comprise diverse datasets, including urban, coastal, and natural terrains, each presenting unique deformation patterns. These datasets were preprocessed to ensure consistency and quality. Preprocessing steps included normalization to standardize data distributions across different sensors, augmentation techniques such as rotations and noise injection to simulate real-world variations, and patch extraction for high-resolution datasets. These steps ensured the data were both representative and robust, supporting effective model training and evaluation.
# IIoT Malware Detection Using Edge Computing and Deep Learning for Cybersecurity in Smart Factories
## 4 Experimental Evaluation
### Dataset and Experiment Setup  
The Malimg dataset was used in this experiment. It is one of the malware datasets frequently used in CNN experiments [102] and is publicly available. This dataset was created by the Vision Research Lab at the University of California and contains 9339 malware image samples belonging to 25 different malware families/classes, which have been previously converted to grayscale images. Figure 8 shows the composition of 25 malware families included in the Malimg dataset. In the composition of this dataset, it can be seen that there is a sample data imbalance (e.g., Allaple.A: 31.5%, Skintrim.N: 0.8%) among various malware classes.  
The experimental setup used for this experiment was implemented with Python 3.9 (Open Source, [[https://www.python.org/downloads/](https://www.python.org/downloads/)]([https://www.python.org/downloads/](https://www.python.org/downloads/)), Python Software Foundation, USA) using Keras 2.9 (Open Source, github.com/keras-team/keras, MIT License, USA) and TensorFlow 2.9 (Open Source, github.com/tensorflow/tensorflow, Apache License, USA) as backend computing frameworks on an Intel(r) Core i7-3537U CPU, with 8 GB RAM and Windows 10 (Microsoft, EULA (End-User License Agreement), USA) environment. Training and test data were randomly selected from the dataset used in this experiment, with 70% of the total data used for training and 30% used for validation. The main hyperparameters used in this experiment are shown in Table 8.
## 5 Conclusions  
In this paper, a novel two-stage receiver combining Chebyshev acceleration and the AOR algorithm (CAOR) is proposed to improve the convergence rate and achieve remarkable BER performance in a small number of iterations. Numerical results demonstrate the performance comparison with the different detection methods, in which the performance of CAOR is the best among all other detection methods. Although this method raises the computational load slightly more than AOR, fortunately, this is acceptable since the hardware's computation ability is growing nowadays. Moreover, it can achieve a similar performance as SSOR and AOR with fewer iterations. Hence, the computational complexity of the proposed method is actually lower than expected. To sum up, regarding the BER performance and complexity, our proposed scheme is beyond comparison.  
In summary, the M-MIMO has the ability to promote BER performance significantly in the 5G OFDMA systems, and our proposed method can achieve an appropriate acceleration parameter \(\gamma\) and relaxation parameter \(\omega\) and has the best BER under moderate computational complexity, so it is very suitable to apply to the current and next-generation wireless systems.  
Figure 11: Computational complexity of different detection schemes with \(N_{T}=16\).  
**Author Contributions:** Conceptualization, Y.-P.T., C.-Y.C. and K.-H.L.; methodology, Y.-P.T. and C.-Y.C.; software, C.-Y.C. and Y.-P.T.; validation, Y.-P.T., C.-Y.C. and K.-H.L.; formal analysis, Y.-P.T. and K.-H.L.; investigation, Y.-P.T. and C.-Y.C.; data curation, [PERSON]-[PERSON] and [PERSON]-[PERSON]; writing--original draft preparation, [PERSON]-[PERSON], [PERSON] and [PERSON]; writing--review and editing, [PERSON]-P.T., [PERSON] and [PERSON]; visualization, [PERSON]-[PERSON] and [PERSON]-[PERSON]; supervision, [PERSON]-[PERSON]; project administration, [PERSON]-[PERSON] and [PERSON]-[PERSON]; funding acquisition, [PERSON]-[PERSON] and [PERSON]-P.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This research was supported in part by the Ministry of Science and Technology in Taiwan under the grant numbers MOST 109-2637-E-150-002 and MOST 110-2637-E-150-011.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Feature Residual Analysis Network for Building Extraction from Remote Sensing Images
## 3 Experiments and Results
### Analysis of Implementation Results
The improved FPP module adds a 2 \(\times\) 2 convolution kernel in the multi-scale fusion method to process details that may not be restored. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Net**} & \multirow{2}{*}{**Parameter/M**} & \multirow{2}{*}{**Floating Point Operations**} & \multicolumn{2}{c}{**Average Prediction**} \\  & & /GFLOPs & **Time** & **mIoU** \\  & & & **(Seconds/Frame)** & \\ \hline FCN-8s & 121.6 & 54.13 & 86.57 & 0.8233 \\ SegNet & 30.5 & 80.20 & 42.80 & 0.8275 \\ UNet & 35.0 & 34.61 & 41.20 & 0.8301 \\ PSPNet & 38.0 & 22.54 & 46.54 & 0.8424 \\ FPN_MSFF & 27.0 & 47.43 & 51.82 & 0.8687 \\ FRA-Net & **12.3** & **10.39** & **34.57** & **0.8678** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation results of different models in the LandCover test set.
###### Abstract  
The aim of the present study was to investigate which of the four music heads of the quadriceps femoris is the best surrogate of quadriceps hypertrophy, following resistance training, evaluated by ultrasonography. Forty three physical education students (age: 22.1 \(\pm\) 3.1 years, height: 175.2 \(\pm\) 9.3 cm, mass: 75.3 \(\pm\) 8.0 kg, BMI: 22.8 \(\pm\) 2.8 kg\(\cdot\)m\({}^{-2}\)) participated in the study. Participants followed an 8-week resistance training program in order to enhance quadriceps muscle hypertrophy. Before and after the training period muscle ultrasonography was used to evaluate: total quadriceps (T), vastus lateralis (VL), vastus intermidis (VI), vastus medialis (VM) and rectus femoris (RF) cross sectional area (CSA). Total quadriceps' as well as VL, VI and VM, CSAs were significantly increased after training (changes ranged between 10.9 \(\pm\) 9.9% and 18.6 \(\pm\) 10.8%; \(p<\) 0.05). No significant changes were found for RF CSA after training (\(p>\) 0.05). Agreement analyses revealed high values for VL and VI (e.g., ICC = 0.879-0.915; \(p\) = 0.000), and low values for VM and RF (e.g., ICC = 0.132-0.526; \(p\) = 0.000). These results suggest that training-induced changes in muscle hypertrophy in VL and VI measured via muscle ultrasonography may be significantly predict the whole quadriceps hypertrophy in response to lower body resistance training. Consequently, VL and VI may considered as valid surrogates of whole quadriceps muscle hypertrophy.  
Keywords:quadriceps; muscle hypertrophy; ultrasonography; cross sectional area; resistance training +
Footnote †: journal: _Sciences_