esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study investigated the effects of aqueous fruit extracts of _Carissa carandas_ (CCA) on inflammation and insulin resistance using an in vitro cellular model, in vivo high-fat diets, and a streptozotocin-induced type 2 diabetic (T2 DM) rat model. CCA significantly ameliorated inflammation by decreasing nitric oxide production in lipopolysaccharide (LPS)-induced RAW 264.7 macrophages. Interestingly, CCA showed anti-insulin resistance activities, as it significantly improved glucose uptake and decreased glycerol release in LPS-induced 3T3-L1 adipocytes. In vivo studies showed that a high dose of 12-week oral supplementation of CCA (400 mg/kg BW/day) significantly reduced visceral fat, triglycerides, and cholesterol level in the blood of diabetic rats. Importantly, the metabolic parameters in both fasting and postprandial states, including fasting plasma glucose, HOMA-IR, and glucose intolerance, significantly improved, indicating its antihyperglycemic benefit in diabetic rats. Moreover, the results of the HOMA-\(\beta\) and histological examination suggested that pancreatic \(\beta\)-cell function and pancreatic morphological changes of the CCA and metformin treatments appeared to be better than those in non-treated diabetes, indicating the protective effect of CCA against pancreatic damage caused by hyperglycemia. In conclusion, the present study first reported that the _C. carandas_ fruit extract has anti-inflammation and anti-insulin resistance, and subsequently improved glycemic control in the T2 DM rat model.  
Carissa carandas Linn; antihyperglycemic; anti-inflammation; anti-insulin resistance; type 2 diabetic rat model +
Footnote †: journal: Applied Sciences
# Crossband Filtering for Weighted Prediction Error-Based Speech Dereverberation
## 4 Experimental Results
### Crossband Filtering--Length Preservation  
In order to evaluate the performance, we set up an experimental configuration where we systematically varied the value of \(L_{\text{cb}}\) for each chosen \(L_{\text{bb}}\). In this setup, we discarded two late samples from the band-to-band component for each increment in the crossband samples in order to maintain fixed computational complexity. Specifically, we explored the range of \(L_{\text{cb}}\) from 0 to \(\lfloor L_{\text{bb}}/3\rfloor\), where \(\lfloor\cdot\rfloor\) denotes the floor function, ensuring that the band  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measure** & \(L_{\text{bb}}\) & **Optimal Gain** & **Optimal \(L_{\text{cb}}\)** \\ \hline \multirow{3}{*}{FWSegSNR} & 14 & 1.23 & 2 \\  & 15 & 1.24 & 2 \\  & 20 & 1.22 & 2 \\ \hline \multirow{3}{*}{CD} & 14 & 1.33 & 2 \\  & 15 & 1.32 & 2 \\  & 20 & 1.31 & 2 \\ \hline \multirow{3}{*}{PESQ} & 14 & 1.76 & 4 \\  & 15 & 1.76 & 3 \\ \cline{1-1}  & 20 & 1.75 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of experimental results (length extension).  
Figure 4: Performance evaluation (Ext.) for \(L_{\text{bb}}=20\) (optimal PESQ) in terms of (**a**) FWSegSNR, (**b**) CD, and (**c**) PESQ.  
Figure 3: Performance evaluation (Ext.) for \(L_{\text{bb}}=14\) (optimal CD) in terms of (**a**) FWSegSNR, (**b**) CD, and (**c**) PESQ.  
to-band component retained its significance relative to the crossband components. Notably, introducing early crossband samples led to an overall improvement in dereverberation performance across all measured criteria. This finding underscores the effectiveness of incorporating the information within the crossband components to enhance the quality of the dereverberated speech signals.
# Differential Characteristics Based Iterative Multiuser Detection for Wireless Sensor Networks
## 4 Simulation Results and Discussions
### Number of Iterations  
First we assume that there exist 50 users simultaneously in a WSN. For balanced Gold sequence with length 127, the cross correlation value \(\rho_{ij}\) can be \(-0.134\), \(-0.008\), and \(0.118\). At the receiver, assume that all the 50 users have the same power. In Figure 5, the receiver employ the proposed iterative multiuser detection algorithm is demonstrated for the first five iterations. The optimal multiuser detector (OMUD) performance is also shown.  
It can be seen in Figure 5 that after four iterations, the BER performance improves little when the iteration continues both for the AWGN and IEEE 802.15.4a CM1 channel. Besides, as a suboptimal multiuser detection algorithm, the proposed iterative multiuser detection algorithm does not converge toward the OMUD performance in this 50 user system. However, we can see that the performance gap between OMUD and the proposed multiuser detector is small after five iterations. For the IEEE 82.15.4a  
\begin{table}
\begin{tabular}{c c} \hline \hline
**System** & **DS-UWB** \\ \hline Modulation mode & BPSK \\ Pseudo random sequences (PRS) & Balanced Gold sequences \\ Length of PRS & 127 \\ Number of tested bits & 50,000,000 \\ Communication channel & AWGN \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
CM1 channel, we can see that there exists a distinct performance loss compare to the AWGN channel. It is mainly because that the S-RAKE receiver does not gather all the paths which results in energy loss.
## 5 Performance Analysis  
To fully verify the performance of the DHFSOA algorithm proposed in this paper, multiple Monte Carlo simulation experiments were implemented in the ocean (3D) node deployment on the Matlab platform(2016b, MathWorks, Natick, MA, USA). The PSSD algorithm is a typical non-uniform deployment algorithm for underwater wireless sensor network nodes. To evaluate the performance of the DHFSOA algorithm, the PSSD algorithm was selected for comparison. Evaluation included simulation, comparison, and analysis of network coverage, coverage efficacy of the event set, and total moving distance of the node. In addition, to eliminate any random effects of individual experiments, the final result was the average of 30 experiments. The parameter settings and experimental parameters of the algorithm are shown in Table 1.
## 1 Introduction  
Isolation technology is the most widely used structural vibration control technology in the world. It can effectively reduce the natural frequency of the structure and make the natural vibration period of the structure far distant from the predominant period of the earthquake [1]. Structural isolation is mainly divided into base isolation and inter-story isolation and base isolation is most used in practical projects [2; 3; 4]. Meanwhile, due to the excellent performance of isolation technology, many different isolation devices (e.g., rubber isolation bearings, lead rubber isolation bearings, friction pendulum bearings, etc.) are used in buildings and bridges [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. However, passive isolation also has many disadvantages. Because of the greater uncertainty of the earthquake, the isolation structure cannot be checked in all cases of design, which may lead to the displacement of the isolation layer exceeding the design requirements. The plastic deformation of isolation bearing cannot be restored, the structure is in danger of rollover and overturning [16; 17; 18; 19; 20].  
To solve this problem, many scholars have adopted the method of adding various dampers (e.g., viscous dampers, magnetorheological dampers, friction dampers, etc.) into the isolation layer of the structure to limit its displacement [21; 22; 23; 24]. As a semi-active control, the input voltage of the magnetorheological (MR) fluid is calculated by the feedback of external excitation to change the viscous coefficient of the MR damper, which can approach the control effect of active control without a large amount of energy input [25; 26; 27; 28; 29; 30; 31; 32; 33], and it also performed well in the isolation layer [34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44]. Therefore, the control strategy of the MR damper is particularly important, and many scholars have done a lot of research on it [45; 46; 47; 48; 49; 50]. However, the MR dampers also have the disadvantage of time-delay. In strong earthquakes, short time-delay may lead to the failure of MR dampers to control structures. Therefore, whether the control of MR dampers under high-intensity earthquakes can be effective is a matter of great concern.  
In this paper, the MR damper isolation system is studied utilizing experiment and numerical simulation. The control effect of MR damper under two control strategies, continuous control and switching control, is analyzed by linear quadratic regulator (LQR) algorithm. At the same time, a viscous damper and friction damper are used to comparative analyze at the same model structure, which verifies the superiority of the control effect of MR damper under two control strategy. Finally, the semi-active MR damper isolation structure under a high-intensity earthquake is tested and the feasibility of the MR isolation system under a high-intensity earthquake is verified.
# Phonocardiography Signals Compression with Deep Convolutional Autoencoder for Telecare Applications
## 2 Related Work
### Autoencoder  
Autoencoders have been widely used in data denoising [32; 33; 34] and data compression [35] applications. An autoencoder comprises encoder and decoder parts. The encoder translates an input vector \(\mathbf{x}\) with length \(N\) to a hidden representation \(\mathbf{y}\) with length \(M\). The transform can be expressed, as follows:  
\[\mathbf{y}=f\left(\mathbf{W}\cdot\mathbf{x}+\mathbf{b}\right), \tag{7}\]  
where \(\mathbf{W}\) denotes the weight matrix of size \(M\times N\), \(\mathbf{b}\) represents the bias vector of size \(M\times 1\), and \(f\left(\cdot\right)\) denotes the transformation function, which is non-linear in general. Note that for data compression purposes, \(N\gg M\) holds. On the other hand, the decoder translates the hidden representation \(\mathbf{y}\) to a reconstructed representation \(\widehat{\mathbf{x}}\) with the same length of \(\mathbf{x}\). The demapping process can be expressed, as follows:  
\[\widehat{\mathbf{x}}=g\left(\mathbf{W}^{\prime}\cdot\mathbf{y}+\mathbf{b}^{ \prime}\right), \tag{8}\]  
where \(\mathbf{W}^{\prime}\) denotes the weight matrix of size \(N\times M\), \(\mathbf{b}^{\prime}\) represents the bias vector of size \(N\times 1\), and \(g\left(\cdot\right)\) denotes the demapping function, which is non-linear in general. The parameters associated with the autoencoder are adjusted by minimizing the following cost function \(J\):  
\[J\left(\mathbf{W},\mathbf{b},\mathbf{W}^{\prime},\mathbf{b}^{\prime}\right)= \frac{1}{L}\sum_{\ell=1}^{L}||\mathbf{x}_{\ell}-\widehat{\mathbf{x}}_{\ell}|| ^{2}, \tag{9}\]  
where \(L\) denotes the length of the training dataset.
## 3 Circuit Architecture for Multi-MFC PMS
### Overview of the Proposed PMS Circuit for Multi-MFC  
The block diagram of the proposed system is shown in Figure 3. Each MFC in the array is individually connected to a low-\(R_{on}\) N-type CMOS switch DMN1019 UVT (Diodes Incorporated(r), Plano, TX, USA), controlled by an extremely low power microcontroller PIC24F16 KA102 (Microchip Technology Inc.(r), Chandler, AZ, USA) [46]. The PMS algorithm embedded in the microcontroller allows testing the total power available in each MFC in the array, by using a power measurement block, as shown in Figure 4. The microcontroller also regulates the power extracted from an individual MFC in the stack by defining the time interleaved EH sequence and the amount of time needed for each MFC before it is harvested again. The DC-DC converter block consists of an ultra-low power boost converter BQ25505 (Texas Instruments Incorporated(r), Dallas, TX, USA) [47]. Finally, the extracted charges are stored in an output supercapacitor that powers the IoT sensor.  
As the output voltage level of the MFC is not high enough for the proposed topology to allow for self-starting operation from the PMS, an external one-time pre-charging of an output secondary capacitor to 1.8 V is required to begin controller operation. Multiple different approaches may be taken to startup the system [48,49].  
Once the system begins extracting energy from the MFC, there is no longer need for an external power source to power the PMS.
# Application of Multimodal AI to Aid Scene Perception for the Visually Impaired
## 1 Introduction
### Challenges in Assistive Vision Systems  
Developing practical assistive vision systems requires solving several key challenges. These include minimizing latency in obstacle detection and feedback, handling dynamic lighting and occlusions, designing ergonomic and non-intrusive wearables, avoiding cognitive overload, and maintaining affordability for wide adoption. To enhance usability, assistive systems must prioritize efficient data processing and intuitive feedback mechanisms while minimizing unnecessary complexity in 3D scene interpretation. Moreover, the level of detail in presenting information should depend on the environment in which a blind person is located. A different level of detail is required indoors, where floor irregularities and door thresholds are unexpected elements, and a different one in an outdoor environment, where there is no need to inform the user, for example, about plants growing on the lawn. As part of previous research and development work, assistive systems for visually impaired users were developed, involving both auditory and haptic representations of the surrounding environment. One solution employed spatialized audio cues to convey directional information [7], while another used a vibrotactile belt equipped with multiple vibrating elements [8]. In both systems, functionality was implemented allowing users to manually adjust scene reconstruction parameters depending on the specific environment, enabling personalized configuration for indoor versus outdoor navigation scenarios.
## 2 Related Work
[25] advanced a deep learning-based decision-level fusion tracking method for infrared and visible light, the results of this method showed that the dual-band fusion tracking has better robustness. Significant developments have been achieved by the aforementioned researchers, most of whose research is based on deep learning methods and mainly focuses on improving fusion methods. However, deep-learning-based object recognition methods usually require a large number of data samples, and the trained networks perform poorly in recognizing images of poor quality, exhibiting insufficient generalization capability. Therefore, in this paper, a vehicle target recognition method based on visible and infrared image fusion using Bayesian inference is proposed. The primary contributions and innovations of this paper are as follows:  
1. In order to better extract target features from visible and infrared images, information fusion based on _IOU_ judgment is used to obtain real target features by comparing the relationship between _IOU_ and the set threshold. 2. Based on the _IOU_ judgment results as parameters for Bayesian inference, vehicle target recognition is achieved, and the feasibility of the method is verified through experiments. The remainder of this paper is organized as follows: Section 2 introduces the research work conducted by researchers. Section 3 elaborates visible light and infrared image feature fusion algorithm. Section 4 states fusion image target recognition based on the Bayesian inference method. Section 5 provides verification methods and calculation results. Finally, Section 6 provides a summary of this paper.
## 2 Reinforcement Steel Corrosion Model
### Flexural Stiffness of Bridge with Corroded Reinforcement  
Concrete cracking caused by reinforcement corrosion and external load will lead to bridge stiffness degradation. The American Concrete Institute uses the product of concrete elastic modulus and effective bending moment of inertia to calculate the bending stiffness of cracked bridges [33], where the effective bending moment of inertia is shown in Equation (12):  
\[I_{e}=\bigg{(}\frac{M_{cr}}{M_{a}}\bigg{)}^{3}I_{g}+\Bigg{[}1-\bigg{(}\frac{M _{cr}}{M_{a}}\bigg{)}^{3}\Bigg{]}I_{cr}, \tag{12}\]  
where \(I_{e}\) is the effective bending moment of inertia, \(I_{g}\) is the bending moment of inertia of the intact bridge section and \(I_{cr}\) is the bending moment of inertia of the cracked bridge section, \(M_{a}\) is the maximum bending moment, and \(M_{cr}\) is the cracking moment, which can be deduced as Equation (13):  
\[M_{cr}=\frac{f_{r}I_{g}}{y_{t}}, \tag{13}\]where \(f_{r}\) is the fracture modulus of concrete, and \(y_{t}\) is the distance from the neutral axis of an intact section to its outer edge.  
The bending stiffness of the bridge is also affected by many factors such as the cross-sectional area reduction of reinforcement and the degradation of bond performance when the reinforcement is corroded. Based on the research of Equation (12) in this paper, the correction coefficient as shown in Equation (14) is proposed to comprehensively characterize the influence of various adverse factors caused by reinforcement corrosion on bridge stiffness.  
\[I_{cc}=\gamma(\rho)I_{c}, \tag{14}\]  
where \(I_{cc}\) is the effective bending moment of inertia of the bridge after reinforcement corrosion, \(\gamma(\rho)\) is the correction coefficient, and \(\rho\) is the corrosion rate of reinforcement. The relationship between the correction coefficient and the corrosion rate of reinforcement is shown in Equation (15) based on the experimental results of flexural mechanical properties of 48 corroded beams [34].  
\[\gamma(\rho)=\left\{\begin{array}{ll}1&\rho\leq 0.05\\ 1.22+12.88\rho^{2}-5.05\rho&\rho>0.05\end{array}\right. \tag{15}\]
# Blockchain-Assisted QoS-Aware Routing for Software-Defined Wide Area Network
## 3 System Architecture  
In packet-switching networks, especially in the SD-WAN, packets do not adhere to a fixed route from source to destination. The paths are determined by the SDN controller, and the packets are routed through networking devices and are subject to change due to external factors, although they often remain constant within a session. Individual packets are routed independently, leading to varying path experiences for the entire session. DiffServ (differentiated services) [23] is an internet standard protocol that can also establish the proposed QoS-aware routing in SD-WAN architecture. This strategy is similar to previous research initiatives [24; 25; 26] made for QoS provisioning. DiffServ [23] provides service differentiation with a set of service classes, including a regular class for bulk traffic and premium classes for reserved traffic. A collective set of routers adhering to a shared DiffServ policy constitutes an SDN controller domain or an autonomous system (AS). In the DiffServ model, premium service marking is confined to the border routers upon entry into each SDN domain, and priority queuing at the interior routers prioritizes service for marked packets. Packet marking utilizes the six bits of the DS field in the IPv4 header, originally part of the IP Type-of-Service bits. DiffServ traffic classes, known as per-hop behaviors (PHBs), determine how routers handle packets. This can serve the requirement of QoS guarantee, as these per-hop behaviors will help to achieve the desired QoS-related support. Figure 1 displays an example scenario where there are a number of SDN providers between cloud end-users and cloud providers. In the next section, the proposed QoS-aware route selection strategy is described in detail, which enables the routing path in SD-WAN to consider QoS requirements.
## 4 Bigram and Traditional Results
### Forecasting Water Levels
#### 4.3.2 Traditional Analysis  
In Table 3, a summary of the main articles found is presented. The studies include the use of CNN and SVR models for groundwater potential mapping, MPMR, RVM, GPR, and ELM for forecasting lake level fluctuations, ANN and ANFIS for reservoir water level prediction, and GTB for estimating water levels in irrigation systems. Additionally, extreme gradient boosting, XGB, and Gaussian process regression models have been employed for groundwater depth estimation and irrigation water demand prediction, while HCA and ANN have been used for annual groundwater level forecasting in the Ogallala Aquifer. Lastly, Landsat 8 OLI-based models were developed for deriving humification indexes in Chinese lakes. The following is a brief summary of some of the articles considered in the table. Figure 12: Conceptual structure and dendrogram for a a forecasting water levels topic. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Techniques** & **Metrics** & **Data** & **Application** \\ \hline \multirow{6}{*}{Convolutional neural network (CNN)} & The results show that the CNN model outperforms the SVR model. This is demonstrated by the higher & The study involves creating 140 groundwater & To develop groundwater potential maps using datasets in South Korea, dividing them into machine learning algorithms (specifically \\  & AUC scores for CNN, & (0.845) datasets for CNN, compared to the AUC) & calibration and testing groups, and using 15 groundwater conditioning factors for model training. & 15 groundwater conditioning factors and management of groundwater resources [66]. \\ \cline{1-1}  & & for the SVR model & & \\ \hline Minimax Probability & The results showed that the MMRR model performed the best among the four models, with the following Reference Metric (WRM), which is \(R=0.5\), MRR \(=0.04\), MRR \(=0.04\), Small-Guillie Efficacy (ENS) \(=0.98\), MRR \(=0.04\), Efficient Learning Machine (ELM) & The data grown from 1981 to 2013, with the period from 1981 to 1993 and for the training phase, and the remaining & GPR, ELM, for forecasting like level fluctuations in Lake Human using \\ \cline{1-1}  & Extreme Learning Machine (ELM) & Definitely 0.995, and Extreme Learning Machine (HEM) & data (from 1994 to 2013) used for testing. & historical datasets [70]. \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFNs) & The best ANN ANN and ANFNs models showed that performance with \(\sim 0.95\), SNR data \(>0.5\), and RMSE \(\sim 0.1\). The optimal NN & daily rainfall and water level data from 1 January 2011 & To develop and compare machine learning models, all CNN models, to detect the water level of the SNF Jackson network, which supplies water to Quito, the capital of Ecuador [71]. \\ \cline{1-1}  & & & &