esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Differential Metabolite Selection  
To further identify the differential metabolites among different varieties, orthogonal partial least-squares discriminant analysis (OPLS-DA) was used. OPLS-DA can maximize the differentiation between groups and facilitate the search for differential metabolites [20]. The selection criteria were as follows: fold change \(\geq\) 2 or \(\leq\) 0.5, \(p\)\(<\) 0.05; variable importance in projection (VIP) \(\geq\) 1. To avoid overfitting, a permutation test (200 permutations) was performed. When these criteria are met, it is considered that there is a significant difference in the considered metabolite. Meanwhile, the relationship between different metabolites in each group is shown in a Venn diagram (Figure 5).  
A total of 28 differential metabolites were identified in YX and GX, of which 5 were upregulated and 23 downregulated, and 3 were unique (Table S2). A total of 33 differential metabolites were identified in YX and KX, among which 5 were upregulated and 28 downregulated, and 15 were unique (Table S3). A total of 10 differential metabolites were identified in KX and GX, all of which were downregulated and none were unique, i.e., they were identified in the other two varieties (Table S4). Only three differential metabolites were identified in all three groups, and the highest contents were found in YX, which were alpha-cadinol (terpenoid), _N_-(2,3,4-trifluorobenzoyl)-l-alanine-methyl ester (ester), and [35-(3-alpha,5-alpha,8-alpha)]-5-azulenemethanol,1,2,3,4,5,6,7,8-octahydro-alpha,alpha,3,8-tetramethyl-acetate (ester).
# Fusion of Bilateral 2 DPCA Information for Image Reconstruction and Recognition
## 3 Methodology
### Alternative 2 DPCA  
Since 2 DPCA only deals with one direction of images, it is necessary to propose an algorithm to deal with the other direction of images. This idea leads to A2 DPCA [21]. A2 DPCA aims to find a projection matrix \(\mathbf{V}\in\mathbb{R}^{h\times l}\) by solving the following optimization problem:  
\[\max_{\mathbf{V}^{T}\mathbf{V}=\mathbf{I}_{l}}tr\mathbf{V}^{T}\frac{1}{n-1} \sum_{i=1}^{n}(\mathbf{X}_{i}-\mathbf{\widetilde{X}})(\mathbf{X}_{i}- \mathbf{\widetilde{X}})^{T}\mathbf{V}, \tag{10}\]  
where \(l\) is the feature number. Similarly, \(\mathbf{V}\) could be calculated by eigen-decomposition of the image covariance matrix  
\[\frac{1}{n-1}\sum_{i=1}^{n}(\mathbf{X}_{i}-\mathbf{\widetilde{X}})(\mathbf{X} _{i}-\mathbf{\widetilde{X}})^{T}\in\mathbb{R}^{h\times h} \tag{11}\]  
and then selecting the eigenvectors corresponding to the \(l\) largest eigenvalues [21]. The percentage of total variance explained by these eigenvectors can be calculated likewise. For a test image sample \(\mathbf{\widetilde{X}}\), its feature matrix is  
\[\mathbf{V}^{T}(\mathbf{\widetilde{X}}-\mathbf{\widetilde{X}})\in\mathbb{R}^{l \times w}. \tag{12}\]  
The reconstruction image of sample \(\mathbf{\widetilde{X}}\) is  
\[\mathbf{V}\mathbf{V}^{T}(\mathbf{\widetilde{X}}-\mathbf{\widetilde{X}})+ \mathbf{\widetilde{X}}\in\mathbb{R}^{h\times w}. \tag{13}\]  
A2 DPCA only projects the columns of images onto the projection matrix. It also lacks symmetry, the same as 2 DPCA.
## 4 Methods for EIT Data Reduction and CNN Model Compression for Efficient Edge Deployment
### CNN Model Compression for Optimised Edge Deployment  
A model compression technique is a method to reduce the size of a neural network model while maintaining its performance, crucial for deploying models on edge devices. Model compression can be categorised into three techniques:  
* **Transfer learning**: Involves Knowledge Distillation (KD), where a smaller model (student) learns from a larger, pre-trained model (teacher), allowing it to achieve comparable performance with fewer parameters [29].
* **Redundancy reduction**: Includes pruning, quantisation, and hashing. Pruning eliminates weights that contribute minimally to the model's output, resulting in a sparser model that retains essential features while reducing the size and computational load [30]. Quantisation represents weights with lower precision, decreasing the model's memory footprint.
* **Data Precision Reduction**: Includes binarisation, which converts the model's outputs into binary values (0 s and 1 s). By simplifying the representation of weights and activations, binarisation can significantly decrease the model size and improve inference speed, suitable for deployment on devices with stringent resource constraints [31].
## 2 Methods
### Audio Recordings Synchronization with PSG Channels  
The PSG and the audio recording systems were activated manually by the specialist supervising each PSG study, within a time difference of a few seconds. Precise calculation of the time delay in order to transfer the information of time occurrence of apnea events in the audio recordings was imperative. The synchronization was performed by the use of the low-quality contact microphone incorporated in the PSG system. This second contact microphone, recording tracheal sound through the entire night, had a sampling frequency of 500 Hz, which was too low to be able to provide high-quality breathing sound recording. The synchronization algorithm was applied to signal excerpts of a total length of 20 min and it included the steps described here: (a) Filtering and downsampling of the high-quality contact microphone so that the two sensors had a common sampling rate of 500 Hz. (b) Envelope extraction of the two signals based on Hilbert transformation. (c) Extraction of the cross-correlation of the two signal envelopes, and (d) Determination of the time point of maximum cross-correlation, which gave the time delay between the acquired signals. The accuracy of the time delay determination was important to assure optimum validation of any system for apnea/hypopnea events detection. Thus, manual observation of all signals was performed to verify that the maximum error in the time delay did not exceed 2 s.
## 8 Conclusions  
In this paper, a novel direct predictive torque control algorithm for the SRM drive system is proposed. In the proposed DPTC method, the torque allocation set is obtained by directly dividing the reachable range of phase torque. Afterward, the optimal torque allocation scheme is obtained by using a cost function. Compared with the MPTC method, the proposed DPTC method solves the problems of unfixed switching frequency and large torque ripple. In addition, the proposed method takes into account the computing power of the hardware, thus ensuring the feasibility of the algorithm. In the torque predictive control of the SRM, the cost function is usually used to balance the contradiction between torque ripple and copper loss, which increases the difficulty of parameter setting and reduces the control effect. In the proposed method, the cost function only includes the penalty for phase currents, so the complexity of the algorithm is reduced. Finally, several algorithms are verified and compared by the simulation and experiment. The comparison results indicate that the proposed DPTC method not only outperforms the PWM-DITC method in terms of copper loss but also has significantly lower torque ripple than the MPTC method, thus  
Figure 12: Experimental results of (**a**) the DITC method, (**b**) the FCS MPTC method in [31], (**c**) the PWM-DITC method in [36], and (**d**) the proposed DPTC method at 800 rpm with a load torque of 5 Nm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Speed (rpm)** & \(\mathbf{T_{r,p}}\) & \(\mathbf{T_{r,RMS}}\) & \(\mathbf{I_{RMS}}\) \\ \hline \multirow{4}{*}{DITC} & 200 & 42.4\% & 0.4838 & 4.7462 \\  & 400 & 51.3\% & 0.5804 & 4.9286 \\  & 800 & 63.8\% & 0.6247 & 5.3230 \\ \cline{1-1}  & 200 & 41.2\% & 0.4792 & 4.5863 \\ \cline{1-1} FCS-MPTC in [31] & 400 & 47.8\% & 0.5635 & 4.6441 \\ \cline{1-1}  & 800 & 56.3\% & 0.6091 & 5.2549 \\ \cline{1-1}  & 200 & 27.1\% & 0.1675 & 4.7106 \\ \cline{1-1} PWM-DITC in [36] & 400 & 36.7\% & 0.2916 & 4.8742 \\ \cline{1-1}  & 800 & 42.5\% & 0.4108 & 5.3400 \\ \cline{1-1}  & 200 & 22.4\% & 0.1481 & 4.3065 \\ \cline{1-1}  & 400 & 33.2\% & 0.2434 & 4.5218 \\ \cline{1-1}  & 800 & 39.4\% & 0.3962 & 4.9945 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental performance at various speeds.  
proving the effectiveness of the proposed method. In future work, a simple SRM model will be considered to implement the proposed method, so as to ensure the control effect while reducing the memory space occupation of LUTs.  
Conceptualization, L.S. and G.W.; methodology, L.S.; software, L.S. and Y.F.; validation, L.S. and J.L.; formal analysis, L.S.; investigation, L.S. and D.L.; resources, L.S. and D.M.; data curation, L.S.; writing--original draft preparation, L.S.; writing--review and editing, L.S. and G.W.; visualization, L.S.; supervision, Y.F.; project administration, G.W.; funding acquisition, Y.F. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The authors declare no conflict of interest.
# Numerical Modeling of Non-Uniformly Reinforced Carbon Concrete Lightweight Ceiling Elements
## 5 Finite Element Simulation of the Carbon Concrete Girder
### Constant Thickness and Smeared Cross Section  
To provide a reference example of the shell simulation with a simple non-scaled version of the cross-sectional parameters, a girder model with a constant thickness of 10 mm was used first. This cross-sectional model shown in Figure 18 (i) corresponded to the original cross section of the tensile test specimens used to identify the integrity functions.  
Three calibrated integrity functions were used in this simulation to reflect the varying cross section within the shell. The zones 5, 7, 9, 11 reinforced with 3300 tex were associated with the strain-hardening behavior \(\sigma_{\mathrm{c}}^{3300}(\varepsilon_{\mathrm{c}})\) given in Figure 9d. The zones 4, 6, 8, 12 reinforced with hybrid fabric 3300 + 800 tex were associated with integrity functions calibrated using the stress-strain curve \(\sigma_{\mathrm{c}}^{3300+800}(\varepsilon_{\mathrm{c}})\) given in Figure 12b. The non-reinforced zones 1, 2, 3 were associated with a plain concrete strain-softening behavior \(\sigma_{\mathrm{m}}(\varepsilon_{\mathrm{c}})\).  
The obtained load-deflection curve (i) shown in Figure 19 has a lower final stiffness than the measured response of the girder test. The reason for the lower stiffness is the fact that the tested girder had additional layers of plain concrete (FCC) in zones 4-12 that were not included in this version of the model.
## 3 The Proposed Fog-Based MQTT Architecture with an Authentication Scheme
### Authentication System
#### 3.2.1 Authentication Architecture  
This section presents the authentication architecture of Mu-TiMB as well as the overview of the interaction between clients, brokers, and AMs.  
As shown in Figure 6, each AM is deployed in each broker, and the AM is in charge of performing an authentication scheme for the associated broker and the group of clients/brokers connected to that broker. For example, the authentication manager of LB2 (AM\({}_{\text{lb2}}\)) manages the authentication for LB2 and its connected clients S2 and P3, while the authentication manager of AB1 (AM\({}_{\text{ab1}}\)) is responsible for authenticating AB1 and its connected local brokers LB1 and LB2, which act as clients. Another example, the authentication manager of CB (AM\({}_{\text{cb}}\)) manages the authentication for CB and its connected aggregation brokers AB1 and AB2. In Mu-TiMB, the authentication process starts once the client/broker sends the normal MQTT CONNECT message to the corresponding broker and completes it successfully after receiving the normal MQTT connection acknowledgment, CONNACK.  
In Mu-TiMB, the AM independently implements the authentication scheme for the associated broker, and their connected group of clients (publishers, subscribers, and brokers that act as clients), as depicted in Figure 7. This creates a multi-tier authentication process, where LB and AB can be authenticated by two different AMs; thus, there are two different authentication parameters, one where they act as a broker and one where they act as a client. For example, LB will be authenticated by AM\({}_{\text{lb}}\) when it receives a connect message from clients and acts as a traditional broker. On the other hand, the same LB will be authenticated by AM\({}_{\text{ab}}\) when it sends a connect message to AB and acts as a client.  
Figure 6: Authentication architecture for Mu-TiMB.
## 3 Results and Discussion
### Lipid Oxidation  
Lipid oxidation triggers oxidative stress and causes rancidity, undesirable taste, odor, discoloration, and a decrease in nutritional value. These changes contribute to a reduction in the consumer acceptability of food [39; 40]. Peroxides, products of polyunsaturated fatty acid oxidation, are unstable and undergo further transformations. Malondialdehydedehyde (MDA) is one of the major end-products of lipid oxidation, the content of which can be determined using the TBARS test [41; 42]. TBARS is widely used for measuring antioxidant capacity based on the MDA content in meat samples [43]. While MDA is not the only product of lipid oxidation able to react with TBA, the meat industry generally recognizes the use of the TBARS assay [44].  
Table 1 shows the effect of turmeric addition on the MDA content in pork meatloaves over 21 days under refrigeration. A significant impact of turmeric was found, with 1% turmeric added exhibiting the lowest content of the MDA compound (\(p<0.05\)). An increase in MDA content was observed on day seven, although nonsignificant, and remained the lowest throughout storage. A significant increase in the MDA on day seven was noted in 3% turmeric-added meatloaf, which remained at a similar level until the last day (\(p<0.05\)). This rise in TBARS can be explained by increased lipid oxidation and the formation of volatile metabolites when oxygen is present [45]. Still, the 3% turmeric-added meatloaf had a significantly lower MDA content than the control meatloaf until day 7 (\(p<0.05\)), which continued to be lower, although insignificantly, as the MDA in the control meatloaf decreased over time. The significant reduction in the MDA content may be because this compound reacts readily with the components found in meat [46]. Therefore, the MDA content and other reactive carbonyl compounds that react with TBA may decrease [47]. Certainly, aldehydes are capable of reacting with other unstable compounds produced during the Maillard and Strecker reactions, reducing MDA content [48].  
The inhibitory effect of oxidation of the turmeric powder addition in meat products was demonstrated by [PERSON] et al. [49], [PERSON] et al. [20], [PERSON] et al. [21], [PERSON] et al. [50], and [PERSON] et al. [7]. It may be due to its ability to trap oxygen-free radicals and act as an antioxidant, primarily owing to its high curcumin content [51]. The results show that fresh rhizomes were highly effective against lipid oxidation in pork meatloaves during refrigeration storage, and the proportion of turmeric had a significant impact. Of note, meatloaves without turmeric tend to reach TBARS levels up to 0.97 mg/kg meat. [PERSON] et al. [52] claimed that a TBARS level of \(\geq\)1 mg/kg meat is the limit at which consumers recognize off-flavors and off-odors in the meat. Others claim the TBARS threshold value may be 2 mg MDA/kg [53]. Although it is difficult to specify the average level of lipid oxidation beyond which meat is unacceptable to the consumer, the inclusion of turmeric helping to delay lipid oxidation in meats such as pork meatloaf has proved valuable.
# Comparative Study of Lightweight Deep Semantic Segmentation Models for Concrete Damage Detection
## 3 Details for Model Training and Evaluation
### Training Details  
Five models were trained on the Python IDE platform using the TensorFlow and Keras [55] libraries. Experiments were conducted on a workstation with two Intel Xeon processor CPUs and four NVIDIA RTX Titan GPUs with 64-GB RAM. The models were trained on multiple GPUs as distributed training, which helped increase the batch size and reduced the training time. During the training, 50% of the training data were randomly selected and augmented at every epoch. Two types of data augmentation options were used on the training images: (1) random flip right and left, and (2) random scale of 1-1.5. All models were trained from scratch, except DeeplabV3+(ResNet-50). To train the deep learning model from scratch all the learnable model parameters, like weights (convolution filters) were initialized randomly according to the input data during training and loss was calculated after each iteration. These model parameters were monitored by loss function and the training stopped when the loss minimized and was close to zero. Models like DeepLabV3+ used backbone network (ResNet-50) for classification tasks and decoder head for semantic segmentation. This backbone network used pretrained weights of ImageNet, excluding last fully connected layer, and helped the model to optimize quickly during training, also known as transfer learning. In this study DeepLabV3+ also used pre-trained weights in the encoder module ResNet-50 as transfer learning. All models had an input image resolution of 680 \(\times\) 680. The global batch size for training was 16 (four per GPU) and a total of 60 K iterations were set to train the models. An adaptive momentum (ADAM) [56] optimizer was employed to effectively train the model by adaptively changing the learning rate. The learning rate was scheduled, starting from 0.001 changes at each iteration and ending at 0.0001 at the completion of training.  
Figure 8: Examples from concrete dataset with damage and respective ground truths (in colors).
## 4 IRS-Aided Wireless Network Performance and Deployment
### IRS as a Wireless Network Signal Reflector
#### 4.1.1 Probabilistic Metrics  
The probabilistic metric can help depict the uncertainty in the quality of wireless transmissions of individual transceivers [91]. An analytic approach to derive the probability of an IRS when it acts as a reflector was proposed in [92]. It was shown that whenever a surface is coated with a MS, any angle of incident can be used to synthesize an angle of reflection [92].
# Analysing Land Cover Change in the Valencian Community through Landsat Imagery: From 1984 to 2022
###### Abstract  
Land cover change represents one of the most significant global transformations, which has profound impacts on ecosystems, biological diversity, and the ongoing climate crisis. In this study, our objective was to analyse land cover transformation in the Valencian Community over the last four decades. Utilising Landsat 5, 8, and 9 summer images, a Random Forest algorithm renowned for its ability to handle large datasets and complex variables, was employed to produce land cover classifications consisting of five categories: 'Urban Areas', 'Dense Vegetation', 'Sparse Vegetation', 'Water Bodies', and 'Other'. The results were validated through in situ measurements comparing with pre-existing products and utilising a confusion matrix. Over the study period, the urban area practically doubled, increasing from approximately 482 to 940 square kilometres. This expansion was concentrated mainly in the proximity of the already existing urban zone and occurred primarily between 1985 and 1990. The Dense and Sparse Vegetation classes exhibit substantial fluctuations over the years, displaying a subtle trend towards a decrease in their cumulative value. Water bodies and Other classes do not show substantial changes over the years. The Random Forest algorithm showed a high Overall Accuracy (OA) of 95% and Kappa values of 93%, showing good agreement with field measurements (88% OA), ESA World Cover (80% OA), and the Copernicus Global Land Service Land Cover Map (73% OA), confirming the effectiveness of this methodology in generating land cover classifications.  
land cover; Landsat; change detection +
Footnote †: journal: _Land_  
0000-0001-5000-0000-00001-5000]S. [PERSON]  
1 Global Change Unit, Image Processing Laboratory (IPL), University of Valencia, E-46980 Paterna, Spain;  
segio.gimeno-crespo@uves (S.G.); virginia.crisafulli@uves (V.C.); aivaro.sobrino@uves (A.S.-G.)1 Correspondence: sobrino@uves (A.S.-G.)1 Correspondence: sobrino@uves
###### Abstract  
Analyzing vegetation coverage and land-use and land cover (LULC) characteristics helps to understand the interaction between human activities and the natural environment. The coastal regions of the Guangdong Province are economically active areas with frequent human activities, located in the advantageous natural environment of South China. This study analyzed the spatiotemporal characteristics of the normalized difference vegetation index (NDVI) and LULC from 2000 to 2020, to explore the response of NDVI changes to LULC changes. The results show that (1) the overall NDVI is relatively high, with a proportion of 85.37% to 89.48% of areas with higher coverage and above categories, mainly distributed in the east and west. Vegetation coverage showed an increasing trend. (2) The LULC in this area is mainly composed of forest land (46.5% to 47.5%) and cultivated land (30.7% to 33.4%), with forest land mainly distributed in relatively high-altitude regions and cultivated land mainly distributed in the plains. The changes in LULC from 2015 to 2020 were relatively significant, mainly due to the mutual transfer of cultivated land and forest land. In addition, built-up land continued to expand from 2000 to 2020, mainly in the Pearl River Delta. (3) The NDVI decreases come from the transfer of various types of land to built-up land, mainly in the Pearl River Delta region, while the NDVI increase comes from the stability and mutual transfer of cultivated land. The net contribution rate of forest land change to vegetation cover change is the most significant (\(-\)38.903% to 23.144%). This study has reference significance for the spatiotemporal characteristics of vegetation cover changes in coastal areas and their response to land-use changes, as well as coastal management and sustainable development.  
vegetation coverage; land-use and land cover change; spatiotemporal characteristics; coastal; South China  
[PERSON], [PERSON], S. Spatiotemporal Pattern of Vegetation Coverage and Its Response to LULC Changes in Coastal Regions in South China from 2000 to 2020. 2020. 2020. 14, 10694. [[https://doi.org/10.3390/app142210694](https://doi.org/10.3390/app142210694)]([https://doi.org/10.3390/app142210694](https://doi.org/10.3390/app142210694))
# Blockchain-Based Multiple Authorities Attribute-Based Encryption for EHR Access Control Scheme
## 6 Scheme Analysis
### Security Analysis
#### 6.2.2 System Robustness  
We use the Shamir secret sharing scheme \((t,n)\) to manage user attributes and distribute user attribute keys, so there is no single point of failure in the system. However, the adversary may bring the system down by attacking multiple AAs. If there are fewer than t AAs in the system, the system will go down. The system's robustness is affected by the threshold \(t\) and the total number \(n\). A threshold \(t\) that is too large introduces additional overhead. Considering the system's security, we can set a suitable threshold value to make the system resist the attack with a high probability.