esa-sceva/satcom-chunk-collection huggingface dataset:
# Potentials for Pressure Gain Combustion in Advanced Gas Turbine Cycles
## Materials and Methods
### Combustor Model
The flame front encountered blockage bodies to eventually form a detonation, which consumed the remaining fuel towards to combustor tube outlet. The first part of the combustion can be modeled as isobaric combustion with some pressure losses, and the second can be modeled as isochoric combustion. Depending on the deflagration to detonation length (DDT), the whole process was more isobaric or isochoric. This idea was implemented in the combustor model. The developed PGC model calculated the combustor outlet temperature from the energy balance around the combustor using lookup tables. Secondly, a portion of the fuel denoted \(r\) was attributed to isobaric combustion \(q_{cp}\). Again, lookup tables gave the temperature rise till \(T_{cp}\) for this isobaric combustion (Equation (9)). This represents the temperature at the end of deflagration. \[q_{cp}=FAR\cdot r=c_{p}\left(T_{cp}-T_{in}\right) \tag{9}\]  
The pressure loss \(dP_{Loss}\) accounted for losses due to friction and heat addition as in Equation (10). \[p_{cp}=p_{in}\cdot dP_{Loss} \tag{10}\]  
Finally, outlet pressure was calculated using Equation (11) and assuming an isochoric change of state. \[p_{out}=\frac{T_{out}}{T_{cp}}\cdot p_{cp} \tag{11}\]  
The model behavior with 30% isobaric combustion (\(r=0.3\)) and a pressure loss of 20% (\(dP_{Loss}~{}=~{}0.8\)) agreed well with the data from Paxson at the same inlet temperature of 792 K and 24 atm, as depicted in Figure 2. However, the model deviated at higher temperature ratios (TR) from the data. Here, the stoichiometric combustion temperature limits at 2500 K were reached, leading to lower equilibrium temperatures. The performance in terms of pressure ratio against temperature ratio is depicted in Figure 2 for all introduced combustor models. For gas turbine cycle analyses, the TR between 1.8 and 3 was relevant [19]; therefore, the shortcomings of the Mix model towards high TR were negligible. In order to assess the sensitivity of the gas turbine design with respect to different PGC implementations, an optimistic and pessimistic version of the developed model with respect to combustor performance will be created. For the optimistic model (Mix opt), \(r~{}=~{}0.24\) was selected, and for the pessimistic (Mix pes) \(r~{}=~{}0.36\). This means that there was 20% more isochoric combustion for the optimistic case, and in the pessimistic case, there was 20% more isobaric combustion. The effect on PR is also depicted in Figure 2. The ideal isochoric model used the mix model with \(r~{}=~{}0.0\). The complete fuel was burnt under isochoric conditions. The isobaric combustion was modeled with a pressure loss of 5% and a combustion efficiency of 99%. For PGC, a combustion efficiency of 100% was assumed.
## 3 Results and Discussion
### Modeling and Model Evaluation of VOCs in 'hongjiang' Obtained from Different Factors  
PCA is a multidimensional data statistical analysis method for unsupervised pattern recognition, widely used for variety identification [19; 20], processing method discrimination [21], and other analytical purposes. As shown in Figure 2A, a PCA model (R2X = 0.885, Q2 = 605) was constructed with the VOC data obtained from 'hongjiang' samples from different locations, varieties, and cultivation modes, and six principal components were extracted with a cumulative contribution rate of 88.5%, representing major data. As shown in Figure S1, the contribution values for PC 1, PC2, PC3, PC4, PC5, and PC6 were 0.320, 0.181, 0.145, 0.096, 0.087, and 0.056, respectively.  
Figure 1: Analysis of species (**A**) and proportion (**B**) of VOCs among _Citrus sinensis_ ‘hongjiang’. a, b, c: Different letters represents a significant difference.  
Figure 2: Score plot of the PCA-X model (**A**), HCA (**B**), OPLS-DA model (**C**), and cross-validation of the OPLS-DA model (**D**) for VOCs among _Citrus sinensis_ ‘hongjiang’.  
As shown in Figure 2A, the 21 samples were distributed in the first, second, third, and fourth quadrants. PRGN was distributed in the fourth quadrant with a significant difference compared to other samples. There was a large difference between PRNN and PRHN distributed in the first quadrant and MRNN, PQOY, and PCON distributed in the third quadrant. HCA was performed based on the six principal components extracted by PCA, as shown in Figure 2B. The results of the HCA analysis showed that the 21 'hongjiang' samples clustered into four classes, of which PQOY and PCON clustered into the first class, PRGN into the second class, PRHN and PROY into the third class, and PRNN and MRNN into the fourth class. This indicated that the VOC characteristics of the location, variety, and cultivation mode varied greatly.  
Similar to PCA, OPLS-DA is also a multidimensional vector analysis method based on dimensionality reduction. Still, the difference is that OPLS-DA is a supervised discriminant analysis method widely used for the quality evaluation of fruit and vegetable varieties [22] and origins [23]. As shown in Figure 2C, an OPLS-DA model with strong cumulative explanatory and predictive power and good stability (R\({}^{2}\)X = 0.884, R\({}^{2}\)Y = 0.989, Q\({}^{2}\) = 0.964) was established with the VOC component data of 'hongjiang' under three factors. In addition, the model was analyzed for reliability using 200 cross-validations and the replacement test (R\({}^{2}\) = 0.2913, Q\({}^{2}\) = \(-\)0.6988), as shown in Figure 2D, and negative values of Q\({}^{2}\) indicated that the model was reliable [24], had no overfitting, and was applicable for discriminant analysis of 21 samples under study.
# Effect of Cooking Methods on Bioactive Pigments in Purple Carrots (_Daucus carota_ L.)
## 3 Results and Discussion
In view of Table 4, the results obtained from both DPPH and ABTS assays were in good agreement. Freeze-drying resulted in a significant (\(p\) < 0.05) increase of the AA with respect to raw purple carrots (6.44 and 5.26 mg\(\cdot\)g\({}^{-1}\) DW vs. 0.16 and 0.13 mg\(\cdot\)g\({}^{-1}\) DW). Actually, freeze-dried carrots were by far the samples with the highest radical scavenging activity, followed by the roasted carrots. Regarding roasting, it is worth mentioning that the longer the roasting time, the higher the AA. Steaming did not have any effect on the AA, whereas boiling, as expected, produced a significant (\(p\) < 0.05) decrease of the radical scavenging activity (0.04 and 0.03 mg\(\cdot\)g\({}^{-1}\) DW vs. 0.16 and 0.13 mg\(\cdot\)g\({}^{-1}\) DW in fresh carrots). AA losses after boiling have been reported in purple foods other than carrots [12; 13] and pigmented carrots [15]. A possible correlation between the content of bioactive pigments (Table 2) and the AA as measured by both assays (Table 4) was assessed by linear regression. The results obtained are shown in Table 5. As observed, TPC and TAC were directly correlated (\(r^{2}\) > 0.99 in all cases) with the free radical scavenging activities of the carrots cooked by different methods. In contrast, no correlation was established between TCC values and those provided by DPPH and ABTS assays. This reflects that polyphenols, and particularly anthocyanins, are mainly responsible for the AA measured in purple carrots regardless of the cooking method used. In this respect, although carotenoids are efficient antioxidants scavenging singlet molecular oxygen radicals, anthocyanins act on a regular basis as an H-atom donator or in a single electron transfer. Both of them are part of the antioxidant defense system in the human organism, but anthocyanins may prevent inflammation and protect against type 2 diabetes, cancer and heart disease. All in all, a balanced diet including carotenoid- and anthocyanin-rich foods contributes to benefiting human health. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{4}{c}{**ANTIOXIDANT ACTIVITY**} \\ \hline \multirow{2}{*}{**UNCOOKED**} & **DPPH** & **ABTS** \\  & **(mg Trolox g\({}^{-1}\) DW)** & **(mg Trolox g\({}^{-1}\) DW)** \\ \hline Fresh & 0.16 \(\pm\) 0.01 a & 0.13 \(\pm\) 0.01a \\ \hline \multicolumn{4}{c}{**COOKING METHOD**} \\ \hline Freeze-drying & 6.44 \(\pm\) 0.08 b & 5.26 \(\pm\) 0.07 b \\ \hline Boiling & 0.04 \(\pm\) 0.02 b & 0.03 \(\pm\) 0.02 b \\ \hline Steaming & 0.20 \(\pm\) 0.02 a & 0.16 \(\pm\) 0.02 a \\ \hline Roasting (10 min) & 0.31 \(\pm\) 0.08 b & 0.25 \(\pm\) 0.07 b \\ \hline Roasting (20 min) & 0.54 \(\pm\) 0.10 b & 0.43 \(\pm\) 0.09 b \\ \hline Roasting (30 min) & 0.86 \(\pm\) 0.07 b & 0.69 \(\pm\) 0.07 b \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Effect of cooking on antioxidant activity. Different lowercase letters between the sample subjected to each cooking method and fresh carrots (used as a reference) indicate differences at \(p\) < 0.05.
# Design of a Dual-Path Speech Enhancement Model
## 5 Results and Discussion
### Comparisons with Recently Proposed SE Models  
We compared our proposed model with several recent SE models on the VBD dataset. Specifically, we selected models that ensure causality while demonstrating strong performance on VBD. The models we compared include the following: DEMUCS [32], CTSNet [33], GaGNet [17], FullSubNet+ [34], NUNet-TLS [22], FRCRN [35], CompNet [36], FDFNet [37], and CNUNet-TB [19]. The parameter values of the compared models were obtained from their respective reference papers. When parameter details were unavailable, we left the corresponding fields blank. The comparison results are presented in Table 2, where the highest scores are highlighted in bold and the second-highest scores are underlined.  
As shown in Table 2, achieving a top performance across all metrics is challenging. For example, aside from our model DPCNU, FRCRN achieves the highest WB-PESQ and CBAK scores, whereas NUNet-TLS attains the highest CSIG and COVL scores. Additionally, CTSNet and CompNet achieve comparable PESQ scores, yet their CSIG and CBAK scores differ significantly. However, DPCNU consistently outperforms all compared models overall. Specifically, DPCNU achieves a WB-PESQ score of 3.33, surpassing the next-best model, FRCRN, which records a score of 3.21. Regarding CSIG, CBAK, and COVL, DPCNU scores4.48, 3.75, and 3.95, respectively. Additionally, it attains an STOI score of 96 and an SNRseg score of 10.44, the highest among all evaluated models.  
Among the compared models, GaGNet, NUNet-TLS, and CNUNet-TB are based on nested U-Net architectures, while CTS-Net, GaGNet, FDFNet, and CNUNet-TB adopt two-stage or two-branch designs. Compared with these models, DPCNU demonstrates substantial performance improvements, highlighting its effectiveness in SE.  
Our model consists of 18.69 million parameters, making it relatively large compared to other recent SE models. Nevertheless, it achieves significantly higher scores across all evaluation metrics. Notably, the lightweight version, DPCNU(S), reduces the number of convolution kernels by half, resulting in 4.78 million parameters. Despite its compact size, it achieves the second-best performance across all metrics. This demonstrates that DPCNU(S) effectively balances computational efficiency and SE quality.  
Although the proposed model demonstrates strong performance across multiple metrics, it has limitations. First, the use of two independent paths increases the model complexity and training time compared with one-branch networks. Second, the model is currently trained and tested on additive noise scenarios. Future work will focus on reducing the computational cost of the dual-path architecture through model compression and efficient training strategies, aiming for faster training and real-time deployment. In addition, we plan to evaluate the model's robustness in more diverse acoustic scenarios, including reverberation, speaker overlap, and unseen environments.
# Differential Characteristics Based Iterative Multiuser Detection for Wireless Sensor Networks
## 3 Multiuser Detection Based on Differential Characteristics  
The main idea of the proposed multiuser detection method is to iteratively find the error bits from the received signals and correct them. The block diagram of the proposed iterative multiuser detection algorithm is shown below in Figure 1. Firstly, the differential characteristics are calculated and the outputs of matched filters are mapped in to a one dimension space due to optimal multiuser detection is a multi-dimensional optimization problem; then a primary threshold classification is conducted to find the potential incorrectly received bits; after that an error bit corrector is employed to correct the wrong bits. The three stage process above is iterative executed to further lower the BER.
# An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care
## 7 Outing and Visiting Detection
### Data Pre-Processing  
In multi-person households, it is challenging to use sparsely deployed binary environmental sensors to track personalized activities, such as outings and visits. Therefore, we define outings as times when nobody is at home, and visits as times of exceptionally increased activity reported by the environmental sensors. Consequently, the detected outings and visits can only be used as an indirect indicator to track the changes of patients' social and physical health status, instead of being reported as a comprehensive list of the corresponding activities.  
In the literature, outings are typically identified as long inactive periods, and visits either based on pre-defined rules (certain overlap of sensor activities), or with supervised algorithms trained using carefully labeled datasets. Our approach is different in the following way: first, we assume that any outing/visiting event happens between adjacent door open/close activities at the entrance. Consequently, outings and visits are identified as inactive and exceptionally increased sensor activities between adjacent door segments. This way, the algorithm can adapt to different households and sensor topology. Please note that we disregard any segment that lasts less than 10 min.
## 2 R-SpaceFibre Design: Features and Trade-Offs
### Fault Tolerance and Compatibility with Full SpaceFibre Interfaces  
R-SpaceFibre is meant to be used as lightweight high performance high speed link. It is a slightly modified version of the SpaceFibre protocol, fully interoperable with a SpaceFibre endpoint but compliant just to a subset of the SpaceFibre standard requirements, in order to reduce system complexity. When two Reduced SpaceFibre (R-SpFi) endpoints communicate together, no restrictions on protocol operations arise, also in the case of Single Event Upset (SEU) bit-flip errors happening on the link. The received corrupted packet will be handled at higher levels of the protocol, application dependent. However, it is possible to connect R-SpaceFibre and SpaceFibre endpoints. Coherent data transmission and reception is guaranteed, discarding received broadcast messages and ignoring retry requests. R-SpaceFibre is programmed to discard any retry request from a far-end. Consequently, if an error occurs during the transmission of a packet from full SpFi interface to a reduced SpFi endpoint, no retry request is given and the received corrupted packet is transmitted at the higher level of the protocol, where it will be handled according to the application requirements. Indeed, R-SpaceFibre has been designed for communication link involving payloads streaming large amount of data that do not contain safety-critical information. On the other hand, when a SEU occurs in the transmission of a packet from a R-SpFi interface to a full SpaceFibre interface, the second one will constantly try to ask for data retransmission, blocking the traffic on that Virtual Channel. This can be easily handled programming a reset interface to be given in order to re-establish the communication flow.
## 3 Methodology and Model Parameters
### Numerical Simulation Method  
The software FLAC3D, which is adept at calculating equivalent stress by assessing the equivalent plastic strain, was employed for the numerical simulations. It is vital to determine the stress state after the plastic yield of the surrounding rock. When the wellbore rock's plastic yield value surpasses its critical threshold, the convergence of the calculation should be judged. If the calculation cannot converge, it signals a state of instability and the potential failure of the surrounding rock.  
Following the factors and parameters detailed in Table 2, the numerical simulation to assess the influence of cracks and solution cavities on wellbore stability comprises several stages:  
1. The stratum model containing the cracks and solution cavities according to the parameters setting as shown in Table 2 is created, the model parameters are set, and initial stress and pore pressure are applied to the model. The model then undergoes a calculation phase to achieve stress balance.
2. Within this stratum model, a borehole is simulated. Since the main concern is the deformation of the strata around the wellbore, the inside of the wellbore is set as a certain pressure boundary condition considering the supporting effect of drilling fluid on the wellbore wall. To ensure the stability of the rock surrounding the wellbore, a high fluid column pressure is applied in the borehole at first.
3. The fluid column pressure is gradually decreased. The point at which the wellbore collapses with a fluid column pressure (\(P_{\mathrm{c}}\)) is determined by the onset of plastic deformation without a convergence result in the surrounding rock, and the Mohr-Coulomb criterion is used to justify this plastic deformation. From this, the equivalent drilling fluid density of collapse pressure (\(\rho_{\mathrm{c}}\)) is calculated by dividing \(P_{\mathrm{c}}\) by the formation depth (\(H\)).  
The numerical simulation flow chart is illustrated in Figure 8.
## 3 Results
### Interference Effect of Various Ions and Selectivity of EgLiCl-mGO  
The study of interreference effects on the suggested method is important, because the adsorption of Pb(II) and Cd(II) ions may be influenced by other cations and anions; consequently, the performance of the method decreases [31]. Adsorption of the target ions by EgLiCl-mGO was conducted in the presence of higher amounts of other ions with respect to their applications in real samples. The \(\pm\)5% was considered as the maximum tolerance limit and values smaller than this amount were acceptable. Table 3 summarizes the data.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**pH**} & \(W_{d}\) & \(W_{s}\) & \multirow{2}{*}{**SD**} \\  & **(\(\times\)10\({}^{-2}\) g)** & (\(\times\)10\({}^{-2}\) g) & \\ \hline
2 & 1.0 & 1.7 & 0.7 \\
3 & 1.0 & 6.3 & 5.3 \\
4 & 1.0 & 9.3 & 8.3 \\
5 & 1.0 & 8.2 & 7.2 \\
6 & 1.0 & 7.9 & 6.9 \\
7 & 1.0 & 6.0 & 5.0 \\
8 & 1.0 & 3.1 & 2.1 \\
9 & 1.0 & 2.1 & 1.1 \\ \hline \end{tabular}
\end{table}
Table 2: Swelling behavior of EgLiCl-mGO to adsorb Pb(II) and Cd(II) at different pH.  
Under the optimum conditions, the selectivity of the method was studied. According to the data, the preconcentration efficiencies of Pb(II) and Cd(II) were 99% and 98%, respectively; however, the preconcentrating efficiencies of Cr(II), Cu(II), Mn(II), and Pd(II) were 32%, 85%, 17%, and 76%, respectively. This strength is related to selective interaction between target ions and specific active sites on the surface of EgLiCl-mGO. Pb(II) and Cd(II) have a smaller ionic radius so they have a greater charge density; consequently, they occupy the active sites of EgLiCl-mGO sooner and more strongly.
# Unsupervised Image Segmentation on 2D Echocardiogram
## Appendix A Appendix A Appendix A.1 Quality Assessment of Selected vs. Non-Selected Videos
### Comparison with State-of-the-Art Models
W-Net uses an autoencoder-like structure, with two U-Net-style architectures working in tandem--one for encoding and one for decoding. This design enables W-Net to capture global and local patterns without the need for pixel-level annotations [4]. In this study, we trained the W-Net over our dataset to maximize its performance. An interesting observation from our experiments is that the results were fairly consistent when training the model between 50 and 150 epochs. Beyond 150 epochs, however, the segmentation quality degraded, often resulting in black images with no useful segmentation information. This indicates a limitation in the w-net approach, as the model may overfit the data and lose its ability to differentiate meaningful structures. Additionally, as seen in the comparison below, W-Net tends to struggle with fine boundary detection and noise handling, especially when applied to gray-scale echocardiograms. In Figure A7, we observe that W-Net is capable of differentiating the background from the heart structures but struggles to accurately delineate the chambers and handle noise within the image. Moreover, W-Net's performance is influenced by the inherent challenges of unsupervised learning, such as the difficulty of learning fine-grained boundaries and dealing with the complex textures present in medical images. Although unsupervised models like W-Net are promising for scenarios with limited labeled data, they often require additional post-processing steps, such as the Watershed algorithm, to improve segmentation quality. Discussions: While the supervised approaches can achieve highly accurate results when trained on labeled data, their dependence on extensive annotated training data poses challenges to medical image analysis research. Direct adoption of pre-trained models has been shown to have poor performance in echocardiiogram segmentation. On the other hand, the recent unsupervised image segmentation techniques in computer vision show limited effectiveness even after training with a large echocardiiogram dataset due to their limited capability for handling noise in grayscale images. Customized model architectures, tailored learning processes, and effective post-processing steps are essential to improve the accuracy of unsupervised techniques in echocardiiogram analysis. It is important to note that we did not fine-tune any of the supervised models in this experiment due to the lack of extensive annotated data. However, upon data availability, fine-tuning these models could potentially improve their segmentation accuracy, especially in capturing fine details and reducing errors in boundary detection. Future work could focus on exploring semi-supervised techniques to reduce training data requirements and fully leverage the capabilities of these models in echocardiographic segmentation.