esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results
### Initial experiment  
An initial experiment was performed to testify the communication performance of sensor nodes designed by us and the LASM with 20 sensor nodes. The wireless sensor node designed by us is shown in Figures 8(a,b). This node mainly consists of a temperature control module, an energy control module, a CC2431, extra ROM&RAM, an RF circuit, a USB controller& interface and a user interface [3]. The temperature on the moon changes from about 102K to 384K [27]. A temperature control module is needed to protect the sensor node from the bad temperatures. A vacuum multiplayer insulation structure is used to passively control the temperature variation. Electric heaters and heat pipes are used to increase the temperature actively if possible [28]. The energy control module is used to make the voltage stable and select the power source. The USB interface is an optional part that is used to communicate with computer. The power can be supplied by either USB or battery. The user interface is an interface that is used for installing various sensors according to the needs of the application. It also gives some communication interfaces such as I2C and UART. CC2431 is the heart of the node, which contains an RF transceiver, a high performance and low-power consumption 8051-based microcontroller, and a location engine. It controls all the other components in the node.  
The experiment field is shown in Figure 8(c). The experiment field is a square of 20 m by 20 m with 20 sensor nodes and a robot deployed in it. The anchor nodes are deployed at (0,0),(0,20),(20,0),(20,20). A sensor node is fixed on the robot for the robot's localization and navigation. The localization results are shown in Figure 8(d). The blue point of each line is the actual position for each sensor node, and the red point of each line is estimated position of each sensor node. The position error is about 2 m in experiment. This is a small scale network in initial experiment. It mainly tests the communication quality of wireless sensor nodes and the feasibility of using LASM for nodes localization and robot's localization. In the next phase, we will increase the network to a 1000 m by 1000 m area with about 1000 wireless sensor nodes deployed in it. A lunar rover will be placed in it for localization and navigation.  
\begin{table}
\begin{tabular}{c c c c} \hline  & Distributed LASM(B) & Distributed LASM(P) & MDS-MAP(P) \\ Communication Cost & \(O(1)\)*n & \(O(1)\)*n & \(O(n\log n)\) \\ \hline  & Centralized LASM(B) & Centralized LASM(P) & MDS-MAP(C) \\ Computational Complexity & \(O(1)\)*n & \(O(1)\)*n & \(O(n^{3})\) \\ \hline \end{tabular}
\end{table}
Table 1: The Complexity of LASM and MDS-MAP.
## 3 Proposed Architecture
### TDOA Measurement Using Generalized Cross Correlation Phase Transform (GCC-PHAT)  
To estimate the location of sound sources from TDOA observations, we use the GCC-PHAT method. As mentioned before, the GCC-PHAT is a method of obtaining the correlation coefficient of signals using phase transform, and operates in the frequency domain as that requires less calculation that the time domain. Algorithm 2 shows the algorithm for obtaining the TDOA measurement using acoustic signal input from two acoustic sensors. The TDOA measurements can be obtained by using the Fourier transform process and inverse Fourier transform process of two acoustic signals as in the algorithm. The position of the sound source in the three-dimensional space can be obtained from these measurements. In this paper, we propose performing the calculation process in parallel and accelerating the operation speed of the entire algorithm.  
```
1:for two signal \(x_{a}[n]\) and \(x_{b}[n]\)
2:\(X_{a}(f)=FFT(x_{a}[n])\)
3:\(X_{b}(f)=FFT(x_{b}[n])\)
4:\(G_{PHAT}(f)=\frac{\chi_{a}(f)|\chi_{b}(f)|^{*}}{|\chi_{a}(f)|\chi_{b}(f)|^{*}|}\)
5:\(I_{GPHAT}(p)=InverseFFT(G_{PHAT}(f))\)
6:\(TDOA\) measurement \(T_{ab}=argmax_{p}(I_{GPHAT}(p))/sampling\) rate
7:endfor
```  
**Algorithm 2** GCC-PHAT
## 3 Results and Discussion
### TG vs. MCC  
We performed TG measurements at the same heating rates as in the MCC experiments, and the results of both experimental techniques are compared in Figures 7 and 8. Based on the procedure proposed in work [40], for the TG and MCC data, the reaction rates were calculated from the Formulas (2)-(4), respectively:  
\[\dot{r_{m}}=-(dm/dt)/(m_{0}-m_{\infty}) \tag{2}\]  
\[\dot{r_{q}}=\dot{q}/\overline{\Delta q} \tag{3}\]  
\[\Delta q\prime=\dot{q}/\dot{r_{m}} \tag{4}\]  
where:  
\(\dot{r_{m}}\) the reaction rate of weight loss, s\({}^{-1}\);  
\(dm/dt\) the weight derivative over time;  
\(m_{0}\) the initial mass of the sample, kg;  
\(m_{\infty}\) the final mass of the sample, kg;  
\(\dot{r_{q}}\) the reaction rate of heat release, s\({}^{-1}\);  
\(\dot{q}\) the specific heat release rate, W/g;  
\(\Delta q^{\prime}\) the heat of combustion per unit mass of the sample, J/g;  
\(\overline{\Delta q\prime}\) the total heat of combustion per unit mass of the sample, J/g.  
Such a comparison leads to the conclusion that most of the sample mass is lost during the main step, and also that most of the heat release occurs in the oxidation of volatiles produced in the same step. This indicates that the heats of combustion of the volatiles produced in the main step are very different for the samples with and without nanofiller. The heat of combustion evaluated by dividing the measured heat release rate by the measured mass loss rate is plotted in Figure 8 as a function of sample temperature.  
Figure 7: Comparison of the thermogravimetry (TG) and microscale combustion calorimetry (MCC) measurements by determining the reaction rates detected by TG and MCC as a function of temperature during the thermal decomposition of the PA6 samples (heating rate 1 \({}^{\circ}\)C/min): (**A**) the samples without nanofiller; and (**B**) the samples with nanofiller.  
It can be seen that the volatiles released up to 400 \({}^{\circ}\)C have a very low calorific value (\(\sim\)2.5 kJ/g for the samples without MMT, and \(\sim\)3.7 kJ/g for the samples with MMT, approximately), while most combustible volatiles (with the heat of combustion in the range of 20-40 kJ/g) are produced at higher sample temperatures around 500-600 \({}^{\circ}\)C. It is worthy to note that the integral heat of combustion differs significantly from these values (\(\sim\)29.23 kJ/g for the samples without MMT and \(\sim\)28.49 kJ/g for the samples with MMT, approximately). We noticed that, with increasing applied layers, the heat of combustion in the final stage of combustion is reduced to almost half, with 15 layer deposition in both types of samples, with or without MMT. However, the treated samples have a slightly better char forming ability, which was also observed in both the TG and MCC investigations.
## 4 Analysis of Security and Performance
### Performance Analysis
#### 4.2.3 Performance Analysis  
The construction of a neighbor set is performed offline, so the load associated with pairing is ignored. During message authentication, the message must be signed to show that it has been sent by a legal vehicle and the signature must be verified. The numbers of computations in message signing and verification are measured. The computations may be bilinear pairing (\(T_{P}\)), EC multiplication (\(T_{m}\)), exponential (\(T_{e}\)) or _HMAC_. The computation times for \(T_{P}\), \(T_{m}\), \(T_{e}\) and _HMAC_, measured on a 3 GHZ Pentium 4 PC [16,17] are 4.5 ms, 0.6 ms, 0.54 ms and 0.002 ms, respectively. Table 7 shows the number of computations and times required by the proposed and other schemes. In the proposed scheme, the generation of \(F(x)\) in signing and the calculation of the _HMAC_ key \(K\) are computations of a polynomial function. The computing time can be ignored, so the computations that are involved in signing or verifying in the proposed scheme are _HMAC_ computations only.
# Cell Therapy in the Treatment of Coronary Heart Disease
## 2 Non-Cardiac Origin Cells for Cardiac Cell Therapy  
### Skeletal Myoblasts  
Skeletal muscle myoblasts have been the first cell type to be investigated in preclinical and clinical studies of cell therapy for CHD [23]. These cells possess the advantage of being easily obtainable from the patient, as well as having the ability to differentiate into  
Figure 1: Basic approaches to cell therapy for coronary heart disease: transplantation of adult stem/progenitor cells releases cardioprotective factors through paracrine effects; cardiomyocytes or their precursors derived from induced pluripotent stem cells can also be transplanted to replace lost myocytes; tissue engineering approaches combining cells with biomaterials can create functional heart tissue in vitro for transplantation; small molecules such as growth factors or microRNAs can be delivered to promote cardiomyocyte proliferation or angiogenesis for wound healing.  
contractile cells [24]. Myoblasts are derived from myosatellite cells present beneath the basal lamina of skeletal muscle fibers [12]. They are identified by the expression of Pax7, Pax3, c-Met, M-cadherin, CD34, syndecan-3, and calcitonin [25]. In mature muscle, satellite cells are typically quiescent [26]. However, injury activates satellite cells and induces MyoD expression, leading to cell cycle entry and the generation of myogenic precursors or myoblasts [27]. After multiple cell divisions, myoblasts exit the cell cycle, fuse with each other, and create multinucleated myotubes that develop into mature muscle fibers. The ability of myoblasts to form muscle fibers in regenerating muscle and ectopic muscle fibers in non-muscle tissues is a potential therapeutic approach for Duchenne muscular dystrophy, urological dysfunction, and heart failure [28].  
Early uncontrolled clinical studies have reported that transplanted skeletal myoblasts engraft into the heart and result in better cardiac performance in patients [11]. However, a subsequent randomized controlled trial of transpicardial autologous skeletal myoblast transplantation during coronary artery bypass grafting in 97 patients with severe left ventricular dysfunction did not reproduce these findings. At 6 months post-surgery, patients who received cell treatment did not demonstrate any significant differences in the functional state of the left ventricle compared to the control group. However, there was an increased occurrence of ventricular tachyarrhythmia, which resulted in early termination of the study [12]. Similar results were observed in the SEISMIC clinical trial, where transendocardial skeletal myoblast transplantation was used. Four years following the procedure, no discrepancies were observed in the left ventricular function between the experimental and control groups [29]. It is generally accepted that the transplantation of skeletal myoblasts is not sufficient to provide a complete electrical conduction system in the heart [30].
## 5 Results
### Stability Derivatives  
Aerodynamic derivatives, the most crucial from a stability point of view, are presented in this section. First, derivatives that play the most important role in the case of the longitudinal modes of motion are presented. The derivatives of the pitching moment with respect to the angle of attack and pitch rate versus the Mach number are shown in Figure 9. The graph on the left presents the derivative, which is the measure of the so-called static margin, and influences both longitudinal modes, i.e., phugoid and short-period oscillation. The trend of both sets of data is consistent. The MAGERO results show a lower static stability, which is caused by the presence of cross flow. The right graph presents damping derivative \(Cm_{q}\). The results show that a very good accordance between methods was achieved and that the impact of compressible effect has negligible effect.  
Figure 10 presents the most important derivatives for lateral modes of motion. The left graph shows the derivative of the yawing moment with respect to the sideslip angle. A notable difference between data sets is visible on the plot. In the case of the asymmetrical flow condition, a vortex around the side plates occurs. In the case of the potential flow, the derivative is the smallest because the increment in force due to vortex is not considered. In the MAGERO and wind tunnel results, the derivatives are significantly higher, but for higher AoAs, a decrement in derivative can be observed due to the change in vortex position (Figure 11). The last derivative that is shown (left graph) is the rolling moment derivative with respect to the side slip angle. A similar trend can be observed for the directional derivative, which is also associated with the change in vortex position.  
Figure 10: Lateral stability derivatives vs. angle of attack for different Mach numbers—yawing moment coefficient with respect to sideslip angle (**left**); rolling moment coefficient with respect to sideslip angle (**right**).  
Figure 9: Longitudinal stability derivatives vs. Mach number—pitching moment coefficient with respect to angle of attack (**left**); pitching moment coefficient with respect to pitch rate (**right**).
Future Internet_**2014**, \(6\), 126-154; doi:10.3390/fi6010126  
_future internet_  
**ISSN 1999-5903**  
www.mdpi.com/journal/futureinternet  
Information Integration Platform for Patient-Centric Healthcare Services: Design, Prototype and Dependability Aspects  
**[PERSON]**  
Department of Information and Communication Technology, University of Agder, [PERSON] 9, Grimstad 4879, Norway; E-Mail: [EMAIL_ADDRESS]; Tel.: +47-3723-3791  
_Received: 1 November 2013; in revised form: 30 January 2014 / Accepted: 8 February 2014 / Published: 6 March 2014_  
**Abstract:** Technology innovations have pushed today's healthcare sector to an unprecedented new level. Various portable and wearable medical and fitness devices are being sold in the consumer market to provide the self-empowerment of a healthier lifestyle to society. Many vendors provide additional cloud-based services for devices they manufacture, enabling the users to visualize, store and share the gathered information through the Internet. However, most of these services are integrated with the devices in a closed \"silo\" manner, where the devices can only be used with the provided services. To tackle this issue, an information integration platform (IIP) has been developed to support communications between devices and Internet-based services in an event-driven fashion by adopting service-oriented architecture (SOA) principles and a publish/subscribe messaging pattern. It follows the \"Internet of Things\" (IoT) idea of connecting everyday objects to various networks and to enable the dissemination of the gathered information to the global information space through the Internet. A patient-centric healthcare service environment is chosen as the target scenario for the deployment of the platform, as this is a domain where IoT can have a direct positive impact on quality of life enhancement. This paper describes the developed platform, with emphasis on dependability aspects, including availability, scalability and security.  
**Keywords:** Internet of Things; integration platform; middleware; REST; publish/subscribe; availability; scalability; security; healthcare
## 3 Results and Discussion
### Kinetic Study  
Figure 6a,b shows the kinetics of the developed PAN-oxime fiber in single and binary metal systems, respectively, and Figure 6c shows the kinetic results of the AmberjetTM 4200 resin in the binary metal system. As a result, more than 90% of the adsorptions by PAN-oxime fiber were  
Figure 5: Adsorption isotherms of PAN–oxime fiber and AmberjetTM 4200. (**a**) Single-metal system; (**b**) binary metal system; and (**c**) selectivity. Standard errors are less than 5%.  
completed within 10 min, while Amberjet(tm) 4200 was done within 120 min of contact with the metal solution. This indicates that PAN-oxime fiber has much better sorption kinetics than Amberjet(tm) 4200, because the former supports plenty of binding sites as a thin fiber form. In order to evaluate the kinetic results, the data points were fitted by pseudo-first- and pseudo-second-order models, which can be represented by their nonlinear kinetic equations as follows [22]:  
\[\text{Pseudo-first-order model}:\ q_{\text{t}}=q_{1}\left(1-\exp\left(-k_{1}t \right)\right) \tag{4}\]  
\[\text{Pseudo-second-order model}:\ q_{\text{t}}=q_{2}^{2}k_{2}t/\left(1+q_{2}k_{2}t\right) \tag{5}\]  
where both \(q_{1}\) and \(q_{2}\) are the amount of adsorbed metal ions at equilibrium (mg/g), \(q_{\text{t}}\) is the amount of adsorbed metal ions at any time \(t\) (mg/g), \(k_{1}\) is the rate constant in the pseudo-first-order model (min\({}^{-1}\)), and \(k_{2}\) is the rate constant in the pseudo-second-order model (g/mg\(\cdot\)min). The experimental and predicted equilibrium uptakes, rate constants, and correlation coefficients are exhibited in Table 2.  
Figure 6: _Cont._  
As shown in Figure 6a, high amounts of Pd(IV) and Pt(II) were rapidly adsorbed onto the PAN-oxime fiber in single-metal conditions, whose final uptakes were 235.52 and 162.16 mg/g, respectively. In the binary metal systems, the final uptake of Pd(II) by the PAN-oxime fiber was 221.00 mg/g, but the Pt(IV) uptake could not be estimated by the pseudo-second-order because an unusual phenomenon of Pt(IV) adsorption by PAN-oxime fiber was observed (Figure 6b). If it was visually checked, it was around 20-25 mg/g after 30 min. Again, in the beginning, Pt(IV) was adsorbed together with Pd(II) onto PAN-oxime fiber. However, as the Pd(II) uptake increased, the uptake of Pt(IV) sharply declined, which shows that initially adsorbed Pt(IV) was desorbed in a few minutes. This implies that Pd(II) has stronger binding affinity with the sorbent than Pt(IV). Although the commercial resin Amberjet(tm) 4200 has higher sorption capacities for both metal ions than PAN-oxime fiber in the single-metal system, it does not have the selective binding property in the binary system. However, the developed PAN-oxime fiber shows selectivity toward Pd(II) in the binary system. In addition, PAN-oxime fiber can bind metal ions much more quickly than the resin.
# Fault Detection and Isolation for Redundant Inertial Measurement Unit under Quantization
## 2 Materials and Methods
### The Three-Channel Filter for FDI  
#### 2.2.1 Fault Classification  
Only when the amplitude of a gyroscope's fault increases to \(100^{\circ}/\mathrm{h}\) is the detection performance improved. There are three categories of fault according to its amplitude: hard fault, medium fault and soft fault [24]. The fault of a laser gyroscope has the following categories, as shown in Table 1 (gyroscope bias: \(0.05^{\circ}/\mathrm{h}\)).  
Hard fault has a comparatively large amplitude which primarily affects flight control performance. Medium fault has a medium amplitude which affects pilot display performance. Soft fault has a comparatively small amplitude which affects navigation performance.
## 2 Methodology
### YOLOv10-N Architecture  
Among the many real-time object detectors, YOLO has gained increasing popularity due to its excellent balance between performance and efficiency. YOLOv10 is the latest masterpiece in the YOLO series [37], proposed by researchers at Tsinghua University, and it outperforms previous versions on the COCO dataset. The main improvements compared to earlier versions are as follows:  
(1) Non-maximum suppression (NMS) has been removed during the inference process to reduce inference latency.  
(2) Optimizations have been made to various components (such as lightweight classification heads, spatial-channel decoupled down sampling, and rank-guided block design) to reduce computational load.  
(3) Performance is enhanced by extending large-kernel convolutions and incorporating partial self-attention modules.  
Figure 2 shows the YOLOv10n architecture drawn based on the description in the paper. The architecture is built on the previous versions of the YOLO model and consists of the following three main parts:  
(1) Backbone: Responsible for feature extraction from the input image. The v10 version uses CSPNet (Cross-Stage Partial Network) to reduce computational redundancy.  
(2) Neck: Responsible for fusing features of different scales extracted by the backbone and passing them to the detect head.  
(3) Detect Head: Generates specific feature maps based on the specified number of categories. v10 Detect cleverly combines the one-to-many head and one-to-one head, eliminating the need for NMS.  
As shown in Table 1, YOLOv10 has six versions. The Latency is measured using TensorRT FP16 on a T4 GPU, and the AP\({}^{val}\) parameter is calculated based on the COCO  
Figure 2: Baseline model architecture (YOLOv10-N).  
dataset with an input image size of 640 \(\times\) 640. Among these versions, YOLOv10-N stands out for its lightweight design and high performance, making it suitable for tasks requiring real-time detection under resource-constrained conditions.  
The YOLOv10-N model has only 2.3 million parameters, a computational cost of 6.7 GFLOPs, and an inference latency as low as 1.84 ms, making it ideal for deployment on devices with limited hardware resources. Despite its compact size, it achieves an excellent balance between accuracy and speed. Since corrosion detection applications often require real-time video-based detection, it is crucial to enhance detection accuracy while maintaining high speed. Tus, the YOLOv10-N version is an optimal choice for such scenarios.
# End-To-End Convolutional Neural Network Model for Fear Fault Diagnosis Based on Sound Signals
## 2 Model Building
### Architecture and Parameter of End-To-End Stacked CNN Model  
Our end-to-end stacked CNN model is shown in Figure 3.  
The fusion and fault feature learning module contains two convolutional layers (before the pooling layer) to adaptively extract features from the sample matrices, which contains time and frequency domain signals with about 16,896 sampling points, with four channels. Each layer has 32 filters. The receptive field is set as (8,1) and the stride is set as (1,1) in both convolutional layers. This small filter size with two convolutional layers are set according to the experimental results of the Automatic speech recognition (ASR) task [25], which demonstrated that by using multi-convolutional layers with small receptive fields, the convolutional neural network model has the ability to extract local features of various scales hierarchically. The output of these two convolutional layers is noted as the \"time-frequency\" feature representation. After that, the non-overlapping maximum pooling with a 64 pooling size can be applied to the output of the convolutional layers. The output of the pool matrix has a size of \(264\times 1\times 32\) (two-dimension feature matrix \(\times\) channel). Then, the pool matrix is reshaped from \(264\times 1\times 32\) to \(264\times 32\times 1\) in order to convolve in both frequency and time. Finally, the three-dimensional matrix \(264\times 32\times 1\) is used as input of \"conv3\" to achieve gear fault pattern classification.  
Figure 3: Architecture of the end-to-end stacked CNN. It consists of two parts: fusion and fault feature learning module (**a**) and fault pattern recognition module (**b**).  
The fault pattern recognition module contains five convolutional layers and one fully connected layer. The first two layers of module (b) uses 48 filters with a receptive field of (5,5) and stride of (1,1). The third and fourth layers have the same setting as the first two layers in the filters and receptive field, but the stride is set to (2,2). The last convolutional layer in module (b) uses 64 filters with a receptive field of (5,5) and stride of (2,2), and the hidden unit of fully connected layer is set to 200. In addition, the rectified linear unit (ReLU) is used as the activation function followed by each convolutional layer and fully connected layer. The output of the fully connected layer is followed by a softmax activation function. The detailed parameter of each layer is list in Table 1.