esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Analysis
### Effect of Initial Contention Window of Stations Affiliated with an MLD  
Figure 14(a),b shows the effect of the initial CW of MLD on overall network performance. In these cases, we fixed the initial CW of single-link devices to \(CW_{min}\), which was set to 15 in 802.11 baseline specification. Since the legacy framework had already been operated, we did not manipulate \(CW_{min}\) of single-link devices.  
In cases when RTS/CTS exchange was disabled, as shown in Figure 14(a), we can see that pursuing equality between MLD and single-link devices helped improve the overall network throughput. Reminding that we set the initial CW of single-link devices as 15, per-link throughput when the initial CW of MLD was the same as the initial CW of the single-link device generally outperformed throughputs for other CW values.  
The situation with RTS/CTS exchange enabled was somewhat different, as shown in Figure 14(b). We can see that the situation where the initial CW of the MLD was the smallest was better than the others since there was less risk of collision when RTS/CTS exchange was disabled than when RTS/CTS exchange was enabled. Prioritizing MLD with a lessened risk of collision could take advantage of a multi-link environment.  
Figure 14: Sum of saturated throughput depending on PPDU length (**a**) when RTS/CTS exchange is off, and (**b**) when RTS/CTS exchange is on.
## 3 System Architecture and Design of Intelligent Reflecting Surface (IRS)  
The growth of EM-MMs has been tremendous in recent times due to their ability to manipulate the EM waves in unconventional ways, leading to phase shift, amplitude modulation (AM) and polarization conversion of EM waves [80]. Coding digital and programmable MMs are realized by simplified design and optimized phase reflection coefficient, thus realizing a simple and an efficient way to manipulate the EM waves for various application including mmWave and terahertz frequencies which can be independently controlled by a field-programmable gate array (FPGA) program, thus creating a link between the physical and digital world [51; 80].  
Figure 3 shows a MS consisting of a large array of passive elements with the ability to transform impinging EM waves to several directions in real time [81]. In Figure 3a, there is a clear LoS between the BS and the smartphone. Therefore, the presence of IRS will enable the generation of a replica copy and result in improved diversity of the system. While in Figure 3b, the LoS is blocked due to the presence of the high rise building, the IRS will help establish a virtual link between the BS and the smartphone. This virtual link will enable the receiver to achieve the transmitted signal, thus improving the overall EE and SE of the system, which was not possible without the presence of the IRS.
## 3 Aerodynamic Drag and Noise of Cavities with Different Shapes
### Results of Optimized Cavities  
According to the previous results, the cavity with a rounded edge has better aerodynamic performance than the sloped edge. Therefore, the cavity edges are optimized by rounded. In addition, the bottom surface of the cavity can also be further optimized with rounded and sloped edges. The geometric model is shown in Figure 11, and \(R/H\) is selected as 1/4, 2/3, 1/1, 3/2, and 4/1.  
The results of the \(C_{d}\) of the cavity are shown in Table 4. At the same \(R/H\), the \(C_{d}\) of the cavity can be further reduced by optimizing the bottom surface of the cavity to rounded or sloped edges, and the effect of the sloped edges is better. When the roundness of the cavity edge is small (\(R/H\) = 0.25), the rounded and sloped bottom surfaces can reduce drag by 5.29% and 6.33%, respectively. With the increase of \(R/H\), the aerodynamic drag coefficient of the cavity gradually decreases, and the influence of the modification of the bottom surface on the aerodynamic drag coefficient of the cavity also decreases.  
Figure 12 shows the streamline distribution around the cavity with \(R/H\) = 4. The optimization of the bottom surface of the cavity to sloped or rounded edges can suppress the generation of vortices downstream of the cavity to a certain extent, thereby achieving drag reduction. Both optimization methods do not significantly reduce the recirculation zone behind the upstream surface of the cavity, so the drag reduction effect is limited. Compared with the original rectangular cavity, the aerodynamic drag of the final optimized cavity model is reduced by 36.04%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**R/H** & **Unmodified** & **Rounded Edge** & **Difference (\%)** & **Sloped Edge** & **Difference (\%)** \\ \hline
1/4 & 0.179 & 0.169 & \(-\)5.29 & 0.168 & \(-\)6.33 \\
2/3 & 0.157 & 0.155 & \(-\)1.37 & 0.153 & \(-\)2.41 \\
1/1 & 0.154 & 0.152 & \(-\)1.16 & 0.151 & \(-\)2.13 \\
3/2 & 0.152 & 0.151 & \(-\)1.02 & 0.150 & \(-\)1.79 \\
4/1 & 0.144 & 0.143 & \(-\)0.69 & 0.142 & \(-\)1.39 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The aerodynamic drag coefficient of the cavity with different bottom surfaces.  
Figure 11: Modification of the bottom surface of the cavity.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**\(\theta\) (\({}^{\circ}\))** & **OASPL** & **Difference (dB)** & **\(R/D\)** & **OASPL** & **Difference (dB)** \\ \hline
0 & 81.25 & — & 0 & 81.25 & — \\
15 & 80.13 & \(-\)1.12 & 0.2 & 76.43 & \(-\)4.82 \\
30 & 78.78 & \(-\)2.47 & 0.4 & 74.28 & \(-\)6.97 \\
45 & 75.83 & \(-\)5.42 & 0.6 & 72.23 & \(-\)9.02 \\
60 & 74.35 & \(-\)6.9 & 0.8 & 71.12 & \(-\)10.13 \\
75 & 73.21 & \(-\)8.04 & 1.0 & 71.71 & \(-\)9.54 \\ \hline \hline \end{tabular}
\end{table}
Table 3: OASPL at the side receiver positions.
# Sustainability Enhancement and Evaluation of a Concrete Dam Using Recycling
## 4 Results and Discussion
### The Effects of Compressive Strength of the Concrete  
Concrete is a major contributor to the environmental impacts of dam construction, with cement being the most critical factor due to its high carbon footprint. The compressive strength of concrete plays a crucial role in determining the overall sustainability of the structure. Increasing compressive strength can reduce the required volume of concrete in the dam, thereby lowering material consumption. However, this comes at the cost of increased cement content in the mix, which has a significant environmental impact due to its energy-intensive production process.  
To explore this trade-off, the effects of different concrete compressive strengths--20 MPa, 30 MPa, 40 MPa, and 50 MPa--are investigated. For each case, the dam is designed to meet the required structural stability while minimizing material use.  
By considering the design criteria, the required volume of concrete for a 1 m width of the dam varies with the compressive strength of the concrete. For compressive strengths of 20 MPa, 30 MPa, 40 MPa, and 50 MPa, the corresponding concrete volumes are 994.3 m\({}^{3}\), 895.4 m\({}^{3}\), 890.5 m\({}^{3}\), and 887.2 m\({}^{3}\), respectively. Figure 11 presents the carbon footprint associated with the construction of 1 m width of dam using different compressive strengths. The results indicate that although lower compressive strength requires a larger volume of concrete, the total carbon footprint remains lower due to the reduced cement content in the mix. In other words, increasing compressive strength leads to a decrease in concrete volume, but results in a higher carbon footprint, primarily due to the increased cement consumption, which is the dominant contributor to greenhouse gas emissions in concrete production.  
Figure 10: The effect of recycling steel and concrete on the overall cost of dam destruction.
## 2 Experimental of the Metallization of Textiles  
This section describes two procedures of the textile metallization: \(Cu\) and \(Ni\) plating. The next section presents results of this metallization. The electroless \(Cu\) plating is performed after the procedure described in [11], with slight changes due to the textile substrate in the following sequence [12; 13; 14]:  
* Sensitization in \(SnCl_{2}\) (8 g/L, the \(PH\) value is adjusted to 1 by \(HCl\) (37 wt%) drop by drop into the solution): the textile sample is dipped in the solution for 10 minutes at room temperature (RT) one after another,
* Rinsing in deionized water two times,
* Activation in \(PdCl_{2}\) (0.2 g/L): \(H_{3}BO_{3}\) (20 g/L) (the \(PH\) value is adjusted to 2 by \(HCl\) by adding \(HCl\) (37 wt%) drop by drop into the solution): the sensitized textile is dipped into the activation solution for 10 min at RT,
* Rinsing thoroughly with deionized water,* The \(Cu\) electroless plating bath consists of 10 g/L of \(CuSO_{4}\) 5\(H_{2}O\), 50 g/L of \(KNaC_{4}H_{4}O_{6}\) + \(H_{2}O\), 10 g/L of \(NaOH\), prior to the plating, 15 mL/L of \(CHOH\) is added as a reducing agent,
* The activated textile is dipped into 100 mL of the prepared \(Cu\) plating bath, and the process takes place at RT for 30 min. During the process, the textile is flipped several times so that the evolved gas can be released and a homogeneous coating can be realized. The deposition is performed at room temperature for 30 min. The electroless \(Ni\) plating is performed after the procedure described by [15] with slight modifications. The sensitization and activation procedures are the same as the above-described electroless \(Cu\) plating. The manufacturing process includes the following steps:
* Sensitization in \(SnCl_{2}\) (8 g/L, the \(PH\) value is adjusted to 1 by \(HCl\) (37 wt%) drop by drop into the solution). The textile sample is dipped in the solution for 10 min at RT one after another,
* Rinsing in deionized water two times,
* Activation in \(PdCl_{2}\) (0.2 g/L): \(H_{3}BO_{3}\) (20 g/L) ( the \(PH\) value is adjusted to 2 by \(HCl\)), dipped for 10 min \(RT\),
* Rinsing thoroughly with deionized water,
* The electroless nickel plating bath containing 8 g/L of \(Na_{3}C_{6}H_{5}O_{7}\cdot 2H_{2}O\), 5 g/L of \(NiSO_{4}\cdot 7H_{2}O\), 18 g/L of \(NH_{4}Cl\), 15 g/L of \(NaH_{2}PO_{2}\cdot H_{2}O\), The \(PH\) of the solution is adjusted to \(PH\) 10 by adding \(NaOH\) (10 wt%) prior to the electroless Ni plating.
* For each sample, 100 mL of the solution is used and the sample is plated separately. The process takes place at RT for 30 min. During the process, the textile is flipped several times so that the evolved gas can be released, and a homogeneous coating can be realized.
# Passive Power Line Communication Filter Design and Benchmarking Using Scattering Parameters
## 2 Filters Integration, Topologies, and Impact in the Power Grid
### Services Present in the Power Grid
#### 2.3.2 Ripple Control System  
Ripple control systems are devices used by the grid operator for load switching. Ripple control receivers are components of a system of remote control permitting the simultaneous switching of many loads from a central point. For the system to function it is necessary that the signal arrives from the injection point to the receiver sites. The ripple control signal frequency band is between 110 Hz and 3000 Hz [31] so it might be easily filtered out by the PLC filter.
# Lossless and High-Throughput Congestion Control in Satellite-Based Cloud Platforms +
## 2 Related Works  
Currently, the congestion control schemes of NDN are based on the principle that each Interest can retrieve one Data. Therefore, the traffic of Data packets can be constrained by limiting the sending rate of Interests. The existing congestion control schemes are mainly divided into three types: consumer-side congestion control, hop-by-hop congestion control, and hybrid congestion control.
## 2 Literature Review
### Summary of Existing SER Models
#### 2.1.3 Hybrid Models and Large Pre-Trained Models  
Hybrid models combine different network types and techniques to leverage their strengths. For instance, Parallel CNN-Attention networks trained on datasets such as RAVDESS have shown improved accuracy when data augmentation is applied. Additionally, combining Convolutional RNN with handcrafted features has resulted in a weighted accuracy (WA) of 60.35% and an unweighted accuracy (UA) of 63.98% [24]. Large pre-trained models like Wav2 Vec 2.0 and HuBERT have significantly advanced SER. Self-supervised pre-trained models for speech, such as Wav2 Vec 2.0, achieved superior results compared to previous work on the IEMOCAP and RAVDESS datasets [25]. These models leverage deep learning techniques to extract high-level representations from raw audio data and have achieved state-of-the-art performance on various SER benchmarks. By pre-training massive amounts of unlabeled data, these models can learn to capture complex patterns in speech that are relevant for emotion recognition, resulting in improved accuracy and generalization [22; 25].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Inputs** & **Emotions** & \
\begin{tabular}{c} **Weighted** \\ **Average** \\ \end{tabular} &
\begin{tabular}{c} **Unweighted** \\ **Average** \\ \end{tabular} \\ \hline ELM [16] & Segment-level Features & A,H,N,S & 52.13\% & 57.91\% \\ DNN & 40 Mel-scaled Filter & & 62.55\% & 58.78\% \\ CNN [15] & Banks & A,E+H,N,S & 64.78\% & 60.89\% \\ LSTM-RNN [18] & Mel-scaled Filter banks & A,E+H,N,S & 61.71\% & 58.05\% \\ CNN-BiLSTM [20] & Short-term Fourier & A,H,N,S & 68.8\% & 59.4\% \\ LSTM [17] & Transformation Spectrogram & A,H,N,S & 52.13\% & 57.91\% \\ CNN-BiGRU [19] & Variable Length & A,H,N,S & 71.45\% & 64.22\% \\ CNN (VGG19) [21] & Log Mel-Scale & A,H,N,S & - & 53.6\% \\ Residual Adapter on & Magnitude & A,H,N,S & 72.73\% & 67.58 \\ VoxCeleb2 (ResNet) [22] & Spectrograms & A,H,N,S & 60.35\% & 63.98\% \\ DNN/ & MFCC & A,H,N,S & 60.35\% & 63.98\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Deep learning methods in speech emotion recognition systems using IEMOCAP.
# A Semi-Analytical Approach for the Linearized Vibration of
## 5 Conclusions  
The present research paper proposed a semi-analytical approach for the dynamic response analysis of beam structures, considering the effects of thermal loading and initial deformation. Static deformation analysis under static and thermal loads is conducted using the Rayleigh-Ritz method, and the structural dynamic equation of the beam under static-thermal coupling is established. A segmented solution method and transfer matrix method based on the theory of local homogenization technique is used to solve the variable coefficient differential equation. Based on the linearized modal analysis, a response analysis method considering static and thermal effects is established using the mode superposition technique. The effects of single static load, single thermal load, and static-thermal combined load on structural responses are analyzed.  
It has been demonstrated that uniform thermal stress resulting from thermal loads can decrease structural stiffness and lower mode frequencies while leaving the mode shape unaffected. On the other hand, non-uniform stress resulting from static deformation can increase structural stiffness and raise mode frequencies, as well as affect the mode shapes. The mode shapes cannot be approximated by harmonic functions. These changes in modal characteristics can cause the peak frequency of the structural random vibration response to shift. Furthermore, when deflection is large, the root-mean-square value of acceleration response on both sides of the beam significantly increases.  
The analysis takes into account the influence of thermal effects on the static deformation of the structure, and the combined loads have a more significant effect on the acceleration response than the superposition of individual load effects.  
Further research could investigate the impact of thermal stresses on the clamped beam and temperature-dependent properties on vibration frequencies. The inclusion of these factors would provide a more comprehensive understanding of the dynamic behavior. Moreover, experimental verification of the obtained results is necessary to validate and confirm the accuracy of the proposed method in future research.  
Conceptualization, X.Y., Y.L. and Q.F.; methodology, X.Y.; software, X.Y. and Q.C.; validation, X.Y., Y.L. and Q.C.; writing--original draft preparation, X.Y. and Q.C.; writing--review and editing, X.Y., Y.L. and Q.C.; supervision, Y.L. and Q.F.; funding acquisition, Y.L. and Q.F. All authors have read and agreed to the published version of the manuscript.  
The research work described in this paper was supported by the National Natural Science Foundation of China (52175220, 52125209), the Natural Science Foundation of Jiangsu Province (BK20211558), the Southeast University \"Zhongying Young Scholars\" Project (202201), Postdoctoral Science Foundation of Jiangsu Province (2021K230B), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX18_0070).  
Not applicable.  
Figure 15: RMS of acceleration in the z-direction under different combined loads.
## 1 Introduction
Currently, the maximum correlation entropy criterion (MCC) is gradually applied to KF with non-Gaussian noise [24; 25; 26; 27]. For the problem of sharp degradation of KF performance with heavy-tailed impulse noise interference, we propose the maximum correntropy Kalman filter (MCKF), but the method still has limitations only applicable to linear systems [28; 29]. The introduction of MCC in the EKF prevents the estimation results of nonlinear systems from being affected by non-Gaussian noise but still generates errors by linearization, which also complicates the computation [30; 31]. The unscented Kalman filtering based on the MCC converges faster [32], but is not suitable for nonlinear systems of high dimensionality [33]. The CKF based on the maximum correlation entropy criterion has better filtering efficiency in nonlinear environments but requires more computational resources [34; 35]. Currently, MCSRCKF has applications in navigation and guidance, but few applications have been made to state estimation in distributed drive vehicles. This paper makes the following contributions. First, it develops a nonlinear 7-DOF distributed vehicle dynamics model and proposes a distributed drive vehicle state estimation algorithm based on MCSRCKF. In addition, simulation experiments were carried out in universal and extreme conditions, which verified that the proposed method can more accurately and stably estimate the vehicle speed and yaw rate in non-Gaussian noise environments using only low-cost sensor signals. The rest of the paper is organized as follows. In Section 2, it presents a 7-DOF vehicle model, and in Section 3, it proposes a nonlinear 7-DOF distributed vehicle dynamics model and a distributed drive vehicle state estimation algorithm based on MCSRCKF. Section 4 verifies the superior performance of the MCSRCKF through two typical working conditions. The conclusion of this paper is given in the final Section 5.
###### Abstract  
One of the best ways to monitor the health of the heart is to regularly record its electrical activity by using an electrocardiogram (ECG). Abnormal ECG signals may indicate conditions such as heart attack, arrhythmia, or heart defects. There are many ECG devices available which can detect and amplify this differential biological signal from the heart, allowing a lot of information to be collected quickly. The ECG is often small and easy to use, but its power is supplied from regular batteries, which need to be replaced after a certain period of use. This causes discomfort for elderly users. To overcome this limitation, in this paper, we aim to develop a solar-powered, portable Bluetooth device for ECG measurements. The device can be interfaced with smartphones or other wireless devices via Bluetooth by a distance up to 100 m. The ECG device was designed to use solar energy, which is also the main power source. Following the solar energy harvesting circuit is a solar panel with an output voltage of 2.4 V and a power out of 0.25 W. We optimized the design to have a very low power consumption and in sleep mode the current consumption is only around 40 \(\upmu\)A. The device was designed with 24-bit resolution and a sampling frequency of up to 2133 Hz, which can allow high accuracy ECG measurements. The device is not only used for heart rate monitoring, but it can also assist doctors in analyzing ECG signals with a high accuracy via embedded operating software.  
Bluetooth low energy; ECG monitoring; solar energy harvesting; low power consumption +
Footnote †: journal: Applied Soft. 2019, 9, 2129; doi:10.3390/app9102129
## 3 Results and Discussion  
### K-Nearest Neighbors Regression  
The KNN algorithm is a simple model to implement for regression and classification problems. To evaluate the effectiveness and adequacy of any algorithm, we need to account for (a) output interpretation ease, (b) calculation time and (c) power of prediction. The main advantages of KNN algorithm are the easiness of its output interpretation and the significantly low time for calculation. KNN is a simple algorithm that stores all available cases and predicts the numerical target based on a similarity measure (e.g., distance functions). A simple implementation of KNN regression is to calculate the average of the numerical target of the K nearest neighbors. Another approach uses an inverse distance weighted average of the K nearest neighbors. KNN regression uses the same distance functions as KNN classification. Choosing the optimal value for K is best done by first inspecting the data. In general, a large K value is more precise, as it reduces the overall noise; however, the compromise is that the distinct boundaries within the feature space are blurred. Cross-validation is another way to retrospectively determine a good K value by  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Min Value** & **Mean Value** & **Max Value** \\ \hline RSSI & 187 & 420.4 & 517 \\ \(p\) (hPa) & 987.7 & 1040.6 & 1015 \\ \(T\) (\({}^{\circ}\)C) & 273.8 & 306 & 288.7 \\ \(RH\) (\%) & 22 & 63.5 & 94 \\ \(DP\) (\({}^{\circ}\)C) & \(-5.5\) & 11.3 & 24.6 \\ \(WS\) (m/s) & 0 & 2.95 & 25.8 \\ \(SF\) (W/m\({}^{2}\)) & 0 & 140.1 & 1149.5 \\ \(ASTD\) (\({}^{\circ}\)C) & \(-11.1\) & \(-1.39\) & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The value range of the meteorological parameters for the period 30 November 2019 to 27 October 2020.  
Figure 3: Matrix of linear correlation coefficients for measured meteorological parameters and RSSI.  
using an independent data set to validate the K value. Mathematically, the KNN regression algorithm can be expressed as the average of all training points in \(N_{0}\), [29]:  
\[f(x_{0})=\frac{1}{\text{K}}\sum_{x_{i}\in N_{0}}y_{i} \tag{5}\]  
where \(x_{i}\) is the input and \(y_{i}\) is the output of the model, \(x_{0}\) a prediction point and \(N_{0}\) the K points that are closer to \(x_{0}\). The coefficient of determination, R2 and RMSE for our experimental data set are plotted against various numbers of neighbors (K) in Figure 4, starting from 2 up to 50. As shown in Figure 4, the coefficient of determination for the training subset initiates from a value of 0.95 and then gradually decreases to 0.75 when K = 50. The equivalent values for the testing subset had a peak value of 0.85 for K = 3, meaning that this is the optimal value for the number of neighbors that best fits KNN model to the data set; therefore, is the value we selected for our model A structured process of scanning the training subset (i.e., grid search) to determine the optimal value for a parameter is employed to determine the optimal K value (K = 3).